From d0404cdeec460919fd6b4641dacaa601a992c1bf Mon Sep 17 00:00:00 2001
From: Kyo Lee <kyol@google.com>
Date: Fri, 15 Mar 2024 21:02:21 +0000
Subject: [PATCH 01/12] [docs-agent] Clean up Docs Agent's README.md files.

---
 examples/gemini/python/docs-agent/README.md   | 85 +++----------------
 .../docs_agent/interfaces/README.md           | 12 ++-
 2 files changed, 18 insertions(+), 79 deletions(-)

diff --git a/examples/gemini/python/docs-agent/README.md b/examples/gemini/python/docs-agent/README.md
index 4b30e9644..8b2feee0f 100644
--- a/examples/gemini/python/docs-agent/README.md
+++ b/examples/gemini/python/docs-agent/README.md
@@ -67,8 +67,8 @@ The following list summarizes the tasks and features supported by Docs Agent:
   to create, populate, update and delete online corpora using the Semantic Retrieval AI.
   For the list of all available Docs Agent command lines, see the
   [Docs Agent CLI reference][cli-reference] page.
-- **Run the Docs Agent CLI from anywhere on a terminal**: You can set up the
-  Docs Agent CLI to ask questions to the Gemini model from anywhere on a terminal.
+- **Run the Docs Agent CLI from anywhere in a terminal**: You can set up the
+  Docs Agent CLI to ask questions to the Gemini model from anywhere in a terminal.
   For more information, see the [Set up Docs Agent CLI][cli-readme] page.
 
 For more information on Docs Agent's architecture and features,
@@ -174,41 +174,32 @@ Authorize Google Cloud credentials on your host machine:
    (`application_default_credentials.json`) in the `$HOME/.config/gcloud/`
    directory of your host machine.
 
-### 4. Clone the Docs Agent project repository
+### 4. Clone the Docs Agent project
 
 **Note**: This guide assumes that you're creating a new project directory
 from your `$HOME` directory.
 
-Clone the Docs Agent repository and install dependencies:
+Clone the Docs Agent project and install dependencies:
 
-1. Clone the following internal repo:
+1. Clone the following repo:
 
    ```posix-terminal
-   git clone sso://doc-llm-internal/docs-agent
+   git clone https://github.com/google/generative-ai-docs.git
    ```
 
-2. Go to the project directory:
+2. Go to the Docs Agent project directory:
 
    ```posix-terminal
-   cd docs-agent
+   cd generative-ai-docs/examples/gemini/python/docs-agent
    ```
 
-3. (**Optional**) If you plan on contributing to the `docs-agent` repo,
-   run the following command to set up your commit hook:
-
-   ```
-   curl -Lo `git rev-parse --git-dir`/hooks/commit-msg https://gerrit-review.googlesource.com/tools/hooks/commit-msg ; chmod +x `git rev-parse --git-dir`/hooks/commit-msg
-   ```
-
-4. Install dependencies using `poetry`:
+3. Install dependencies using `poetry`:
 
    ```posix-terminal
    poetry install
    ```
 
-   This may take some time to complete.
-
-5. Enter the `poetry` shell environment:
+4. Enter the `poetry` shell environment:
 
    ```posix-terminal
    poetry shell
@@ -234,7 +225,7 @@ Update settings in the Docs Agent project to use your custom dataset:
 1. Go to the Docs Agent project home directory, for example:
 
    ```
-   cd $HOME/docs-agent
+   cd $HOME/generative-ai-docs/examples/gemini/python/docs-agent
    ```
 
 2. Open the [`config.yaml`][config-yaml] file using a text editor, for example:
@@ -315,7 +306,7 @@ To populate a new vector database:
 1. Go to the Docs Agent project home directory, for example:
 
    ```
-   cd $HOME/docs-agent
+   cd $HOME/generative-ai-docs/examples/gemini/python/docs-agent
    ```
 
 2. Process Markdown files into small text chunks:
@@ -349,7 +340,7 @@ To start the Docs Agent chat app:
 1. Go to the Docs Agent project home directory, for example:
 
    ```
-   cd $HOME/docs-agent
+   cd $HOME/generative-ai-docs/examples/gemini/python/docs-agent
    ```
 
 2. Launch the Docs Agent chat app:
@@ -391,56 +382,6 @@ To start the Docs Agent chat app:
 
 **The Docs Agent chat app is all set!**
 
-## Contribute to Docs Agent
-
-The section provides instructions on how to set up your account with the Docs Agent
-repository so that you can start contributing to the Docs Agent project.
-
-To set up your account for the Docs Agent repository, do the following:
-
-1. To create an account on Gerrit Code Review, open the following page
-   on a browser:
-
-   ```
-   https://doc-llm-internal-review.git.corp.google.com/
-   ```
-
-1. Click **Create account**.
-
-1. Clone the `docs-agent` repository on your host machine:
-
-   ```
-   git clone sso://doc-llm-internal/docs-agent
-   ```
-
-1. Go to the project directory:
-
-   ```
-   cd docs-agent
-   ```
-
-1. To set up your commit hook, run the following command:
-
-   ```
-   curl -Lo `git rev-parse --git-dir`/hooks/commit-msg https://gerrit-review.googlesource.com/tools/hooks/commit-msg ; chmod +x `git rev-parse --git-dir`/hooks/commit-msg
-   ```
-
-1. Create a new Gerrit change, for example:
-
-   ```
-   git add <files>
-   ```
-
-   ```
-   git commit [--amend]
-   ```
-
-1. Upload the change for review:
-
-   ```
-   git push origin HEAD:refs/for/main
-   ```
-
 ## Contributors
 
 Nick Van der Auwermeulen (`@nickvander`), Rundong Du (`@rundong08`),
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/README.md b/examples/gemini/python/docs-agent/docs_agent/interfaces/README.md
index bd383e5ae..27e2ae9ae 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/README.md
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/README.md
@@ -116,21 +116,21 @@ credentials (via `gcloud`) stored on your host machine.
    (`application_default_credentials.json`) in the `$HOME/.config/gcloud/`
    directory of your host machine.
 
-## 4. Clone the Docs Agent project repository
+## 4. Clone the Docs Agent project
 
 **Note**: This guide assumes that you're creating a new project directory
 from your `$HOME` directory.
 
-1. Clone the following internal repo:
+1. Clone the following repo:
 
    ```posix-terminal
-   git clone sso://doc-llm-internal/docs-agent
+   git clone https://github.com/google/generative-ai-docs.git
    ```
 
-2. Go to the project directory:
+2. Go to the Docs Agent project directory:
 
    ```posix-terminal
-   cd docs-agent
+   cd generative-ai-docs/examples/gemini/python/docs-agent
    ```
 
 3. Install dependencies using `poetry`:
@@ -139,8 +139,6 @@ from your `$HOME` directory.
    poetry install
    ```
 
-   This may take some time to complete.
-
 ## 5. Set up an alias to the gemini command
 
 **Note**: If your Docs Agent project is not cloned in the `$HOME` directory,

From 61758684020f0d2cae9f2959d062d7fa70f3dcff Mon Sep 17 00:00:00 2001
From: Kyo Lee <kyol@google.com>
Date: Fri, 29 Mar 2024 20:00:49 +0000
Subject: [PATCH 02/12] [Release] Docs Agent version 0.3.1

What's changed:

- Bug fixes in the Docs Agent web app UI.
- Added more templates for the Docs Agent web app: widget and experimental
- A new custom splitter added: FIDL (.fidl) file specific splitter.
---
 .../python/docs-agent/apps_script/exportmd.gs | 1309 ++++++++++++++++-
 .../docs_agent/interfaces/README.md           |   12 +-
 .../docs_agent/interfaces/chatbot/__init__.py |    4 +-
 .../docs_agent/interfaces/chatbot/chatui.py   |   56 +-
 .../chatbot/static/css/style-chatui.css       |   30 +-
 .../css/{style-old.css => style-widget.css}   |  198 ++-
 .../chatbot/static/javascript/app.js          |   41 +
 .../chatbot/templates/chat-widget/base.html   |   25 +
 .../chatbot/templates/chat-widget/index.html  |   16 +
 .../chatbot/templates/chat-widget/result.html |  168 +++
 .../templates/chatui-experimental/base.html   |   27 +
 .../index.html}                               |    2 +-
 .../result.html}                              |    0
 .../chatbot/templates/chatui/index.html       |    6 +-
 .../chatbot/templates/chatui/result.html      |   16 +-
 .../docs-agent/docs_agent/interfaces/cli.py   |   14 +-
 .../preprocess/files_to_plain_text.py         |   58 +-
 .../preprocess/splitters/fidl_splitter.py     |   64 +
 .../preprocess/splitters/markdown_splitter.py |    4 +-
 .../docs-agent/docs_agent/utilities/config.py |   15 +-
 .../docs_agent/utilities/helpers.py           |    6 +-
 .../gemini/python/docs-agent/pyproject.toml   |    2 +-
 22 files changed, 1983 insertions(+), 90 deletions(-)
 mode change 120000 => 100644 examples/gemini/python/docs-agent/apps_script/exportmd.gs
 rename examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/{style-old.css => style-widget.css} (68%)
 create mode 100644 examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chat-widget/base.html
 create mode 100644 examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chat-widget/index.html
 create mode 100644 examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chat-widget/result.html
 create mode 100644 examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-experimental/base.html
 rename examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/{chatui/index_experimental.html => chatui-experimental/index.html} (90%)
 rename examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/{chatui/result_experimental.html => chatui-experimental/result.html} (100%)
 create mode 100644 examples/gemini/python/docs-agent/docs_agent/preprocess/splitters/fidl_splitter.py

diff --git a/examples/gemini/python/docs-agent/apps_script/exportmd.gs b/examples/gemini/python/docs-agent/apps_script/exportmd.gs
deleted file mode 120000
index 203251435..000000000
--- a/examples/gemini/python/docs-agent/apps_script/exportmd.gs
+++ /dev/null
@@ -1 +0,0 @@
-../third_party/g2docsmd-html/exportmd.gs
\ No newline at end of file
diff --git a/examples/gemini/python/docs-agent/apps_script/exportmd.gs b/examples/gemini/python/docs-agent/apps_script/exportmd.gs
new file mode 100644
index 000000000..283872c79
--- /dev/null
+++ b/examples/gemini/python/docs-agent/apps_script/exportmd.gs
@@ -0,0 +1,1308 @@
+/**
+ * Copyright 2023 Google LLC
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* Original script is from:
+https://github.com/lmmx/gdocs2md-html/blob/master/exportmd.gs
+and commit: 0d86cfa
+Parsing from mangini/gdocs2md.
+Modified by clearf to add files to the google directory structure.
+Modified by lmmx to write Markdown, going back to HTML-incorporation.
+
+Usage:
+  NB: don't use on top-level doc (in root Drive folder) See comment in setupScript function.
+  Adding this script to your doc:
+    - Tools > Script Manager > New
+    - Select "Blank Project", then paste this code in and save.
+  Running the script:
+    - Tools > Script Manager
+    - Select "convertDocumentToMarkdown" function.
+    - Click Run button.
+    - Converted doc will be added to a "Markdown" folder in the source document's directories.
+    - Images will be added to a subfolder of the "Markdown" folder.
+*/
+
+function onInstall(e) {
+  onOpen(e);
+}
+
+function onOpen() {
+  // Add a menu with some items, some separators, and a sub-menu.
+  setupScript();
+//  In future:
+//  DocumentApp.getUi().createAddonMenu();
+  DocumentApp.getUi().createMenu('Markdown')
+      .addItem('View as markdown', 'markdownPopup')
+      .addSubMenu(DocumentApp.getUi().createMenu('Export \u2192 markdown')
+                  .addItem('Export to local file', 'convertSingleDoc')
+                  .addItem('Export entire folder to local file', 'convertFolder')
+                  .addItem('Customise markdown conversion', 'changeDefaults'))
+      .addSeparator()
+      .addSubMenu(DocumentApp.getUi().createMenu('Toggle comment visibility')
+                 .addItem('Image source URLs', 'toggleImageSourceStatus')
+                 .addItem('All comments', 'toggleCommentStatus'))
+      .addItem("Add comment", 'addCommentDummy')
+      .addToUi();
+}
+
+function changeDefaults() {
+  var ui = DocumentApp.getUi();
+  var default_settings = '{ use your imagination... }';
+  var greeting = ui.alert('This should be set up to display defaults from variables passed to getDocComments etc., e.g. something like:\n\nDefault settings are:'
+                    + '\ncomments - not checking deleted comments.\nDocument - this document (alternatively specify a document ID).'
+                    + '\n\nClick OK to edit these, or cancel.',
+                    ui.ButtonSet.OK_CANCEL);
+  ui.alert("There's not really need for this yet, so this won't proceed, regardless of what you just pressed.");
+  return;
+
+  // Future:
+  if (greeting == ui.Button.CANCEL) {
+    ui.alert("Alright, never mind!");
+    return;
+  }
+  // otherwise user clicked OK
+  // user clicked OK, to proceed with editing these defaults. Ask case by case whether to edit
+
+  var response = ui.prompt('What is x (default y)?', ui.ButtonSet.YES_NO_CANCEL);
+
+  // Example code from docs at https://developers.google.com/apps-script/reference/base/button-set
+  // Process the user's response.
+  if (response.getSelectedButton() == ui.Button.YES) {
+    Logger.log('The user\'s name is %s.', response.getResponseText());
+  } else if (response.getSelectedButton() == ui.Button.NO) {
+    Logger.log('The user didn\'t want to provide a name.');
+  } else {
+    Logger.log('The user clicked the close button in the dialog\'s title bar.');
+  }
+}
+
+function setupScript() {
+  var script_properties = PropertiesService.getScriptProperties();
+  script_properties.setProperty("user_email", Drive.About.get().user.emailAddress);
+
+  // manual way to do the following:
+  // script_properties.setProperty("folder_id", "INSERT_FOLDER_ID_HERE");
+  // script_properties.setProperty("document_id", "INSERT_FILE_ID_HERE");
+
+  var doc_id = DocumentApp.getActiveDocument().getId();
+  script_properties.setProperty("document_id", doc_id);
+  var doc_parents = DriveApp.getFileById(doc_id).getParents();
+  var folders = doc_parents;
+  while (folders.hasNext()) {
+    var folder = folders.next();
+    var folder_id = folder.getId();
+  }
+  script_properties.setProperty("folder_id", folder_id);
+  script_properties.setProperty("image_folder_prefix", ""); // add if modifying image location
+}
+
+function addCommentDummy() {
+  // Dummy function to be switched during development for addComment
+  DocumentApp.getUi()
+    .alert('Cancelling comment entry',
+           "There's not currently a readable anchor for Google Docs - you need to write your own!"
+
+           + "\n\nThe infrastructure for using such an anchoring schema is sketched out in"
+           + " the exportmd.gs script's addComment function, for an anchor defined in anchor_props"
+
+           + "\n\nSee github.com/lmmx/devnotes/wiki/Custom-Google-Docs-comment-anchoring-schema",
+           DocumentApp.getUi().ButtonSet.OK
+          );
+  return;
+}
+
+function addComment() {
+
+  var doc_id = PropertiesService.getScriptProperties().getProperty('document_id');
+  var user_email = PropertiesService.getScriptProperties().getProperty('email');
+/*  Drive.Comments.insert({content: "hello world",
+                         context: {
+                           type: 'text/html',
+                           value: 'hinges'
+                         }
+                        }, document_id); */
+  var revision_list = Drive.Revisions.list(doc_id).items;
+  var recent_revision_id = revision_list[revision_list.length - 1].id;
+  var anchor_props = {
+    revision_id: recent_revision_id,
+    starting_offset: '',
+    offset_length: '',
+    total_chars: ''
+  }
+  insertComment(doc_id, 'hinges', 'Hello world!', my_email, anchor_props);
+}
+
+function insertComment(fileId, selected_text, content, user_email, anchor_props) {
+
+  // NB Deal with handling missing args
+
+  /*
+  anchor_props is an object with 4 properties:
+    - revision_id,
+    - starting_offset,
+    - offset_length,
+    - total_chars
+  */
+
+  var context = Drive.newCommentContext();
+    context.value = selected_text;
+    context.type = 'text/html';
+  var comment = Drive.newComment();
+    comment.kind = 'drive#comment';
+    var author = Drive.newUser();
+      author.kind = 'drive#user';
+      author.displayName = user_email;
+      author.isAuthenticatedUser = true;
+    comment.author = author;
+    comment.content = type;
+    comment.context = context;
+    comment.status = 'open';
+    comment.anchor = "{'r':"
+                     + anchor_props.revision_id
+                     + ",'a':[{'txt':{'o':"
+                     + anchor_props.starting_offset
+                     + ",'l':"
+                     + anchor_props.offset_length
+                     + ",'ml':"
+                     + anchor_props.total_chars
+                     + "}}]}";
+    comment.fileId = fileId;
+  Drive.Comments.insert(comment, fileId);
+}
+
+function decodeScriptSwitches(optional_storage_name) {
+  var property_name = (typeof(optional_storage_name) == 'string') ? optional_storage_name : 'switch_settings';
+  var script_properties = PropertiesService.getScriptProperties();
+  return script_properties
+              .getProperty(property_name)
+              .replace(/{|}/g,'')          // Get the statements out of brackets...
+              .replace(',', ';');          // ...swap the separator for a semi-colon...
+  // ...evaluate the stored object string as statements upon string return and voila, switches interpreted
+}
+
+
+function getDocComments(comment_list_settings) {
+  var possible_settings = ['images', 'include_deleted'];
+
+  // switches are processed and set on a script-wide property called "comment_switches"
+  var property_name = 'comment_switches';
+  switchHandler(comment_list_settings, possible_settings, property_name);
+
+  var script_properties = PropertiesService.getScriptProperties();
+  var comment_switches = decodeScriptSwitches(property_name);
+  eval(comment_switches);
+
+  var document_id = script_properties.getProperty("document_id");
+  var comments_list = Drive.Comments.list(document_id,
+                                          {includeDeleted: include_deleted,
+                                           maxResults: 100 }); // 0 to 100, default 20
+  // See https://developers.google.com/drive/v2/reference/comments/list for all options
+  var comment_array = [];
+  var image_sources = [];
+  // To collect all comments' image URLs to match against inlineImage class elements LINK_URL attribute
+
+  for (var i = 0; i < comments_list.items.length; i++) {
+    var comment = comments_list.items[i];
+    var comment_text = comment.content;
+    var comment_status = comment.status;
+ /*
+    images is a generic parameter passed in as a switch to
+    return image URL-containing comments only.
+
+    If the parameter is provided, it's no longer undefined.
+ */
+    var img_url_regex = /(https?:\/\/.+?\.(png|gif|jpe?g))/;
+    var has_img_url = img_url_regex.test(comment_text);
+
+    if (images && !has_img_url) continue; // no image URL, don't store comment
+    if (has_img_url) image_sources.push(RegExp.$1);
+    comment_array.push(comment);
+  }
+  script_properties.setProperty('image_source_URLs', image_sources)
+  return comment_array;
+}
+
+function isValidAttrib(attribute) { // Sanity check function, called per element in array
+
+  // Possible list of attributes to check against (leaving out unchanging ones like kind)
+  possible_attrs = [
+    'selfLink',
+    'commentId',
+    'createdDate',
+    'modifiedDate',
+    'author',
+    'htmlContent',
+    'content',
+    'deleted',
+    'status',
+    'context',
+    'anchor',
+    'fileId',
+    'fileTitle',
+    'replies',
+    'author'
+  ];
+
+  // Check if attribute(s) provided can be used to match/filter comments:
+
+  if (typeof(attribute) == 'string' || typeof(attribute) == 'object') {
+    // Either a string/object (1-tuple)
+
+    // Generated with Javascript, gist: https://gist.github.com/lmmx/451b301e1d78ed2c10b4
+
+    // Return false from the function if any of the attributes specified are not in the above list
+
+    // If an object, the name is the key, otherwise it's just the string
+    if (attribute.constructor === Object) {
+      var att_keys = [];
+      for (var att_key in attribute) {
+        if (attribute.hasOwnProperty(att_key)) {
+          att_keys.push(att_key);
+        }
+      }
+      for (var n=0; n < att_keys.length; n++) {
+        var attribute_name = att_keys[n];
+        var is_valid_attrib = (possible_attrs.indexOf(attribute_name) > -1);
+
+        // The attribute needs to be one of the possible attributes listed above, match its given value(s),
+        // else returning false will throw an error from onAttribError when within getCommentAttributes
+        return is_valid_attrib;
+      }
+    } else if (typeof(attribute) == 'string') {
+      var attribute_name = attribute;
+      var is_valid_attrib = (possible_attrs.indexOf(attribute_name) > -1);
+      return is_valid_attrib;
+      // Otherwise is a valid (string) attribute
+    } else if (attribute.constructor === Array) {
+      return false; // Again, if within getCommentAttributes this will cause an error - shouldn't pass an array
+    } else {
+      // Wouldn't expect this to happen, so give a custom error message
+      Logger.log('Unknown type (assumed impossible) passed to isValidAttrib: ', attribute, attribute.constructor);
+      throw new TypeError('Unknown passed to isValidAttrib - this should be receiving 1-tuples only, see logs for details.');
+    }
+  } else return false; // Neither string/object / array of strings &/or objects - not a valid attribute
+}
+
+function getCommentAttributes(attributes, comment_list_settings) {
+
+  // A filter function built on Comments.list, for a given list of attributes
+  // Objects' values are ignored here, only their property titles are used to filter comments.
+
+
+  /*
+    - attributes: array of attributes to filter/match on
+    - comment_list_settings: (optional) object with properties corresponding to switches in getDocComments
+
+    This function outputs an array of the same length as the comment list, containing
+    values for all fields matched/filtered on.
+  */
+
+
+  /*
+   *         All possible comment attributes are listed at:
+   *   https://developers.google.com/drive/v2/reference/comments#properties
+   */
+
+  //  Firstly, describe the type in a message to be thrown in case of TypeError:
+
+  var attrib_def_message = "'attributes' should be a string (the attribute to get for each comment), "
+                           + "an object (a key-value pair for attribute and desired value), "
+                           + "or an array of objects (each with key-value pairs)";
+
+  function onAttribError(message) {
+    Logger.log(message);
+    throw new TypeError(message);
+  }
+
+  // If (optional) comment_list_settings isn't set, make a getDocComments call with switches left blank.
+  if (typeof(comment_list_settings) == 'undefined') var comment_list_settings = {};
+  if (typeof(attributes) == 'undefined') onAttribError(attrib_def_message); // no variables specified
+
+  if (isValidAttrib(attributes)) { // This will be true if there's only one attribute, not provided in an array
+
+    /*
+       Make a 1-tuple (array of 1) from either an object or a string,
+       i.e. a single attribute, with or without a defined value respectively.
+    */
+
+    var attributes = Array(attributes);
+
+  } else if (attributes.constructor === Array) {
+
+    // Check each item in the array is a valid attribute specification
+    for (var l = 0; l < attributes.length; l++) {
+      if (! isValidAttrib(attributes[l]) ) {
+        onAttribError('Error in attribute '
+                      + (l+1) + ' of ' + attributes.length
+                      + '\n\n' + + attrib_def_message);
+      }
+    }
+
+  } else { // Neither attribute nor array of attributes
+    throw new TypeError(attrib_def_message);
+  }
+
+  // Attributes now holds an array of string and/or objects specifying a comment match and/or filter query
+
+  var comment_list = getDocComments(comment_list_settings);
+  var comment_attrib_lists = [];
+  for (var i in comment_list) {
+    var comment = comment_list[i];
+    var comment_attrib_list = [];
+    for (var j in attributes) {
+      var comment_attribute = comment_list[i][attributes[j]];
+      comment_attrib_list.push(comment_attribute);
+    }
+    comment_attrib_lists.push(comment_attrib_list);
+  }
+  // The array comment_attrib_lists is now full of the requested attributes,
+  // of length equal to that of attributes
+  return comment_attrib_lists;
+}
+
+// Example function to use getCommentAttributes:
+
+function filterComments(attributes, comment_list_settings) {
+  var comment_attributes = getCommentAttributes(attributes, comment_list_settings);
+  var m = attribs.indexOf('commentId') // no need to keep track of commentID array position
+  comm_attribs.map(function(attrib_pair) {
+     if (attrib_pair[1]);
+  })
+}
+
+function toggleCommentStatus(comment_switches){
+  // Technically just image URL-containing comments, not sources just yet
+  var attribs = ['commentId', 'status'];
+  var comm_attribs = getCommentAttributes(attribs, comment_switches);
+  var rearrangement = [];
+  comm_attribs.map(
+    function(attrib_pair) { // for every comment return with the images_only / images: true comments.list setting,
+      switch (attrib_pair[1]){ // check the status of each
+        case 'open':
+          rearrangement.push([attrib_pair[0],'resolved']);
+          break;
+        case 'resolved':
+          rearrangement.push([attrib_pair[0],'open']);
+          break;
+      }
+    }
+  );
+  var script_properties = PropertiesService.getScriptProperties();
+  var doc_id = script_properties.getProperty("document_id");
+  rearrangement.map(
+    function(new_attrib_pair) { // for every comment ID with flipped status
+      Drive.Comments.patch('{"status": "'
+                           + new_attrib_pair[1]
+                           + '"}', doc_id, new_attrib_pair[0])
+    }
+  );
+  return;
+}
+
+function toggleImageSourceStatus(){
+  toggleCommentStatus({images: true});
+}
+
+function flipResolved() {
+  // Flip the status of resolved comments to open, and open comments to resolved (respectful = true)
+  // I.e. make resolved URL-containing comments visible, without losing track of normal comments' status
+
+  // To force all comments' statuses to switch between resolved and open en masse set respectful to false
+
+  var switch_settings = {};
+    switch_settings.respectful = true;
+    switch_settings.images_only = false; // If true, only switch status of comments with an image URL
+    switch_settings.switch_deleted_comments = false; // If true, also switch status of deleted comments
+
+  var comments_list = getDocComments(
+    { images: switch_settings.images_only,
+      include_deleted: switch_settings.switch_deleted_comments });
+
+  // Note: these parameters are unnecessary if both false (in their absence assumed false)
+  //       but included for ease of later reuse
+
+  if (switch_settings.respectful) {
+    // flip between
+  } else {
+    // flip all based on status of first in list
+  }
+}
+
+function markdownPopup() {
+  var css_style = '<style type="text/css" data-github="https://github.com/lmmx/gdocs2md-html/blob/master/md-preview.css">'
+    + 'h1, h2, h3, h4, h5, h6 {'
+    + '  font-family: "Gibson", sans-serif, "Helvetica Neue", HelveticaNeue, Helvetica, Arial, sans-serif;'
+    + '  font-size: 1.3em;'
+    + '  line-height: 1.4;'
+    + '  margin-top: 9px;'
+    + '}'
+    + 'code,pre,pre *{'
+    + '  font-size: 12px;'
+    + '  line-height: 1.4;'
+    + '  white-space: pre-wrap;'
+    + '  padding-top: 1em;'
+    + '  padding-bottom: 1em;'
+    + '  clear:both;'
+    + '  padding: 0.2em;'
+    + '  margin: 0;'
+    + '  background-color: rgba(0,0,0,0.04);'
+    + '  border-radius: 3px;'
+    + '  font: 11px Consolas, "Liberation Mono", Menlo, Courier, monospace;'
+    + '}'
+    + 'pre textarea {'
+    + '    background: transparent;'
+    + '    border: none;'
+    + '    height: inherit !important;'
+    + '    width: 775px;'
+    + '}'
+    + 'pre {'
+    + '    height: 430px;'
+    + '}'
+    + '</style>';
+
+  // The above was written with js since <link rel="stylesheet" href=[GitHub raw URL]> doesn't work:
+  // https://gist.github.com/lmmx/ec084fc351528395f2bb
+
+  var mdstring = stringMiddleMan();
+
+  var htmlstring =
+      '<!doctype html><html lang="en"><head><meta charset="utf-8">'
+    + css_style
+    + '</head><body><pre><textarea id="md-output" readonly="readonly">'
+    + mdstring
+    + '</textarea></pre><script type="text/javascript">'
+    + 'function mdClick() {'
+    + 'var textbox = document.getElementById("md-output");'
+    + 'textbox.setAttribute("onclick", "this.focus(); this.select();") }'
+    + 'window.onload = mdClick();'
+    + '</script></body></html>';
+
+  var html5 = HtmlService.createHtmlOutput(htmlstring)
+      .setSandboxMode(HtmlService.SandboxMode.IFRAME)
+      .setWidth(800)
+      .setHeight(500);
+
+  DocumentApp.getUi()
+      .showModalDialog(html5, 'Markdown output');
+}
+
+function stringMiddleMan() {
+  var returned_string;
+  convertSingleDoc({"return_string": true}); // for some reason needs the scope to be already set...
+  // could probably rework to use mdstring rather than returned_string, cut out middle man function
+  return this.returned_string;
+}
+
+function convertSingleDoc(optional_switches) {
+  var script_properties = PropertiesService.getScriptProperties();
+  // renew comments list on every export
+  var doc_comments = getDocComments();
+  var image_urls = getDocComments({images: true}); // NB assumed false - any value will do
+  script_properties.setProperty("comments", doc_comments);
+  script_properties.setProperty("image_srcs", image_urls);
+  var folder_id = script_properties.getProperty("folder_id");
+  var document_id = script_properties.getProperty("document_id");
+  var source_folder = DriveApp.getFolderById(folder_id);
+  var markdown_folders = source_folder.getFoldersByName("Markdown");
+
+  var markdown_folder;
+  if (markdown_folders.hasNext()) {
+    markdown_folder = markdown_folders.next();
+  } else {
+    // Create a Markdown folder if it doesn't exist.
+    markdown_folder = source_folder.createFolder("Markdown")
+  }
+
+  convertDocumentToMarkdown(DocumentApp.openById(document_id), markdown_folder, optional_switches);
+}
+
+function convertFolder() {
+  var script_properties = PropertiesService.getScriptProperties();
+  var folder_id = script_properties.getProperty("folder_id");
+  var source_folder = DriveApp.getFolderById(folder_id);
+  var markdown_folders = source_folder.getFoldersByName("Markdown");
+
+
+  var markdown_folder;
+  if (markdown_folders.hasNext()) {
+    markdown_folder = markdown_folders.next();
+  } else {
+    // Create a Markdown folder if it doesn't exist.
+    markdown_folder = source_folder.createFolder("Markdown");
+  }
+
+  // Only try to convert google docs files.
+  var gdoc_files = source_folder.getFilesByType("application/vnd.google-apps.document");
+
+  // For every file in this directory
+  while(gdoc_files.hasNext()) {
+    var gdoc_file = gdoc_files.next()
+
+    var filename = gdoc_file.getName();
+    var md_files = markdown_folder.getFilesByName(filename + ".md");
+    var update_file = false;
+
+    if (md_files.hasNext()) {
+      var md_file = md_files.next();
+
+      if (md_files.hasNext()){ // There are multiple markdown files; delete and rerun
+        update_file = true;
+      } else if (md_file.getLastUpdated() < gdoc_file.getLastUpdated()) {
+        update_file = true;
+      }
+    } else {
+      // There is no folder and the conversion needs to be rerun
+      update_file = true;
+    }
+
+    if (update_file) {
+      convertDocumentToMarkdown(DocumentApp.openById(gdoc_file.getId()), markdown_folder);
+    }
+  }
+}
+
+function switchHandler(input_switches, potential_switches, optional_storage_name) {
+
+  // Firstly, if no input switches were set, make an empty input object
+  if (typeof(input_switches) == 'undefined') input_switches = {};
+
+  // Use optional storage name if it's defined (must be a string), else use default variable name "switch_settings"
+  var property_name = (typeof(optional_storage_name) == 'string') ? optional_storage_name : 'switch_settings';
+
+  // Make a blank object to be populated and stored as the script-wide property named after property_name
+  var switch_settings = {};
+
+  for (var i in potential_switches) {
+    var potential_switch = potential_switches[i];
+
+    // If each switch has been set (in input_switches), evaluate it, else assume it's switched off (false):
+
+    if (input_switches.propertyIsEnumerable(potential_switch)) {
+
+      // Evaluates a string representing a statement which sets switch_settings properties from input_switches
+      // e.g. "switch_settings.images = true" when input_switches = {images: true}
+
+      eval('switch_settings.' + potential_switch + " = " + input_switches[potential_switch]);
+
+    } else {
+
+      // Alternatively, the evaluated statement sets anything absent from the input_switches object as false
+      // e.g. "switch_settings.images = false" when input_switches = {} and potential_switches = ['images']
+
+      eval('switch_settings.' + potential_switch + " = false");
+    }
+  }
+
+  PropertiesService.getScriptProperties().setProperty(property_name, switch_settings);
+
+  /*
+  Looks bad but more sensible than repeatedly checking if arg undefined.
+
+  Sets every variable named in the potential_switches array to false if
+  it wasn't passed into the input_switches object, otherwise evaluates.
+
+  Any arguments not passed in are false, but so are any explicitly passed in as false:
+  all parameters are therefore Boolean until otherwise specified.
+  */
+
+}
+
+function convertDocumentToMarkdown(document, destination_folder, frontmatter_input, optional_switches) {
+  // if returning a string, force_save_images will make the script continue - experimental
+  var possible_switches = ['return_string', 'force_save_images'];
+  var property_name = 'conversion_switches';
+  switchHandler(optional_switches, possible_switches, property_name);
+
+  // TODO switch off image storage if force_save_images is true - not necessary for normal behaviour
+  var script_properties = PropertiesService.getScriptProperties();
+  var comment_switches = decodeScriptSwitches(property_name);
+  eval(comment_switches);
+
+  var image_prefix = script_properties.getProperty("image_folder_prefix");
+  var numChildren = document.getActiveSection().getNumChildren();
+  if (frontmatter_input != "") {
+    var text = frontmatter_input;
+  }
+  else {
+    var text = ""
+  }
+  var md_filename = sanitizeFileName(document.getName()) + ".md";
+  var image_foldername = document.getName()+"_images";
+  var inSrc = false;
+  var inClass = false;
+  var globalImageCounter = 0;
+  var globalListCounters = {};
+  // edbacher: added a variable for indent in src <pre> block. Let style sheet do margin.
+  var srcIndent = "";
+
+  var postHasImages = false;
+
+  var files = [];
+
+  // Walk through all the child elements of the doc.
+  for (var i = 0; i < numChildren; i++) {
+    var child = document.getActiveSection().getChild(i);
+    var result = processParagraph(i, child, inSrc, globalImageCounter, globalListCounters, image_prefix + image_foldername);
+    globalImageCounter += (result && result.images) ? result.images.length : 0;
+    if (result!==null) {
+      if (result.sourceGlossary==="start" && !inSrc) {
+        inSrc=true;
+        text+="<pre class=\"glossary\">\n";
+      } else if (result.sourceGlossary==="end" && inSrc) {
+        inSrc=false;
+        text+="</pre>\n\n";
+      } else if (result.sourceFigCap==="start" && !inSrc) {
+        inSrc=true;
+        text+="<pre class=\"glossary\">\n";
+      } else if (result.sourceFigCap==="end" && inSrc) {
+        inSrc=false;
+        text+="</pre>\n\n";
+      } else if (result.source==="start" && !inSrc) {
+        inSrc=true;
+        text+="<pre>\n";
+      } else if (result.source==="end" && inSrc) {
+        inSrc=false;
+        text+="</pre>\n\n";
+      } else if (result.inClass==="start" && !inClass) {
+        inClass=true;
+        text+="<pre class=\""+result.className+"\">\n";
+      } else if (result.inClass==="end" && inClass) {
+        inClass=false;
+        text+="</pre>\n\n";
+      } else if (inClass) {
+        text+=result.text+"\n\n";
+      } else if (inSrc) {
+        text+=(srcIndent+escapeHTML(result.text)+"\n");
+      } else if (result.text && result.text.length>0) {
+        text+=result.text+"\n\n";
+      }
+
+      if (result.images && result.images.length>0) {
+        for (var j=0; j<result.images.length; j++) {
+          files.push( { "blob": result.images[j].blob } );
+          postHasImages = true;
+        }
+      }
+    } else if (inSrc) { // support empty lines inside source code
+      text+='\n';
+    }
+
+  }
+
+  if (return_string && !force_save_images) {
+    returned_string = text;
+    return returned_string;
+  }
+  // return the string rather than continuing to write files
+
+  // I don't need to handle string return *with* saved images yet so this flag doesn't do anything in absence of return_string
+
+  files.push({"fileName": md_filename, "mimeType": "text/plain", "content": text});
+
+
+  // Cleanup any old folders and files in our destination directory with an identical name
+  var old_folders = destination_folder.getFoldersByName(image_foldername)
+  while (old_folders.hasNext()) {
+    var old_folder = old_folders.next();
+    old_folder.setTrashed(true)
+  }
+
+  // Remove any previously converted markdown files.
+  var old_files = destination_folder.getFilesByName(md_filename)
+  while (old_files.hasNext()) {
+    var old_file = old_files.next();
+    old_file.setTrashed(true)
+  }
+
+  // Create a subfolder for images if they exist
+  var image_folder;
+  if (postHasImages) {
+    image_folder = DriveApp.createFolder(image_foldername);
+    DriveApp.removeFolder(image_folder); // Confusing convention; this just removes the folder from the google drive root.
+    destination_folder.addFolder(image_folder)
+  }
+
+  for (var i = 0; i < files.length; i++) {
+    var saved_file;
+    if (files[i].blob) {
+      saved_file = DriveApp.createFile(files[i].blob)
+      // The images go into a subfolder matching the post title
+      image_folder.addFile(saved_file)
+    } else {
+      // The markdown files all go in the "Markdown" directory
+      saved_file = DriveApp.createFile(files[i]["fileName"], files[i]["content"], files[i]["mimeType"])
+      destination_folder.addFile(saved_file)
+    }
+   DriveApp.removeFile(saved_file) // Removes from google drive root.
+  }
+return saved_file;
+}
+
+function escapeHTML(text) {
+  return text.replace(/</g, '&lt;').replace(/>/g, '&gt;');
+}
+
+function standardQMarks(text) {
+  return text.replace(/\u2018|\u8216|\u2019|\u8217/g,"'").replace(/\u201c|\u8220|\u201d|\u8221/g, '"')
+}
+
+// Process each child element (not just paragraphs).
+function processParagraph(index, element, inSrc, imageCounter, listCounters, image_path) {
+  // First, check for things that require no processing.
+  if (element.getType() === DocumentApp.ElementType.UNSUPPORTED) {
+    return null;
+  }
+  if (element.getNumChildren()==0) {
+    return null;
+  }
+  // Skip on TOC.
+  if (element.getType() === DocumentApp.ElementType.TABLE_OF_CONTENTS) {
+    return {"text": "[[TOC]]"};
+  }
+
+  // Set up for real results.
+  var result = {};
+  var pOut = "";
+  var textElements = [];
+  var imagePrefix = "image_";
+
+  // Handle Table elements. Pretty simple-minded now, but works for simple tables.
+  // Note that Markdown does not process within block-level HTML, so it probably
+  // doesn't make sense to add markup within tables.
+  if (element.getType() === DocumentApp.ElementType.TABLE) {
+    textElements.push("<table>\n");
+    var nCols = element.getChild(0).getNumCells();
+    for (var i = 0; i < element.getNumChildren(); i++) {
+      textElements.push("  <tr>\n");
+      // process this row
+      for (var j = 0; j < nCols; j++) {
+        textElements.push("    <td>" + element.getChild(i).getChild(j).getText() + "</td>\n");
+      }
+      textElements.push("  </tr>\n");
+    }
+    textElements.push("</table>\n");
+  }
+
+  // Need to handle this element type, return null for now
+  if (element.getType() === DocumentApp.ElementType.CODE_SNIPPET) {
+    return null
+  }
+
+  // Process various types (ElementType).
+  for (var i = 0; i < element.getNumChildren(); i++) {
+    var t = element.getChild(i).getType();
+
+    if (t === DocumentApp.ElementType.TABLE_ROW) {
+      // do nothing: already handled TABLE_ROW
+    } else if (t === DocumentApp.ElementType.TEXT) {
+      var txt  = element.getChild(i);
+      pOut += txt.getText();
+      textElements.push(txt);
+    } else if (t === DocumentApp.ElementType.INLINE_IMAGE) {
+      var imglink = element.getChild(i).getLinkUrl();
+      result.images = result.images || [];
+      var blob = element.getChild(i).getBlob()
+      var contentType = blob.getContentType();
+      var extension = "";
+      if (/\/png$/.test(contentType)) {
+        extension = ".png";
+      } else if (/\/gif$/.test(contentType)) {
+        extension = ".gif";
+      } else if (/\/jpe?g$/.test(contentType)) {
+        extension = ".jpg";
+      } else {
+        throw "Unsupported image type: "+contentType;
+      }
+
+      var name = imagePrefix + imageCounter + extension;
+      blob.setName(name);
+
+      imageCounter++;
+      if (!return_string || force_save_images) {
+        textElements.push('![](' + image_path + '/' + name + ')');
+      } else {
+        textElements.push('![](' + imglink + ')');
+      }
+      //result.images.push( {
+      //  "bytes": blob.getBytes(),
+      //  "type": contentType,
+      //  "name": name});
+
+      result.images.push({ "blob" : blob } )
+
+    // Need to fix this case TODO
+    } else if (t === DocumentApp.ElementType.INLINE_DRAWING) {
+
+      imageCounter++;
+      if (!return_string || force_save_images) {
+        textElements.push('![](' + "drawing" + '/' + " name" + ')');
+      } else {
+        textElements.push('![](' + "drawing" + ')');
+      }
+      //result.images.push( {
+      //  "bytes": blob.getBytes(),
+      //  "type": contentType,
+      //  "name": name});
+
+      // result.images.push({ "blob" : blob } )
+
+    }
+     else if (t === DocumentApp.ElementType.PAGE_BREAK) {
+      // ignore
+    } else if (t === DocumentApp.ElementType.HORIZONTAL_RULE) {
+      textElements.push('* * *\n');
+    } else if (t === DocumentApp.ElementType.FOOTNOTE) {
+      textElements.push(' ('+element.getChild(i).getFootnoteContents().getText()+')');
+    // Fixes for new elements
+    } else if (t === DocumentApp.ElementType.DATE) {
+      textElements.push(' ('+element.getChild(i)+')');
+    } else if (t === DocumentApp.ElementType.RICH_LINK) {
+      textElements.push(' ('+element.getChild(i).getUrl()+')');
+    } else if (t === DocumentApp.ElementType.PERSON) {
+      textElements.push(element.getChild(i).getName() + ', ');
+    } else if (t === DocumentApp.ElementType.UNSUPPORTED) {
+      textElements.push(' <UNSUPPORTED> ');
+    } else {
+      throw "Paragraph "+index+" of type "+element.getType()+" has an unsupported child: "
+      +t+" "+(element.getChild(i)["getText"] ? element.getChild(i).getText():'')+" index="+index;
+    }
+  }
+
+  if (textElements.length==0) {
+    // Isn't result empty now?
+    return result;
+  }
+
+// Fix for unrecognized command getIndentFirstLine
+  var ind_f = 0;
+  var ind_s = 0;
+  var ind_e = 0;
+  if (t === DocumentApp.ElementType.PARAGRAPH) {
+
+    var ind_f = element.getIndentFirstLine();
+    var ind_s = element.getIndentStart();
+    var ind_e = element.getIndentEnd();
+  }
+  var i_fse = [ind_f,ind_s,ind_e];
+  var indents = {};
+  for (indt=0;indt<i_fse.length;indt++) {
+    var indname = i_fse[indt];
+    if (eval(indname) > 0) indents[indname] = eval(indname);
+    // lazy test, null (no indent) is not greater than zero, but becomes set if indent 'undone'
+  }
+  var inIndent = (Object.keys(indents).length > 0);
+
+  // evb: Add glossary and figure caption too. (And abbreviations: gloss and fig-cap.)
+  // process source code block:
+  if (/^\s*---\s+gloss\s*$/.test(pOut) || /^\s*---\s+source glossary\s*$/.test(pOut)) {
+    result.sourceGlossary = "start";
+  } else if (/^\s*---\s+fig-cap\s*$/.test(pOut) || /^\s*---\s+source fig-cap\s*$/.test(pOut)) {
+    result.sourceFigCap = "start";
+  } else if (/^\s*---\s+src\s*$/.test(pOut) || /^\s*---\s+source code\s*$/.test(pOut)) {
+    result.source = "start";
+  } else if (/^\s*---\s+class\s+([^ ]+)\s*$/.test(pOut)) {
+    result.inClass = "start";
+    result.className = RegExp.$1.replace(/\./g,' ');
+  } else if (/^\s*---\s*$/.test(pOut)) {
+    result.source = "end";
+    result.sourceGlossary = "end";
+    result.sourceFigCap = "end";
+    result.inClass = "end";
+  } else if (/^\s*---\s+jsperf\s*([^ ]+)\s*$/.test(pOut)) {
+    result.text = '<iframe style="width: 100%; height: 240px; overflow: hidden; border: 0;" '+
+                  'src="http://www.html5rocks.com/static/jsperfview/embed.html?id='+RegExp.$1+
+                  '"></iframe>';
+  } else {
+
+    prefix = findPrefix(inSrc, element, listCounters);
+
+    var pOut = "";
+    for (var i=0; i<textElements.length; i++) {
+      pOut += processTextElement(inSrc, textElements[i]);
+    }
+
+    // replace Unicode quotation marks (double and single)
+    pOut = standardQMarks(pOut);
+
+    result.text = prefix+pOut;
+  }
+
+  var indent_prefix = '> ';
+  var indent_alt_prefix = '> <sub>';
+  if (inIndent && !inSrc) {
+    if (/^#*\s/.test(result.text)) { // don't subscript-prefix header prefix
+      result.text = indent_alt_prefix + result.text;
+    } else {
+      result.text = indent_prefix + result.text;
+    }
+  }
+
+  return result;
+}
+
+// Add correct prefix to list items.
+function findPrefix(inSrc, element, listCounters) {
+  var prefix = "";
+  if (!inSrc) {
+    if (element.getType()===DocumentApp.ElementType.PARAGRAPH) {
+      var paragraphObj = element;
+      switch (paragraphObj.getHeading()) {
+        // Add a # for each heading level. No break, so we accumulate the right number.
+        case DocumentApp.ParagraphHeading.HEADING6: prefix+="#";
+        case DocumentApp.ParagraphHeading.HEADING5: prefix+="#";
+        case DocumentApp.ParagraphHeading.HEADING4: prefix+="#";
+        case DocumentApp.ParagraphHeading.HEADING3: prefix+="#";
+        case DocumentApp.ParagraphHeading.HEADING2: prefix+="#";
+        case DocumentApp.ParagraphHeading.HEADING1: prefix+="# ";
+        default:
+      }
+    } else if (element.getType()===DocumentApp.ElementType.LIST_ITEM) {
+      var listItem = element;
+      var nesting = listItem.getNestingLevel()
+      for (var i=0; i<nesting; i++) {
+        prefix += "    ";
+      }
+      var gt = listItem.getGlyphType();
+      // Bullet list (<ul>):
+      if (gt === DocumentApp.GlyphType.BULLET
+          || gt === DocumentApp.GlyphType.HOLLOW_BULLET
+          || gt === DocumentApp.GlyphType.SQUARE_BULLET) {
+        prefix += "* ";
+      } else {
+        // Ordered list (<ol>):
+        var key = listItem.getListId() + '.' + listItem.getNestingLevel();
+        var counter = listCounters[key] || 0;
+        counter++;
+        listCounters[key] = counter;
+        prefix += counter+". ";
+      }
+    }
+  }
+  return prefix;
+}
+
+function processTextElement(inSrc, txt) {
+  if (typeof(txt) === 'string') {
+    return txt;
+  }
+
+  var pOut = txt.getText();
+  if (! txt.getTextAttributeIndices) {
+    return pOut;
+  }
+
+//  Logger.log("Initial String: " + pOut)
+
+  // CRC introducing reformatted_txt to let us apply rational formatting that we can actually parse
+  var reformatted_txt = txt.copy();
+  reformatted_txt.deleteText(0,pOut.length-1);
+  reformatted_txt = reformatted_txt.setText(pOut);
+
+  var attrs = txt.getTextAttributeIndices();
+  var lastOff = pOut.length;
+  // We will run through this loop multiple times for the things we care about.
+  // Font
+  // URL
+  // Then for alignment
+  // Then for bold
+  // Then for italic.
+
+  // FONTs
+  var lastOff = pOut.length; // loop goes backwards, so this holds
+  for (var i=attrs.length-1; i>=0; i--) {
+    var off=attrs[i];
+    var font=txt.getFontFamily(off)
+     if (font) {
+       while (i>=1 && txt.getFontFamily(attrs[i-1])==font) {
+          // detect fonts that are in multiple pieces because of errors on formatting:
+          i-=1;
+          off=attrs[i];
+       }
+       reformatted_txt.setFontFamily(off, lastOff-1, font);
+     }
+    lastOff=off;
+  }
+
+  // URL
+  // XXX TODO actually convert to URL text here.
+  var lastOff=pOut.length;
+  for (var i=attrs.length-1; i>=0; i--) {
+    var off=attrs[i];
+    var url=txt.getLinkUrl(off);
+     if (url) {
+       while (i>=1 && txt.getLinkUrl(attrs[i-1]) == url) {
+          // detect urls that are in multiple pieces because of errors on formatting:
+          i-=1;
+          off=attrs[i];
+       }
+     reformatted_txt.setLinkUrl(off, lastOff-1, url);
+     }
+    lastOff=off;
+  }
+
+  // alignment
+  var lastOff=pOut.length;
+  for (var i=attrs.length-1; i>=0; i--) {
+    var off=attrs[i];
+    var alignment=txt.getTextAlignment(off);
+     if (alignment) { //
+       while (i>=1 && txt.getTextAlignment(attrs[i-1]) == alignment) {
+          i-=1;
+          off=attrs[i];
+       }
+     reformatted_txt.setTextAlignment(off, lastOff-1, alignment);
+     }
+    lastOff=off;
+  }
+
+  // strike
+  var lastOff=pOut.length;
+  for (var i=attrs.length-1; i>=0; i--) {
+    var off=attrs[i];
+    var strike=txt.isStrikethrough(off);
+     if (strike) {
+       while (i>=1 && txt.isStrikethrough(attrs[i-1])) {
+          i-=1;
+          off=attrs[i];
+       }
+     reformatted_txt.setStrikethrough(off, lastOff-1, strike);
+     }
+    lastOff=off;
+  }
+
+  // bold
+  var lastOff=pOut.length;
+  for (var i=attrs.length-1; i>=0; i--) {
+    var off=attrs[i];
+    var bold=txt.isBold(off);
+     if (bold) {
+       while (i>=1 && txt.isBold(attrs[i-1])) {
+          i-=1;
+          off=attrs[i];
+       }
+     reformatted_txt.setBold(off, lastOff-1, bold);
+     }
+    lastOff=off;
+  }
+
+  // italics
+  var lastOff=pOut.length;
+  for (var i=attrs.length-1; i>=0; i--) {
+    var off=attrs[i];
+    var italic=txt.isItalic(off);
+     if (italic) {
+       while (i>=1 && txt.isItalic(attrs[i-1])) {
+          i-=1;
+          off=attrs[i];
+       }
+     reformatted_txt.setItalic(off, lastOff-1, italic);
+     }
+    lastOff=off;
+  }
+
+
+  var mOut=""; // Modified out string
+  var harmonized_attrs = reformatted_txt.getTextAttributeIndices();
+  reformatted_txt.getTextAttributeIndices(); // @lmmx: is this a typo...?
+  pOut = reformatted_txt.getText();
+
+
+  // Markdown is farily picky about how it will let you intersperse spaces around words and strong/italics chars. This regex (hopefully) clears this up
+  // Match any number of \*, followed by spaces/word boundaries against anything that is not the \*, followed by boundaries, spaces and * again.
+  // Test case at http://jsfiddle.net/ovqLv0s9/2/
+
+  var reAlignStars = /(\*+)(\s*\b)([^\*]+)(\b\s*)(\*+)/g;
+
+  var lastOff=pOut.length;
+  for (var i=harmonized_attrs.length-1; i>=0; i--) {
+    var off=harmonized_attrs[i];
+
+    var raw_text = pOut.substring(off, lastOff)
+
+    var d1 = ""; // @lmmx: build up a modifier prefix
+    var d2 = ""; // @lmmx: ...and suffix
+
+    var end_font;
+
+    var mark_bold = false;
+    var mark_italic = false;
+    var mark_code = false;
+    var mark_sup = false;
+    var mark_sub = false;
+    var mark_strike = false;
+
+    // The end of the text block is a special case.
+    if (lastOff == pOut.length) {
+      end_font = reformatted_txt.getFontFamily(lastOff - 1)
+      if (end_font) {
+        if (!inSrc && end_font===end_font.COURIER_NEW) {
+          mark_code = true;
+        }
+      }
+      if (reformatted_txt.isBold(lastOff -1)) {
+        mark_bold = true;
+      }
+      if (reformatted_txt.isItalic(lastOff - 1)) {
+        // edbacher: changed this to handle bold italic properly.
+        mark_italic = true;
+      }
+      if (reformatted_txt.isStrikethrough(lastOff - 1)) {
+        mark_strike = true;
+      }
+      if (reformatted_txt.getTextAlignment(lastOff - 1)===DocumentApp.TextAlignment.SUPERSCRIPT) {
+        mark_sup = true;
+      }
+      if (reformatted_txt.getTextAlignment(lastOff - 1)===DocumentApp.TextAlignment.SUBSCRIPT) {
+        mark_sub = true;
+      }
+    } else {
+      end_font = reformatted_txt.getFontFamily(lastOff -1 )
+      if (end_font) {
+        if (!inSrc && end_font===end_font.COURIER_NEW && reformatted_txt.getFontFamily(lastOff) != end_font) {
+          mark_code=true;
+        }
+      }
+      if (reformatted_txt.isBold(lastOff - 1) && !reformatted_txt.isBold(lastOff) ) {
+        mark_bold=true;
+      }
+      if (reformatted_txt.isStrikethrough(lastOff - 1) && !reformatted_txt.isStrikethrough(lastOff)) {
+        mark_strike=true;
+      }
+      if (reformatted_txt.isItalic(lastOff - 1) && !reformatted_txt.isItalic(lastOff)) {
+        mark_italic=true;
+      }
+      if (reformatted_txt.getTextAlignment(lastOff - 1)===DocumentApp.TextAlignment.SUPERSCRIPT) {
+        if (reformatted_txt.getTextAlignment(lastOff)!==DocumentApp.TextAlignment.SUPERSCRIPT) {
+          mark_sup = true;
+        }
+      }
+      if (reformatted_txt.getTextAlignment(lastOff - 1)===DocumentApp.TextAlignment.SUBSCRIPT) {
+        if (reformatted_txt.getTextAlignment(lastOff)!==DocumentApp.TextAlignment.SUBSCRIPT) {
+          mark_sub = true;
+        }
+      }
+    }
+
+    if (mark_code) {
+      d2 = '`'; // shouldn't these go last? or will it interfere w/ reAlignStars?
+    }
+    if (mark_bold) {
+      d2 = "**" + d2;
+    }
+    if (mark_italic) {
+      d2 = "*" + d2;
+    }
+    if (mark_strike) {
+      d2 = "</strike>" + d2;
+    }
+    if (mark_sup) {
+      d2 = '</sup>' + d2;
+    }
+    if (mark_sub) {
+      d2 = '</sub>' + d2;
+    }
+
+    mark_bold = mark_italic = mark_code = mark_sup = mark_sub = mark_strike = false;
+
+    var font=reformatted_txt.getFontFamily(off);
+    if (off == 0) {
+      if (font) {
+        if (!inSrc && font===font.COURIER_NEW) {
+          mark_code = true;
+        }
+      }
+      if (reformatted_txt.isBold(off)) {
+        mark_bold = true;
+      }
+      if (reformatted_txt.isItalic(off)) {
+        mark_italic = true;
+      }
+      if (reformatted_txt.isStrikethrough(off)) {
+        mark_strike = true;
+      }
+      if (reformatted_txt.getTextAlignment(off)===DocumentApp.TextAlignment.SUPERSCRIPT) {
+        mark_sup = true;
+      }
+      if (reformatted_txt.getTextAlignment(off)===DocumentApp.TextAlignment.SUBSCRIPT) {
+        mark_sub = true;
+      }
+    } else {
+      if (font) {
+        if (!inSrc && font===font.COURIER_NEW && reformatted_txt.getFontFamily(off - 1) != font) {
+          mark_code=true;
+        }
+      }
+      if (reformatted_txt.isBold(off) && !reformatted_txt.isBold(off -1) ) {
+        mark_bold=true;
+      }
+      if (reformatted_txt.isItalic(off) && !reformatted_txt.isItalic(off - 1)) {
+        mark_italic=true;
+      }
+      if (reformatted_txt.isStrikethrough(off) && !reformatted_txt.isStrikethrough(off - 1)) {
+        mark_strike=true;
+      }
+      if (reformatted_txt.getTextAlignment(off)===DocumentApp.TextAlignment.SUPERSCRIPT) {
+        if (reformatted_txt.getTextAlignment(off - 1)!==DocumentApp.TextAlignment.SUPERSCRIPT) {
+          mark_sup = true;
+        }
+      }
+      if (reformatted_txt.getTextAlignment(off)===DocumentApp.TextAlignment.SUBSCRIPT) {
+        if (reformatted_txt.getTextAlignment(off - 1)!==DocumentApp.TextAlignment.SUBSCRIPT) {
+          mark_sub = true;
+        }
+      }
+    }
+
+
+    if (mark_code) {
+      d1 = '`';
+    }
+
+    if (mark_bold) {
+      d1 = d1 + "**";
+    }
+
+    if (mark_italic) {
+      d1 = d1 + "*";
+    }
+
+    if (mark_sup) {
+      d1 = d1 + '<sup>';
+    }
+
+    if (mark_sub) {
+      d1 = d1 + '<sub>';
+    }
+
+    if (mark_strike) {
+      d1 = d1 + '<strike>';
+    }
+
+    var url=reformatted_txt.getLinkUrl(off);
+    if (url) {
+      mOut = d1 + '['+ raw_text +']('+url+')' + d2 + mOut;
+    } else {
+      var new_text = d1 + raw_text + d2;
+      new_text = new_text.replace(reAlignStars, "$2$1$3$5$4");
+      mOut =  new_text + mOut;
+    }
+
+    lastOff=off;
+//    Logger.log("Modified String: " + mOut)
+  }
+
+  mOut = pOut.substring(0, off) + mOut;
+  return mOut;
+}
\ No newline at end of file
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/README.md b/examples/gemini/python/docs-agent/docs_agent/interfaces/README.md
index 27e2ae9ae..bd383e5ae 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/README.md
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/README.md
@@ -116,21 +116,21 @@ credentials (via `gcloud`) stored on your host machine.
    (`application_default_credentials.json`) in the `$HOME/.config/gcloud/`
    directory of your host machine.
 
-## 4. Clone the Docs Agent project
+## 4. Clone the Docs Agent project repository
 
 **Note**: This guide assumes that you're creating a new project directory
 from your `$HOME` directory.
 
-1. Clone the following repo:
+1. Clone the following internal repo:
 
    ```posix-terminal
-   git clone https://github.com/google/generative-ai-docs.git
+   git clone sso://doc-llm-internal/docs-agent
    ```
 
-2. Go to the Docs Agent project directory:
+2. Go to the project directory:
 
    ```posix-terminal
-   cd generative-ai-docs/examples/gemini/python/docs-agent
+   cd docs-agent
    ```
 
 3. Install dependencies using `poetry`:
@@ -139,6 +139,8 @@ from your `$HOME` directory.
    poetry install
    ```
 
+   This may take some time to complete.
+
 ## 5. Set up an alias to the gemini command
 
 **Note**: If your Docs Agent project is not cloned in the `$HOME` directory,
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/__init__.py b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/__init__.py
index 629ffe29f..58c4c46d9 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/__init__.py
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/__init__.py
@@ -19,7 +19,7 @@
 from docs_agent.utilities import config
 
 
-def create_app(product: config.ProductConfig):
+def create_app(product: config.ProductConfig, app_mode: str = "web"):
     app = Flask(__name__)
-    app.register_blueprint(chatui.construct_blueprint(product_config=product))
+    app.register_blueprint(chatui.construct_blueprint(product_config=product, app_mode=app_mode))
     return app
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/chatui.py b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/chatui.py
index 084dfa884..3e0b351ed 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/chatui.py
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/chatui.py
@@ -45,19 +45,32 @@
 
 
 # This is used to define the app blueprint using a productConfig
-def construct_blueprint(product_config: config.ProductConfig):
+def construct_blueprint(product_config: config.ProductConfig, app_mode: str = None):
     bp = Blueprint("chatui", __name__)
     if product_config.db_type == "google_semantic_retriever":
         docs_agent = DocsAgent(config=product_config, init_chroma=False)
     else:
         docs_agent = DocsAgent(config=product_config)
-    logging.info(f"Launching the flask app for product: {product_config.product_name}")
+    logging.info(f"Launching the Flask app for product: {product_config.product_name} with app_mode: {app_mode}")
+    # Assign templates and redirects
+    if app_mode == "web":
+        app_template = "chatui/index.html"
+        redirect_index = "chatui.index"
+    elif app_mode == "experimental":
+        app_template = "chatui-experimental/index.html"
+        redirect_index = "chatui-experimental.index"
+    elif app_mode == "widget":
+        app_template = "chat-widget/index.html"
+        redirect_index = "chat-widget.index"
+    else:
+        app_template = "chatui/index.html"
+        redirect_index = "chatui.index"
 
     @bp.route("/", methods=["GET", "POST"])
     def index():
         server_url = request.url_root.replace("http", "https")
         return render_template(
-            "chatui/index.html",
+            app_template,
             product=product_config.product_name,
             server_url=server_url,
         )
@@ -73,7 +86,7 @@ def api():
                     context,
                     sources_ref,
                     plain_token,
-                ) = ask_model_2_with_sources(input["question"], agent=docs_agent)
+                ) = ask_model_with_sources(input["question"], agent=docs_agent)
                 source_array = []
                 for source in sources_ref:
                     source_array.append(source.returnDictionary())
@@ -100,7 +113,7 @@ def like():
             log_like(is_like, str(uuid_found).strip())
             return "OK"
         else:
-            return redirect(url_for("chatui.index"))
+            return redirect(url_for(redirect_index))
 
     @bp.route("/rewrite", methods=["GET", "POST"])
     def rewrite():
@@ -149,10 +162,7 @@ def rewrite():
                 file.close()
             return "OK"
         else:
-            if product_config.docs_agent_config == "experimental":
-                return redirect(url_for("chatui.index_experimental"))
-            elif product_config.docs_agent_config == "normal":
-                return redirect(url_for("chatui.index"))
+            return redirect(url_for(redirect_index))
 
     # Render a response page when the user asks a question
     # using input text box.
@@ -160,17 +170,9 @@ def rewrite():
     def result():
         if request.method == "POST":
             question = request.form["question"]
-            if product_config.docs_agent_config == "experimental":
-                return ask_model2(question, agent=docs_agent, template="chatui/index_experimental.html")
-            elif product_config.docs_agent_config == "normal":
-                return ask_model2(
-                    question, agent=docs_agent, template="chatui/index.html"
-                )
+            return ask_model(question, agent=docs_agent, template=app_template)
         else:
-            if product_config.docs_agent_config == "experimental":
-                return redirect(url_for("chatui.index_experimental"))
-            elif product_config.docs_agent_config == "normal":
-                return redirect(url_for("chatui.index"))
+            return redirect(url_for(redirect_index))
 
     # Render a response page when the user clicks a question
     # from the related questions list.
@@ -178,17 +180,9 @@ def result():
     def question(ask):
         if request.method == "GET":
             question = urllib.parse.unquote_plus(ask)
-            if product_config.docs_agent_config == "experimental":
-                return ask_model2(question, agent=docs_agent, template="chatui/index_experimental.html")
-            elif product_config.docs_agent_config == "normal":
-                return ask_model2(
-                    question, agent=docs_agent, template="chatui/index.html"
-                )
+            return ask_model(question, agent=docs_agent, template=app_template)
         else:
-            if product_config.docs_agent_config == "experimental":
-                return redirect(url_for("chatui.index_expiremental"))
-            elif product_config.docs_agent_config == "normal":
-                return redirect(url_for("chatui.index"))
+            return redirect(url_for(redirect_index))
 
     return bp
 
@@ -196,7 +190,7 @@ def question(ask):
 # Construct a set of prompts using the user question, send the prompts to
 # the lanaguage model, receive responses, and present them into a page.
 # Use template to specify a custom template for the classic web UI
-def ask_model2(question, agent, template: str = "chatui/index.html"):
+def ask_model(question, agent, template: str = "chatui/index.html"):
     # Returns a built context, a total token count of the context and an array
     # of sourceOBJ
     full_prompt = ""
@@ -307,7 +301,7 @@ def ask_model2(question, agent, template: str = "chatui/index.html"):
 # Not fully implemented
 # This method is used for the API endpoint, so it returns values that can be
 # packaged as JSON
-def ask_model_2_with_sources(question, agent):
+def ask_model_with_sources(question, agent):
     docs_agent = agent
     full_prompt = ""
     context, plain_token, sources_ref = docs_agent.query_vector_store_to_build(
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui.css b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui.css
index fb67d4b4b..7155e750d 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui.css
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui.css
@@ -93,18 +93,20 @@ body {
     font-size: 0.9em;
     font-family: system-ui;
     line-height: 150%;
+    word-break: break-word;
     padding: 4px;
   }
 
   #response-box {
     font-size: 1.0em;
     font-family: sans-serif;
-    line-height: 100%;
+    line-height: 140%;
     margin-top: 10px;
   }
 
   #suggested-questions {
     font-family: sans-serif;
+    word-break: break-word;
   }
 
   #context-content{
@@ -176,6 +178,13 @@ body {
     margin-top: 12px;
   }
 
+  #answerable-span {
+    font-size: small;
+    font-family: system-ui;
+    float: right;
+    padding: 10px;
+  }
+
   /* ======= Style by class ======= */
 
   .hidden {
@@ -240,6 +249,7 @@ body {
   .related-questions {
     margin-bottom: 20px;
     font-size: 0.9em;
+    line-height: 140%;
   }
 
   /* ======= Style buttons by ID ======= */
@@ -291,7 +301,7 @@ body {
   #edit-text-area {
     font: 13px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
     max-height: 500px;
-    max-width: 650px;
+    max-width: -webkit-fill-available;
     height: 300px;
     width: 650px;
     padding: 8px;
@@ -340,7 +350,7 @@ body {
 
   .search input[type="text"] {
     border: 0;
-    width: 91%;
+    width: calc(100% - 65px);
     padding: 10px;
   }
 
@@ -505,3 +515,17 @@ body {
     margin-bottom: 0;
   }
 
+/* Loader animation */
+/* Source: https://css-loaders.com/classic/ */
+.loader {
+  width: fit-content;
+  font-family: monospace;
+  font-size: 14px;
+  margin-left: 13px;
+  clip-path: inset(0 3ch 0 0);
+  animation: animation 1s steps(4) infinite;
+}
+.loader:before {
+  content:"Generating a response..."
+}
+@keyframes animation {to{clip-path: inset(0 -1ch 0 0)}}
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-old.css b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-widget.css
similarity index 68%
rename from examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-old.css
rename to examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-widget.css
index b1c0c1b3c..a0732ba9f 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-old.css
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-widget.css
@@ -22,13 +22,13 @@ body {
     font-weight: 300;
     max-width: 960px;
     margin: auto;
-    background-color: #d9d9d9;
+    background-color: white;
     padding-top: 15px;
     padding-bottom: 15px;
   }
 
   a {
-    color: #22578c;
+    color: #0a619a;
   }
 
   p {
@@ -39,15 +39,18 @@ body {
   h1 {
     margin: 0 0 0.5em;
     font-weight: 500;
-    font-size: 2.0em;
-    margin-left: 0.8em;
-    margin-top: 0.3em;
+    font-size: 1.3em;
+    margin-top: 0.1em;
+    margin-left: 0.1em;
+    margin-bottom: 0.9em;
   }
 
   h2 {
     margin: 0;
-    margin-top: 17px;
-    margin-bottom: 15px;
+    margin-top: 15px;
+    margin-bottom: 10px;
+    font-weight: normal;
+    font-size: 1.4em;
   }
 
   h3 {
@@ -58,22 +61,32 @@ body {
 
   h4 {
     color: #505050;
-    margin: 0;
     margin-top: 3px;
-    margin-bottom: 10px;
+    margin-left: 5px;
+    margin-bottom: 8px;
   }
 
   li {
     margin: 0 0 0.3em;
   }
 
-  /* code {
-      font-family: math;
-      color: darkgreen;
-  } */
+  code {
+    font-family: math;
+    color: darkgreen;
+    text-wrap: pretty;
+  }
 
   /* ======= Style layout by ID ======= */
 
+  #iframe-box {
+    margin: 0px;
+    max-width: 760px;
+    font: 15px arial, sans-serif;
+    background-color: white;
+    padding-bottom: 0px;
+    padding-left: 0px;
+  }
+
   #callout-box {
     margin: auto;
     max-width: 800px;
@@ -86,6 +99,108 @@ body {
     border-radius: 15px;
   }
 
+  #important-box {
+    font-size: 0.9em;
+    font-family: system-ui;
+    word-break: break-word;
+    line-height: 150%;
+    word-break: break-word;
+    padding: 4px;
+  }
+
+  #response-box {
+    font-size: 1.0em;
+    font-family: sans-serif;
+    line-height: 140%;
+    margin-top: 10px;
+  }
+
+  #suggested-questions {
+    font-family: sans-serif;
+    word-break: break-word;
+  }
+
+  #source-pages {
+    font-family: sans-serif;
+    word-break: break-all;
+  }
+
+  #context-content{
+    background: #d7dbd7;
+    font-family: sans-serif;
+    word-break: break-all;
+  }
+
+  #context-pre {
+    font-size: small;
+    font-family: monospace;
+    text-wrap: pretty;
+    margin-top: 0.3px;
+  }
+
+  #probability-box {
+    font-size: small;
+    padding: 4px;
+    margin-bottom: 10px;
+  }
+
+  #grounding-box {
+    font-size: small;
+    padding: 4px;
+    word-break: break-all;
+  }
+
+  #grounding-pre {
+    font-size: small;
+    font-family: monospace;
+    text-wrap: pretty;
+  }
+
+  #reference-box {
+    font-size: 0.9em;
+    font-family: system-ui;
+    text-wrap: pretty;
+    word-break: break-all;
+    margin-bottom: 12px;
+    line-height: 1.5em;
+  }
+
+  #reference-box-no-aqa {
+    font-size: 0.9em;
+    font-family: system-ui;
+    text-wrap: pretty;
+    word-break: break-all;
+    line-height: 1.5em;
+  }
+
+  #aqa-content{
+    background: #9fc7db;
+    font-family: math;
+  }
+
+  #aqa-label{
+    background: #49a5d2;
+  }
+
+  #aqa-json {
+    font-family: system-ui;
+    font-size: small;
+    text-wrap: pretty;
+    word-break: break-all;
+    margin: 0;
+  }
+
+  #rewrite-buttons-box {
+    margin-top: 12px;
+  }
+
+  #answerable-span {
+    font-size: small;
+    font-family: system-ui;
+    float: right;
+    padding: 10px;
+  }
+
   /* ======= Style by class ======= */
 
   .hidden {
@@ -137,7 +252,7 @@ body {
     border-radius: 15px;
   }
 
-  .question, .response, .response-text, .fact-checked-text, .related-questions {
+  .question, .response, .response-text, .fact-checked-text {
     max-width: 700px;
     margin-left: 3px;
   }
@@ -147,6 +262,18 @@ body {
     margin-left: 10px;
   }
 
+  .related-questions {
+    margin-bottom: 20px;
+    font-size: 0.9em;
+    line-height: 140%;
+  }
+
+  .relevant-sources {
+    margin-bottom: 20px;
+    font-size: 0.9em;
+    line-height: 140%;
+  }
+
   /* ======= Style buttons by ID ======= */
 
   #rewrite-button {
@@ -156,6 +283,7 @@ body {
     padding: 7px;
     border-radius: 5px;
     cursor:pointer;
+    margin-top: 0.3em;
   }
 
   #rewrite-button:hover {
@@ -189,14 +317,15 @@ body {
 
   #submit-result {
     color: #027f02d6;
+    font-family: fantasy;
   }
 
   #edit-text-area {
     font: 13px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
     max-height: 500px;
-    max-width: 650px;
+    max-width: -webkit-fill-available;
     height: 300px;
-    width: 650px;
+    width: 580px;
     padding: 8px;
   }
 
@@ -234,7 +363,7 @@ body {
   .search {
     border: 2px solid #CF5C3F;
     overflow: auto;
-    max-width: 700px;
+    max-width: 600px;
     margin-top: 15px;
     margin-left: 10px;
     margin-bottom: 10px;
@@ -243,7 +372,7 @@ body {
 
   .search input[type="text"] {
     border: 0;
-    width: 91%;
+    width: calc(100% - 65px);
     padding: 10px;
   }
 
@@ -269,7 +398,7 @@ body {
 
   .accordion {
     max-width: 65em;
-    margin-bottom: 1em;
+    #margin-bottom: 1em;
   }
 
   .accordion > input[type="checkbox"] {
@@ -284,7 +413,8 @@ body {
   }
 
   .accordion .reference-content {
-    font-size: 13px;
+    font-size: 15px;
+    font-family: serif;
   }
 
   .accordion > input[type="checkbox"]:checked ~ .content {
@@ -298,17 +428,19 @@ body {
 
   .accordion .handle {
     margin: 0;
-    font-size: 1.125em;
-    line-height: 1.2em;
+    font-size: 1.0em;
   }
 
   .accordion label {
     display: block;
     font-weight: normal;
     border: 2px solid #000;
-    padding: 12px;
+    #padding: 12px;
     background: #4490b8ab;
-    border-radius: 15px;
+    #border-radius: 15px;
+    padding: 5px;
+    #background: #027f023b;
+    border-radius: 10px;
   }
 
   .accordion label:hover,
@@ -403,4 +535,20 @@ body {
 
   .accordion-source p:last-child {
     margin-bottom: 0;
-  }
\ No newline at end of file
+  }
+
+/* Loader animation */
+/* Source: https://css-loaders.com/classic/ */
+.loader {
+  width: fit-content;
+  font-family: monospace;
+  font-size: 14px;
+  margin-left: 13px;
+  clip-path: inset(0 3ch 0 0);
+  animation: animation 1s steps(4) infinite;
+}
+.loader:before {
+  content:"Generating a response..."
+}
+@keyframes animation {to{clip-path: inset(0 -1ch 0 0)}}
+
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/javascript/app.js b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/javascript/app.js
index cf5065e9e..32908b566 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/javascript/app.js
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/javascript/app.js
@@ -26,6 +26,20 @@ if (askButton != null){
   });
 }
 
+// Display the "loading" message when a related question is clicked.
+let relatedQuestions = document.getElementById('suggested-questions');
+
+if (relatedQuestions != null){
+  questions = relatedQuestions.getElementsByTagName('a');
+  for(i=0; i<questions.length; i++){
+    questions[i].addEventListener('click',function (){
+      if (loadingDiv.classList.contains("hidden")){
+        loadingDiv.classList.remove("hidden");
+      }
+    });
+  }
+}
+
 // Display the "aqa-box" div only if the aqa json response is included.
 let aqaContent = document.getElementById('aqa-content');
 let aqaBox = document.getElementById('aqa-box');
@@ -155,3 +169,30 @@ if (rewriteSubmitButton != null){
   }, false);
 }
 
+// Monitor the width of the Docs Agent widget.
+let answerableSpan = document.getElementById('answerable-span');
+//let rewriteButton = document.getElementById('rewrite-button');
+const element = document.querySelector('#chat-border');
+
+const resizeObserver = new ResizeObserver(entries => {
+  for (const entry of entries) {
+    const width = entry.contentRect.width;
+    console.log('Element width changed to:', width);
+    if (width < 520) {
+      answerableSpan.classList.add("hidden");
+    }else{
+      if (answerableSpan.classList.contains("hidden")) {
+        answerableSpan.classList.remove("hidden");
+      }
+    }
+    if (width < 300) {
+      rewriteButton.classList.add("hidden");
+    }else{
+      if (rewriteButton.classList.contains("hidden")) {
+        rewriteButton.classList.remove("hidden");
+      }
+    }
+  }
+});
+
+resizeObserver.observe(element);
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chat-widget/base.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chat-widget/base.html
new file mode 100644
index 000000000..42987dee4
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chat-widget/base.html
@@ -0,0 +1,25 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <link rel="stylesheet" href="{{ url_for('static', filename='css/style-widget.css') }}">
+    <link rel="stylesheet" href="{{ url_for('static', filename='css/chatbox.css') }}">
+    <link rel="shortcut icon" href="{{ url_for('static', filename='images/favicon.png') }}">
+    <title>{{ product }} ChatBot</title>
+</head>
+<body>
+  <div id='iframe-box'>
+    <div name="title"><h1>{{ product }} Docs Agent</h1></div>
+    <div id='chat-border'>
+      <div class="body-content">
+        {% block content %} {% endblock %}
+      </div>
+    </div>
+  </div>
+  <script>
+    let urlRewrite = "{{ server_url }}rewrite";
+    let urlLike = "{{ server_url }}like";
+  </script>
+  <script src="{{ url_for('static', filename='javascript/app.js') }}"></script>
+</body>
+</html>
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chat-widget/index.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chat-widget/index.html
new file mode 100644
index 000000000..437b9bfd2
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chat-widget/index.html
@@ -0,0 +1,16 @@
+{% extends 'chat-widget/base.html' %}
+
+{% block content %}
+  <div class="search">
+    <form class="search-form" method="POST" action="/result">
+      <input type="text" name="question" placeholder="Ask me about topics related to {{ product }}.">
+      <input type="submit" value="Ask" id="ask-button">
+    </form>
+  </div>
+  <div class="loader hidden" id="loading-div"></div>
+  {% if response %}
+  <div class="full-response">
+    {% include 'chat-widget/result.html' %}
+  </div>
+  {% endif %}
+{% endblock %}
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chat-widget/result.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chat-widget/result.html
new file mode 100644
index 000000000..e3c65c759
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chat-widget/result.html
@@ -0,0 +1,168 @@
+<!--Sets defaults-->
+{% if search_result[0] %}
+  {% set check_url = search_result[0].section.url %}
+  {% set probability = search_result[0].probability %}
+{% else %}
+  {% set check_url = "None" %}
+  {% set probability = "0.0" %}
+{% endif %}
+
+{% if aqa_response_in_html != "" %}
+  <!--Gemini AQA template-->
+  <div class="question">
+    <h2>{{ question | replace("+", " ") | replace("%3F", "?")}}</h2>
+  </div>
+  {% if probability|float > 0.7 %}
+  <div class="important-box" id="important-box">
+    <b>Important:</b> The answer below is generated by Gemini. Please
+    verify this answer by visiting the source pages.
+  </div>
+  <div class="response-text" id="response-box">
+    <span id="gemini-response">
+      {{ md_to_html(response) | safe }}
+    </span>
+  </div>
+  <div class="relevant-sources">
+    <h4>Source pages</h4>
+    <span id="source-pages">
+      <ol>
+      {% for source in search_result %}
+        {% set section_url = named_link_html(source.section.url, source.section.url) %}
+        <li>{{ section_url | safe}}</li>
+      {% endfor %}
+      <ol>
+    </span>
+  </div>
+  <div class="related-questions">
+    <h4>Suggested questions</h4>
+    <span id="suggested-questions">
+      {{ related_questions | safe }}
+    </span>
+  </div>
+  {% else %}
+  <div class="response-text" id="response-box">
+    <span id="gemini-response">
+      I'm sorry, but I cannot answer this question at the moment.
+      <b>The answer to your question does not seem to be available in documentation.</b><br>
+      <br>
+      However, based on the documentation available in the corpus, you could ask questions like:
+    </span>
+  </div>
+  <div class="related-questions">
+    <!--<h4>Suggested questions</h4>-->
+    <span id="suggested-questions">
+      {{ related_questions | safe }}
+    </span>
+  </div>
+  {% endif %}
+  <div class="hidden">
+  <section class="accordion">
+    <input type="checkbox" name="collapse" id="handle1">
+    <h2 class="handle">
+      <label for="handle1">Context</label>
+    </h2>
+    <div class="content" id="context-content">
+      <div id="probability-box">
+        <b>Answerable probability</b>: {{"%.6f"|format(probability|float)}}
+      </div>
+      <div id="grounding-box">
+        <b>Grounding attributions</b>:
+	<pre id="grounding-pre">{{ final_context | safe }}</pre>
+      </div>
+      <div id="reference-box">
+      {% for source in search_result %}
+        {% set section_url = named_link_html(source.section.url, source.section.url) %}
+        <b>[{{loop.index}}]</b>{{ section_url | safe}}
+        <br>
+      {% endfor %}
+      </div>
+      <div class="hidden" id="aqa-box">
+        <section class="accordion">
+          <input type="checkbox" name="collapse" id="handle2">
+          <h2 class="handle">
+            <label for="handle2" id="aqa-label">AQA model's JSON response</label>
+          </h2>
+          <div class="content" id="aqa-content">
+	    <pre id="aqa-json">{{ aqa_response_in_html | safe }}</pre>
+          </div>
+        </section>
+      </div>
+    </div>
+  </section>
+  </div>
+{% else %}
+  <!--Gemini Pro template-->
+  <div class="question">
+    <h2>{{ question | replace("+", " ") | replace("%3F", "?")}}</h2>
+  </div>
+  <div class="important-box" id="important-box">
+    <!--Uses the URL from the first result-->
+    <b>Important:</b> The answer below is generated by the Gemini Pro model.
+    To verify this answer, please visit: {{named_link_html(check_url, check_url) | safe}}
+  </div>
+  <div class="response-text" id="response-box">
+    <span id="gemini-response">
+      {{ md_to_html(response) | safe }}
+    </span>
+  </div>
+  <div class="related-questions">
+    <h4>Suggested questions</h4>
+    <span id="suggested-questions">
+      {{ related_questions | safe }}
+    <span>
+  </div>
+  <section class="accordion">
+    <input type="checkbox" name="collapse" id="handle1">
+    <h2 class="handle">
+      <label for="handle1">Context</label>
+    </h2>
+    <div class="content" id="context-content">
+      <!-- {{ md_to_html(final_context) | safe }} -->
+      {% for source in search_result %}
+      <pre id="context-pre">{{md_to_html(source.section.content) | safe}}Reference <b>[{{loop.index}}]</b></pre>
+      {% endfor %}
+      <div id="reference-box-no-aqa">
+      {% for source in search_result %}
+        {% set section_url = named_link_html(source.section.url, source.section.url) %}
+        <b>[{{loop.index}}]</b>{{ section_url | safe}} ({{ source.distance }})
+        <br>
+      {% endfor %}
+      </div>
+    </div>
+  </section>
+{% endif %}
+
+<!--Common like/rewrite-->
+<div id="rewrite-buttons-box">
+  <input type="button" value="Rewrite this response" id="rewrite-button">
+  <input type="button" class="notselected" value="Like this response &#128077;" id="like-button">
+  {% if aqa_response_in_html != "" %}
+  <span id="answerable-span">
+    (<b>Answerable probability</b>: {{"%.3f"|format(probability|float)}})
+  </span>
+  {% endif %}
+</div>
+<div class="rewrite hidden" id="rewrite-box">
+  <h4 id="rewrite-question-header">Question:</h4>
+  <span id="rewrite-question-span">
+    <p>{{ question | replace("+", " ") | replace("%3F", "?")}}</p>
+  </span>
+  <h4 id="rewrite-response-header">Gemini's response:</h4>
+  <span id="rewrite-original-response-span">
+    {{ md_to_html(response) | safe }}
+  </span>
+  <h4>Rewrite:</h4>
+  <textarea id="edit-text-area">{{ response | safe }}</textarea>
+  <br>
+  <label for="user-id">User ID:</label>
+  <input type="text" id="user-id" name="user-id" placeholder="Optional">
+  <br>
+  <span id="submit-box">
+    <input type="button" value="Submit" id="submit-button">
+  </span>
+  <span id="submit-result">
+  </span>
+</div>
+<div class="hidden" id="uuid-box">
+  {{ uuid | safe }}
+</div>
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-experimental/base.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-experimental/base.html
new file mode 100644
index 000000000..944984182
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-experimental/base.html
@@ -0,0 +1,27 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <link rel="stylesheet" href="{{ url_for('static', filename='css/style-chatui.css') }}">
+    <link rel="stylesheet" href="{{ url_for('static', filename='css/chatbox.css') }}">
+    <link rel="shortcut icon" href="{{ url_for('static', filename='images/favicon.png') }}">
+    <title>{{ product }} ChatBot</title>
+</head>
+<body>
+  <div id='body-box'>
+    <div name="title"><h1>{{ product }} Docs Agent</h1></div>
+    <div id='chat-border'>
+      <div class="body-content">
+        {% block content %} {% endblock %}
+      </div>
+    </div>
+  </div>
+  <br>
+  <br>
+  <script>
+    let urlRewrite = "{{ server_url }}rewrite";
+    let urlLike = "{{ server_url }}like";
+  </script>
+  <script src="{{ url_for('static', filename='javascript/app.js') }}"></script>
+</body>
+</html>
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui/index_experimental.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-experimental/index.html
similarity index 90%
rename from examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui/index_experimental.html
rename to examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-experimental/index.html
index 088fb364b..97376ac8a 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui/index_experimental.html
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-experimental/index.html
@@ -12,7 +12,7 @@
   </div>
   {% if response %}
   <div class="full-response">
-    {% include 'chatui/result_experimental.html' %}
+    {% include 'chatui-experimental/result.html' %}
   </div>
   {% endif %}
 {% endblock %}
\ No newline at end of file
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui/result_experimental.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-experimental/result.html
similarity index 100%
rename from examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui/result_experimental.html
rename to examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-experimental/result.html
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui/index.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui/index.html
index 5d00e10b5..042f18d02 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui/index.html
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui/index.html
@@ -7,12 +7,10 @@
       <input type="submit" value="Ask" id="ask-button">
     </form>
   </div>
-  <div class="loading hidden" id="loading-div">
-    GENERATING A RESPONSE...
-  </div>
+  <div class="loader hidden" id="loading-div"></div>
   {% if response %}
   <div class="full-response">
     {% include 'chatui/result.html' %}
   </div>
   {% endif %}
-{% endblock %}
\ No newline at end of file
+{% endblock %}
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui/result.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui/result.html
index 37e695141..4bc14b304 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui/result.html
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui/result.html
@@ -1,10 +1,18 @@
+<!--Sets defaults-->
+{% if search_result[0] %}
+  {% set check_url = search_result[0].section.url %}
+  {% set probability = search_result[0].probability %}
+{% else %}
+  {% set check_url = "None" %}
+  {% set probability = "0.0" %}
+{% endif %}
+
 {% if aqa_response_in_html != "" %}
   <!--Gemini AQA template-->
   <div class="question">
     <h2>{{ question | replace("+", " ") | replace("%3F", "?")}}</h2>
   </div>
   <div class="important-box" id="important-box">
-    {% set check_url = search_result[0].section.url %}
     <b>Important:</b> The answer below is generated by the Gemini AQA model.
     To verify this answer, please visit: {{named_link_html(check_url, check_url) | safe}}
   </div>
@@ -26,7 +34,6 @@ <h2 class="handle">
     </h2>
     <div class="content" id="context-content">
       <div id="probability-box">
-        {% set probability = search_result[0].probability %}
         <b>Answerable probability</b>: {{"%.6f"|format(probability|float)}}
       </div>
       <div id="grounding-box">
@@ -60,7 +67,6 @@ <h2>{{ question | replace("+", " ") | replace("%3F", "?")}}</h2>
   </div>
   <div class="important-box" id="important-box">
     <!--Uses the URL from the first result-->
-    {% set check_url = search_result[0].section.url %}
     <b>Important:</b> The answer below is generated by the Gemini Pro model.
     To verify this answer, please visit: {{named_link_html(check_url, check_url) | safe}}
   </div>
@@ -100,6 +106,9 @@ <h2 class="handle">
 <div id="rewrite-buttons-box">
   <input type="button" value="Rewrite this response" id="rewrite-button">
   <input type="button" class="notselected" value="Like this response &#128077;" id="like-button">
+  <span id="answerable-span">
+    (<b>Answerable probability</b>: {{"%.3f"|format(probability|float)}})
+  </span>
 </div>
 <div class="rewrite hidden" id="rewrite-box">
   <h4 id="rewrite-question-header">Question:</h4>
@@ -112,6 +121,7 @@ <h4 id="rewrite-response-header">Gemini's response:</h4>
   </span>
   <h4>Rewrite:</h4>
   <textarea id="edit-text-area">{{ response | safe }}</textarea>
+  <br>
   <label for="user-id">User ID:</label>
   <input type="text" id="user-id" name="user-id" placeholder="Optional">
   <br>
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/cli.py b/examples/gemini/python/docs-agent/docs_agent/interfaces/cli.py
index d3de0ffc7..99fefa14c 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/cli.py
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/cli.py
@@ -97,14 +97,16 @@ def populate(config_file: str = None, product: list[str] = [""]):
 
 
 @cli.command()
-@click.option("--hostname", default=socket.gethostname())
-@click.option("--port", default=5000, type=int)
-@click.option("--debug", default=True, type=bool)
+@click.option("--hostname", default=socket.gethostname(), show_default=True)
+@click.option("--port", default=5000, show_default=True, type=int)
+@click.option("--debug", default=True, show_default=True, type=bool)
+@click.option("--app_mode", default=None, show_default=True, type=click.Choice(["web", "widget", "experimental"], case_sensitive=False), help="Specify a mode for the chatbot.")
 @common_options
 def chatbot(
     hostname: str,
     port: str,
     debug: str,
+    app_mode: str,
     config_file: str = None,
     product: list[str] = [""],
 ):
@@ -115,8 +117,10 @@ def chatbot(
     )
 
     product_file = product_config.products[0]
-    app = chatbot_flask.create_app(product=product_file)
-    click.echo("Launching the chatbot UI.")
+    if app_mode == None:
+        app_mode = product_file.app_mode
+    app = chatbot_flask.create_app(product=product_file, app_mode=app_mode)
+    click.echo(f"Launching the chatbot UI for product {product_file.product_name} in {app_mode} mode.")
     app.run(host=hostname, port=port, debug=debug)
 
 
diff --git a/examples/gemini/python/docs-agent/docs_agent/preprocess/files_to_plain_text.py b/examples/gemini/python/docs-agent/docs_agent/preprocess/files_to_plain_text.py
index 2b95c97f9..a3d99ec6f 100644
--- a/examples/gemini/python/docs-agent/docs_agent/preprocess/files_to_plain_text.py
+++ b/examples/gemini/python/docs-agent/docs_agent/preprocess/files_to_plain_text.py
@@ -18,7 +18,6 @@
 
 from markdown import markdown
 import shutil
-from bs4 import BeautifulSoup
 import os
 import re
 import json
@@ -36,7 +35,7 @@
     end_path_backslash,
     start_path_no_backslash,
 )
-from docs_agent.preprocess.splitters import markdown_splitter, html_splitter
+from docs_agent.preprocess.splitters import markdown_splitter, html_splitter, fidl_splitter
 from docs_agent.models import palm as palmModule
 import uuid
 
@@ -144,6 +143,7 @@ def process_files_from_input(
     f_count = 0
     md_count = 0
     html_count = 0
+    fidl_count = 0
     file_index = []
     full_file_metadata = {}
     resolved_output_path = resolve_path(product_config.output_path)
@@ -305,6 +305,52 @@ def process_files_from_input(
                             f"Select a valid markdown_splitter option in your configuration for {product_config.product_name}\n"
                         )
                         exit()
+            elif file.endswith(".fidl") and not file.startswith("_"):
+                filename_prefix = "index"
+                chunk_number = 0
+                fidl_count += 1
+                # Add filename to a list
+                file_index.append(relative_path)
+                with open(os.path.join(root, file), "r", encoding="utf-8") as auto:
+                    # Read the input FIDL file
+                    to_file = auto.read()
+                    # Split the FIDL file into a list of FIDL protocols.
+                    fidl_protocols = fidl_splitter.split_file_to_protocols(to_file)
+                    library_name = ""
+                    # Iterate the list of FIDL protocols.
+                    for fidl_protocol in fidl_protocols:
+                        # Identify the new FIDL chunk file path and name.
+                        filename_to_save = make_file_chunk_name(new_path=new_path, filename_prefix=filename_prefix, index=chunk_number)
+                        with open(filename_to_save, "w", encoding="utf-8") as new_file:
+                            new_file.write(fidl_protocol)
+                            new_file.close()
+                        chunk_number += 1
+                        # Handle metadata for the FIDL chunk
+                        md_hash = uuid.uuid3(namespace_uuid, fidl_protocol)
+                        uuid_file = uuid.uuid3(namespace_uuid, filename_to_save)
+                        origin_uuid = uuid.uuid3(namespace_uuid, relative_path)
+                        # Contruct URLs
+                        match_library = re.search(r"^Library\sname:\s+(.*)\n", fidl_protocol)
+                        if match_library:
+                            library_name = match_library.group(1)
+                        fidl_url = url_prefix + library_name
+                        full_file_metadata[filename_to_save] = {
+                            "UUID": str(uuid_file),
+                            "origin_uuid": str(origin_uuid),
+                            "source": str(original_input),
+                            "source_file": str(relative_path),
+                            "source_id": int(fidl_count),
+                            "page_title": str("None"),
+                            "section_title": str("None"),
+                            "section_name_id": str("None"),
+                            "section_id": int(1),
+                            "section_level": int(1),
+                            "previous_id": int(1),
+                            "URL": str(fidl_url),
+                            "md_hash": str(md_hash),
+                            "token_estimate": float(1.0),
+                            "full_token_estimate": float(1.0),
+                        }
             elif (
                 file.endswith(".htm") or file.endswith(".html")
             ) and not file.startswith("_"):
@@ -347,6 +393,14 @@ def make_relative_path(file: str, inputpath: str, root: str = None) -> str:
     return relative_path
 
 
+# Given a path, filename_prefix, chunk index, and an optional path extension (to save chunk)
+# Create a file chunk name
+def make_file_chunk_name(new_path: str, filename_prefix: str, index: int, extension: str = "md") -> str:
+    filename_to_save = (filename_prefix + "_" + str(index) + "." + extension)
+    full_filename = os.path.join(new_path, filename_to_save)
+    return full_filename
+
+
 # Given a path, file, chunk index, and an optional path extension (to save chunk)
 # Create a chunk name
 def make_chunk_name(new_path: str, file: str, index: int, extension: str = "md") -> str:
diff --git a/examples/gemini/python/docs-agent/docs_agent/preprocess/splitters/fidl_splitter.py b/examples/gemini/python/docs-agent/docs_agent/preprocess/splitters/fidl_splitter.py
new file mode 100644
index 000000000..4ea84779b
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/preprocess/splitters/fidl_splitter.py
@@ -0,0 +1,64 @@
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import re
+import os
+
+def split_file_to_protocols(this_file):
+    protocols = []
+    line_buffer = ""
+    protocol_name = ""
+    library_name = ""
+    index = 0
+    for line in this_file.split("\n"):
+        match_protocol = re.search(r"^closed\s+protocol\s+(.*)\s+\{", line)
+        match_library = re.search(r"^library\s+(.*);", line)
+        match_comment = re.search(r"^\s*\/\/\/\s+(.*)", line)
+        match_new_line = re.search(r"^\s*$", line)
+        match_end_bracket = re.search(r"^};", line)
+        if match_protocol:
+            #print("MATCHED [Protocol]: " + match_protocol.group(1))
+            protocol_name = match_protocol.group(1)
+            line_buffer += line + "\n"
+        elif match_library:
+            #print("MATCHED [Library]: " + match_library.group(1))
+            library_name = match_library.group(1)
+        elif match_comment:
+            #print("MATCHED [Comment]: " + match_comment.group(1))
+            line_buffer += line + "\n"
+        elif match_new_line:
+            #print("MATCHED [New line]")
+            line_buffer += line + "\n"
+        elif match_end_bracket:
+            #print("MATCHED [End bracket]")
+            line_buffer += line + "\n"
+            content_to_store = "Library name: " + library_name + "\n"
+            content_to_store += "Protocol name: " + protocol_name + "\n\n"
+            content_to_store += "In Fuchsia's " + library_name + " FIDL library, the " + protocol_name + " protocol interface is defined as the following:\n\n"
+            content_to_store += "<code>\n"
+            content_to_store += line_buffer.strip() + "\n"
+            content_to_store += "</code>\n"
+            if library_name != "" and protocol_name != "":
+                protocols.append(content_to_store)
+            # Clear the line butter when an end bracket is found.
+            line_buffer = ""
+            # Clear the protocol name when an end bracket is found.
+            protocol_name = ""
+        else:
+            line_buffer += line + "\n"
+        index += 0
+    return protocols
+
diff --git a/examples/gemini/python/docs-agent/docs_agent/preprocess/splitters/markdown_splitter.py b/examples/gemini/python/docs-agent/docs_agent/preprocess/splitters/markdown_splitter.py
index b46472c73..0e8834f79 100644
--- a/examples/gemini/python/docs-agent/docs_agent/preprocess/splitters/markdown_splitter.py
+++ b/examples/gemini/python/docs-agent/docs_agent/preprocess/splitters/markdown_splitter.py
@@ -15,7 +15,7 @@
 #
 
 from markdown import markdown
-from bs4 import BeautifulSoup
+import bs4
 import re, os
 from absl import logging
 from docs_agent.models import tokenCount
@@ -212,7 +212,7 @@ def markdown_to_text(markdown_string):
     # md -> html -> text since BeautifulSoup can extract text cleanly
     html = markdown(markdown_string)
     # Extract text
-    soup = BeautifulSoup(html, "html.parser")
+    soup = bs4.BeautifulSoup(html, "html.parser")
     text = "".join(soup.findAll(string=True))
     # Remove [][] in Markdown
     text = re.sub(r"\[(.*?)\]\[(.*?)\]", "\\1", text)
diff --git a/examples/gemini/python/docs-agent/docs_agent/utilities/config.py b/examples/gemini/python/docs-agent/docs_agent/utilities/config.py
index 4dc5f1bda..9031e821a 100644
--- a/examples/gemini/python/docs-agent/docs_agent/utilities/config.py
+++ b/examples/gemini/python/docs-agent/docs_agent/utilities/config.py
@@ -288,6 +288,7 @@ def __init__(
         docs_agent_config: str = None,
         markdown_splitter: str = "token_splitter",
         db_type: str = "chroma",
+        app_mode: str = "web",
     ):
         self.product_name = product_name
         self.docs_agent_config = docs_agent_config
@@ -299,6 +300,7 @@ def __init__(
         self.conditions = conditions
         self.log_level = log_level
         self.inputs = inputs
+        self.app_mode = app_mode
 
     def __str__(self):
         # Extracts the list of Inputs
@@ -313,6 +315,7 @@ def __str__(self):
         db_config_str = "\n".join(dbconfigs)
         return f"Product: {self.product_name}\n\
 Docs Agent config: {self.docs_agent_config}\n\
+App mode: {self.app_mode}\n\
 Markdown splitter: {self.markdown_splitter}\n\
 Database type: {self.db_type}\n\
 Output path: {self.output_path}\n\
@@ -374,6 +377,15 @@ def returnProducts(self, product: str = None) -> list[ConfigFile]:
                     name = item["product_name"]
                 except KeyError as error:
                     logging.error(f"Your configuration is missing a {error}")
+                    return sys.exit()
+                # Set the default value of `app_mode` to "web"
+                supported_app_modes = ["web", "experimental", "widget"]
+                try:
+                    app_mode = item["app_mode"]
+                except KeyError:
+                    app_mode = "web"
+                if app_mode not in supported_app_modes:
+                    logging.error(f"Your configuration is using an invalid mode: {app_mode}. Valid modes are {supported_app_modes}")
                     return sys.exit(1)
                 try:
                     product_config = ProductConfig(
@@ -383,12 +395,11 @@ def returnProducts(self, product: str = None) -> list[ConfigFile]:
                         db_type=item["db_type"],
                         output_path=item["output_path"],
                         db_configs=item["db_configs"],
-                        # vector_db_dir=item["vector_db_dir"],
-                        # collection_name=item["collection_name"],
                         log_level=item["log_level"],
                         models=item["models"],
                         conditions=item["conditions"],
                         inputs=item["inputs"],
+                        app_mode=app_mode,
                     )
                     # This is done for keys with children
                     # Inputs
diff --git a/examples/gemini/python/docs-agent/docs_agent/utilities/helpers.py b/examples/gemini/python/docs-agent/docs_agent/utilities/helpers.py
index e14d49545..8d316a73b 100644
--- a/examples/gemini/python/docs-agent/docs_agent/utilities/helpers.py
+++ b/examples/gemini/python/docs-agent/docs_agent/utilities/helpers.py
@@ -18,7 +18,7 @@
 
 import urllib, os
 from flask import url_for
-from bs4 import BeautifulSoup
+import bs4
 from pathlib import Path, PurePath
 import markdown
 
@@ -79,7 +79,7 @@ def add_scheme_url(url: str, scheme: str = "https"):
 # Parse a response containing a list of related questions from the language model
 # and convert it into an HTML-based list.
 def parse_related_questions_response_to_html_list(response):
-    soup = BeautifulSoup(response, "html.parser")
+    soup = bs4.BeautifulSoup(response, "html.parser")
     for item in soup.find_all("li"):
         if item.find("code"):
             # If there are <code> tags, strip the tags.
@@ -154,7 +154,7 @@ def build_list_html_links(
 
 # Build an html URL link
 def named_link_html(url: str, label: str = "", **kwargs):
-    soup = BeautifulSoup("")
+    soup = bs4.BeautifulSoup("")
     final_url = add_scheme_url(url)
     attrs = dict(href=f"{final_url}", target=f"_blank", **kwargs)
     tag = soup.new_tag(name="a", attrs=attrs)
diff --git a/examples/gemini/python/docs-agent/pyproject.toml b/examples/gemini/python/docs-agent/pyproject.toml
index 2844a43d6..f241d23f6 100644
--- a/examples/gemini/python/docs-agent/pyproject.toml
+++ b/examples/gemini/python/docs-agent/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "docs-agent"
-version = "0.3.0"
+version = "0.3.1"
 description = ""
 authors = ["Docs Agent contributors"]
 readme = "README.md"

From dbaa77d4b2ab831ce08014cfa9c566cf9e5a7986 Mon Sep 17 00:00:00 2001
From: Kyo Lee <kyol@google.com>
Date: Fri, 29 Mar 2024 20:10:25 +0000
Subject: [PATCH 03/12] [docs] Update the git clone instructions on Docs
 Agent's CLI setup guide.

---
 .../gemini/python/docs-agent/docs_agent/interfaces/README.md  | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/README.md b/examples/gemini/python/docs-agent/docs_agent/interfaces/README.md
index bd383e5ae..f5a445ec2 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/README.md
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/README.md
@@ -124,13 +124,13 @@ from your `$HOME` directory.
 1. Clone the following internal repo:
 
    ```posix-terminal
-   git clone sso://doc-llm-internal/docs-agent
+   git clone https://github.com/google/generative-ai-docs.git
    ```
 
 2. Go to the project directory:
 
    ```posix-terminal
-   cd docs-agent
+   cd generative-ai-docs/examples/gemini/python/docs-agent
    ```
 
 3. Install dependencies using `poetry`:

From 105b46bcef0de8a0c139bb7a1e661610d516236f Mon Sep 17 00:00:00 2001
From: Kyo Lee <kyol@google.com>
Date: Fri, 29 Mar 2024 20:24:54 +0000
Subject: [PATCH 04/12] [docs] Minor edits in Docs Agent's CLI setup guide.

---
 .../python/docs-agent/docs_agent/interfaces/README.md     | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/README.md b/examples/gemini/python/docs-agent/docs_agent/interfaces/README.md
index f5a445ec2..27e2ae9ae 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/README.md
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/README.md
@@ -116,18 +116,18 @@ credentials (via `gcloud`) stored on your host machine.
    (`application_default_credentials.json`) in the `$HOME/.config/gcloud/`
    directory of your host machine.
 
-## 4. Clone the Docs Agent project repository
+## 4. Clone the Docs Agent project
 
 **Note**: This guide assumes that you're creating a new project directory
 from your `$HOME` directory.
 
-1. Clone the following internal repo:
+1. Clone the following repo:
 
    ```posix-terminal
    git clone https://github.com/google/generative-ai-docs.git
    ```
 
-2. Go to the project directory:
+2. Go to the Docs Agent project directory:
 
    ```posix-terminal
    cd generative-ai-docs/examples/gemini/python/docs-agent
@@ -139,8 +139,6 @@ from your `$HOME` directory.
    poetry install
    ```
 
-   This may take some time to complete.
-
 ## 5. Set up an alias to the gemini command
 
 **Note**: If your Docs Agent project is not cloned in the `$HOME` directory,

From c8529d099db55f2dec238f0d403bfe08ce28a7bd Mon Sep 17 00:00:00 2001
From: Kyo Lee <kyol@google.com>
Date: Fri, 12 Apr 2024 19:05:24 +0000
Subject: [PATCH 05/12] [Docs Agent] Release of Docs Agent v 0.3.2.

What's change:

- Support the new Gemini 1.5 models in preview.
- Add new experimental CLI features to interact with Gemini models
  directly from a Linux terminal: `agent tellme` and `agent helpme`.
- Better handle uploading of text chunks using the Semantic Retrieval API.
- Add a new chat UI feature to provide a page for viewing logs.
- Update the Google Generative AI SDK version to `0.5.0`.
- Refactor the pre-preprocessing module (in progress).
- Remove unused code and type mismatch errors.
- Bug fixes.
---
 examples/gemini/python/docs-agent/README.md   |   64 +-
 .../apps_script/drive_to_markdown.gs          |   13 +-
 .../python/docs-agent/apps_script/exportmd.gs |    6 +-
 .../docs_agent/agents/docs_agent.py           |   77 +-
 .../docs_agent/interfaces/README.md           |  131 +-
 .../docs_agent/interfaces/chatbot/chatui.py   |   89 +-
 .../chatbot/static/css/style-chatui-1.5.css   |  556 +++++++
 .../chatbot/static/css/style-logs.css         |   22 +
 .../chatbot/templates/admin/logs.html         |   26 +
 .../chatbot/templates/chatui-1.5/base.html    |   27 +
 .../chatbot/templates/chatui-1.5/index.html   |   16 +
 .../chatbot/templates/chatui-1.5/result.html  |  161 ++
 .../chatbot/templates/chatui/result.html      |    2 +
 .../docs-agent/docs_agent/interfaces/cli.py   |  193 ++-
 .../docs_agent/interfaces/run_console.py      |  279 +++-
 .../docs-agent/docs_agent/memory/logging.py   |    7 +-
 .../docs_agent/models/google_genai.py         |   98 +-
 .../docs_agent/models/tokenCount.py           |   69 +-
 .../docs_agent/postprocess/docs_retriever.py  |   25 +-
 .../preprocess/files_to_plain_text.py         |  548 ++++---
 .../preprocess/populate_vector_database.py    |  440 +++---
 .../preprocess/splitters/fidl_splitter.py     |  109 +-
 .../preprocess/splitters/markdown_splitter.py |   75 +-
 .../docs-agent/docs_agent/storage/chroma.py   |   64 +-
 .../storage/google_semantic_retriever.py      |  128 +-
 .../docs-agent/docs_agent/utilities/config.py |   85 +-
 .../docs_agent/utilities/helpers.py           |    7 +-
 examples/gemini/python/docs-agent/helpme.sh   |   25 +
 examples/gemini/python/docs-agent/poetry.lock | 1353 +++++++++--------
 .../gemini/python/docs-agent/pyproject.toml   |    6 +-
 30 files changed, 3243 insertions(+), 1458 deletions(-)
 create mode 100644 examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui-1.5.css
 create mode 100644 examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-logs.css
 create mode 100644 examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/admin/logs.html
 create mode 100644 examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-1.5/base.html
 create mode 100644 examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-1.5/index.html
 create mode 100644 examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-1.5/result.html
 create mode 100755 examples/gemini/python/docs-agent/helpme.sh

diff --git a/examples/gemini/python/docs-agent/README.md b/examples/gemini/python/docs-agent/README.md
index 8b2feee0f..85d4ca610 100644
--- a/examples/gemini/python/docs-agent/README.md
+++ b/examples/gemini/python/docs-agent/README.md
@@ -70,6 +70,22 @@ The following list summarizes the tasks and features supported by Docs Agent:
 - **Run the Docs Agent CLI from anywhere in a terminal**: You can set up the
   Docs Agent CLI to ask questions to the Gemini model from anywhere in a terminal.
   For more information, see the [Set up Docs Agent CLI][cli-readme] page.
+- **Support the Gemini 1.5 models**: You can use the new Gemini 1.5 models,
+  `gemini-1.5-pro-latest` and `text-embedding-004`, with Docs Agent today.
+  For the moment, the following `config.yaml` setup is recommended:
+
+  ```
+  models:
+  - language_model: "models/aqa"
+    embedding_model: "models/text-embedding-004"
+    api_endpoint: "generativelanguage.googleapis.com"
+  ...
+  app_mode: "1.5"
+  db_type: "chroma"
+  ```
+
+  The setup above uses 3 Gemini models to their strength: AQA (`aqa`),
+  Gemini 1.0 Pro (`gemini-pro`), and Gemini 1.5 Pro (`gemini-1.5-pro-latest`).
 
 For more information on Docs Agent's architecture and features,
 see the [Docs Agent concepts][docs-agent-concepts] page.
@@ -113,27 +129,24 @@ Update your host machine's environment to prepare for the Docs Agent setup:
 2. Install the following dependencies:
 
    ```posix-terminal
-   sudo apt install git pip python3-venv
+   sudo apt install git pipx python3-venv
    ```
 
 3. Install `poetry`:
 
    ```posix-terminal
-   curl -sSL https://install.python-poetry.org | python3 -
+   pipx install poetry
    ```
 
-   **Important**: Make sure that `$HOME/.local/bin` is in your `PATH` variable
-   (for example, `export PATH=$PATH:~/.local/bin`).
-
-4. Set the following environment variable:
+4. To add `$HOME/.local/bin` to your `PATH` variable, run the following
+   command:
 
    ```posix-terminal
-   export PYTHON_KEYRING_BACKEND=keyring.backends.null.Keyring
+   pipx ensurepath
    ```
 
-   This is a [known issue][poetry-known-issue] in `poetry`.
-
-5. Set the Google API key as a environment variable:
+5. To set the Google API key as a environment variable, add the following
+   line to your `$HOME/.bashrc` file:
 
    ```
    export GOOGLE_API_KEY=<YOUR_API_KEY_HERE>
@@ -142,8 +155,11 @@ Update your host machine's environment to prepare for the Docs Agent setup:
    Replace `<YOUR_API_KEY_HERE>` with the API key to the
    [Gemini API][genai-doc-site].
 
-   **Tip**: To avoid repeating these `export` lines, add them to your
-   `$HOME/.bashrc` file.
+6. Update your environment:
+
+   ```posix-termainl
+   source ~/.bashrc
+   ```
 
 ### 3. (Optional) Authorize credentials for Docs Agent
 
@@ -256,7 +272,7 @@ Update settings in the Docs Agent project to use your custom dataset:
 
    ```
    inputs:
-     - path: "/usr/local/home/user01/website/src"
+     - path: "/usr/local/home/user01/website/src/content"
        url_prefix: "https://docs.flutter.dev"
    ```
 
@@ -265,30 +281,31 @@ Update settings in the Docs Agent project to use your custom dataset:
 
    ```
    inputs:
-     - path: "/usr/local/home/user01/website/src/ui"
+     - path: "/usr/local/home/user01/website/src/content/ui"
        url_prefix: "https://docs.flutter.dev/ui"
-     - path: "/usr/local/home/user01/website/src/tools"
+     - path: "/usr/local/home/user01/website/src/content/tools"
        url_prefix: "https://docs.flutter.dev/tools"
    ```
 
-6. (**Optional**) If you want to use the Gemini AQA model and populate a corpus online
-   via the [Semantic Retrieval API][semantic-api], use the following settings:
+6. If you want to use the `gemini-pro` model with a local vector database setup
+   (`chroma`), use the following settings:
 
    ```
    models:
-     - language_model: "models/aqa"
+     - language_model: "models/gemini-pro"
    ...
-   db_type: "google_semantic_retriever"
+   db_type: "chroma"
    ```
 
-   Or if you want to use the `gemini-pro` model with a local vector database setup
-   (`chroma`), use the following settings:
+   (**Optional**) Or if you want to use the Gemini AQA model and populate
+   a corpus online via the [Semantic Retrieval API][semantic-api], use the
+   following settings:
 
    ```
    models:
-     - language_model: "models/gemini-pro"
+     - language_model: "models/aqa"
    ...
-   db_type: "chroma"
+   db_type: "google_semantic_retriever"
    ```
 
 7. Save the `config.yaml` file and exit the text editor.
@@ -403,7 +420,6 @@ Meggin Kearney (`@Meggin`), and Kyo Lee (`@kyolee415`).
 [chroma-docs]: https://docs.trychroma.com/
 [flutter-docs-src]: https://github.com/flutter/website/tree/main/src
 [flutter-docs-site]: https://docs.flutter.dev/
-[poetry-known-issue]: https://github.com/python-poetry/poetry/issues/1917
 [apps-script-readme]: ./apps_script/README.md
 [scripts-readme]: ./docs_agent/preprocess/README.md
 [config-yaml]: config.yaml
diff --git a/examples/gemini/python/docs-agent/apps_script/drive_to_markdown.gs b/examples/gemini/python/docs-agent/apps_script/drive_to_markdown.gs
index 2b0ef9072..b6204fc5e 100644
--- a/examples/gemini/python/docs-agent/apps_script/drive_to_markdown.gs
+++ b/examples/gemini/python/docs-agent/apps_script/drive_to_markdown.gs
@@ -44,11 +44,19 @@ function convertDriveFolderToMDForDocsAgent(folderName) {
 
     while (myfiles.hasNext()) {
       var myfile = myfiles.next();
+      var ftype = myfile.getMimeType();
+      // If this is a shorcut, retrieve the target file
+      if (ftype == "application/vnd.google-apps.shortcut") {
+        var fid = myfile.getTargetId();
+        var myfile = DriveApp.getFileById(fid);
+        var ftype = myfile.getMimeType();
+      }
+      else{
+        var fid = myfile.getId();
+      }
       var fname = sanitizeFileName(myfile.getName());
       var fdate = myfile.getLastUpdated();
       var furl = myfile.getUrl();
-      var fid = myfile.getId();
-      var ftype = myfile.getMimeType();
       var fcreate = myfile.getDateCreated();
 
       //Function returns an array, assign each array value to seperate variables
@@ -58,7 +66,6 @@ function convertDriveFolderToMDForDocsAgent(folderName) {
         var md5_backup = backup_results[1];
         var mdoutput_backup_id = backup_results[2];
       }
-
       if (ftype == "application/vnd.google-apps.document") {
         Logger.log("File: " + fname + " is a Google doc.");
         let gdoc = DocumentApp.openById(fid);
diff --git a/examples/gemini/python/docs-agent/apps_script/exportmd.gs b/examples/gemini/python/docs-agent/apps_script/exportmd.gs
index 283872c79..40a4461ff 100644
--- a/examples/gemini/python/docs-agent/apps_script/exportmd.gs
+++ b/examples/gemini/python/docs-agent/apps_script/exportmd.gs
@@ -866,6 +866,8 @@ function processParagraph(index, element, inSrc, imageCounter, listCounters, ima
     } else if (t === DocumentApp.ElementType.FOOTNOTE) {
       textElements.push(' ('+element.getChild(i).getFootnoteContents().getText()+')');
     // Fixes for new elements
+    } else if (t === DocumentApp.ElementType.EQUATION) {
+      textElements.push(element.getChild(i).getText());
     } else if (t === DocumentApp.ElementType.DATE) {
       textElements.push(' ('+element.getChild(i)+')');
     } else if (t === DocumentApp.ElementType.RICH_LINK) {
@@ -875,8 +877,8 @@ function processParagraph(index, element, inSrc, imageCounter, listCounters, ima
     } else if (t === DocumentApp.ElementType.UNSUPPORTED) {
       textElements.push(' <UNSUPPORTED> ');
     } else {
-      throw "Paragraph "+index+" of type "+element.getType()+" has an unsupported child: "
-      +t+" "+(element.getChild(i)["getText"] ? element.getChild(i).getText():'')+" index="+index;
+      Logger.log("Paragraph "+index+" of type "+element.getType()+" has an unsupported child: "
+      +t+" "+(element.getChild(i)["getText"] ? element.getChild(i).getText():'')+" index="+index);
     }
   }
 
diff --git a/examples/gemini/python/docs-agent/docs_agent/agents/docs_agent.py b/examples/gemini/python/docs-agent/docs_agent/agents/docs_agent.py
index 4837dc2d7..27d59af1a 100644
--- a/examples/gemini/python/docs-agent/docs_agent/agents/docs_agent.py
+++ b/examples/gemini/python/docs-agent/docs_agent/agents/docs_agent.py
@@ -16,26 +16,21 @@
 
 """Docs Agent"""
 
-import os
-import sys
+import typing
 
 from absl import logging
 import google.api_core
 import google.ai.generativelanguage as glm
 from chromadb.utils import embedding_functions
 
-from docs_agent.storage.chroma import Chroma, Format, ChromaEnhanced
-from docs_agent.models.palm import PaLM
+from docs_agent.storage.chroma import ChromaEnhanced
 
 from docs_agent.models.google_genai import Gemini
 
-from docs_agent.utilities.config import ProductConfig, ReadConfig, Input, Models
-from docs_agent.models import tokenCount
+from docs_agent.utilities.config import ProductConfig, Models
 from docs_agent.preprocess.splitters import markdown_splitter
 
 from docs_agent.preprocess.splitters.markdown_splitter import Section as Section
-from docs_agent.utilities.helpers import get_project_path
-from docs_agent.postprocess.docs_retriever import FullPage as FullPage
 from docs_agent.postprocess.docs_retriever import SectionDistance as SectionDistance
 from docs_agent.postprocess.docs_retriever import (
     SectionProbability as SectionProbability,
@@ -49,6 +44,8 @@ class DocsAgent:
     def __init__(self, config: ProductConfig, init_chroma: bool = True):
         # Models settings
         self.config = config
+        self.embedding_model = str(self.config.models.embedding_model)
+        self.api_endpoint = str(self.config.models.api_endpoint)
         # Use the new chroma db for all queries
         # Should make a function for this or clean this behavior
         if init_chroma:
@@ -62,9 +59,9 @@ def __init__(self, config: ProductConfig, init_chroma: bool = True):
             )
             self.collection = self.chroma.get_collection(
                 self.collection_name,
-                embedding_model=self.config.models.embedding_model,
+                embedding_model=self.embedding_model,
                 embedding_function=embedding_function_gemini_retrieval(
-                    self.config.models.api_key
+                    self.config.models.api_key, self.embedding_model
                 ),
             )
         # AQA model settings
@@ -77,9 +74,12 @@ def __init__(self, config: ProductConfig, init_chroma: bool = True):
             self.context_model = "models/gemini-pro"
             gemini_model_config = Models(
                 language_model=self.context_model,
-                embedding_model="models/embedding-001",
+                embedding_model=self.embedding_model,
+                api_endpoint=self.api_endpoint,
+            )
+            self.gemini = Gemini(
+                models_config=gemini_model_config, conditions=config.conditions
             )
-            self.gemini = Gemini(models_config=gemini_model_config)
         # Semantic retriever
         if self.config.db_type == "google_semantic_retriever":
             for item in self.config.db_configs:
@@ -93,9 +93,34 @@ def __init__(self, config: ProductConfig, init_chroma: bool = True):
                         )
             self.aqa_response_buffer = ""
 
-        if self.config.models.language_model == "models/gemini-pro":
-            self.gemini = Gemini(models_config=config.models)
-            self.context_model = "models/gemini-pro"
+        if self.config.models.language_model.startswith("models/gemini"):
+            self.gemini = Gemini(
+                models_config=config.models, conditions=config.conditions
+            )
+            self.context_model = self.config.models.language_model
+
+        # Always initialize the gemini-pro model for other tasks.
+        gemini_pro_model_config = Models(
+            language_model="models/gemini-pro",
+            embedding_model=self.embedding_model,
+            api_endpoint=self.api_endpoint,
+        )
+        self.gemini_pro = Gemini(
+            models_config=gemini_pro_model_config, conditions=config.conditions
+        )
+
+        if self.config.app_mode == "1.5":
+            # Initialize the gemini-1.5.pro model for summarization.
+            gemini_15_model_config = Models(
+                language_model="models/gemini-1.5-pro-latest",
+                embedding_model=self.embedding_model,
+                api_endpoint=self.api_endpoint,
+            )
+            self.gemini_15 = Gemini(
+                models_config=gemini_15_model_config, conditions=config.conditions
+            )
+        else:
+            self.gemini_15 = self.gemini_pro
 
     # Use this method for talking to a Gemini content model
     def ask_content_model_with_context(self, context, question):
@@ -261,7 +286,7 @@ def ask_aqa_model_using_corpora(self, question, answer_style: str = "VERBOSE"):
 
     def ask_aqa_model(self, question):
         response = ""
-        if self.db_type == "ONLINE_STORAGE":
+        if self.config.db_type == "google_semantic_retriever":
             response = self.ask_aqa_model_using_corpora(question)
         else:
             response = self.ask_aqa_model_using_local_vector_store(question)
@@ -436,7 +461,11 @@ def query_vector_store_to_build(
     # If prompt is "fact_checker" it will use the fact_check_question from
     # config.yaml for the prompt
     def ask_content_model_with_context_prompt(
-        self, context: str, question: str, prompt: str = None
+        self,
+        context: str,
+        question: str,
+        prompt: typing.Optional[str] = None,
+        model: typing.Optional[str] = None,
     ):
         if prompt == None:
             prompt = self.config.conditions.condition_text
@@ -447,7 +476,13 @@ def ask_content_model_with_context_prompt(
         if self.config.log_level == "VERBOSE":
             self.print_the_prompt(new_prompt)
         try:
-            response = self.gemini.generate_content(contents=new_prompt)
+            response = ""
+            if model == "gemini-pro":
+                response = self.gemini_pro.generate_content(contents=new_prompt)
+            elif model == "gemini-1.5-pro":
+                response = self.gemini_15.generate_content(contents=new_prompt)
+            else:
+                response = self.gemini.generate_content(contents=new_prompt)
         except:
             return self.config.conditions.model_error_message, new_prompt
         for chunk in response:
@@ -475,7 +510,7 @@ def ask_content_model_to_use_file(self, prompt: str, file: str):
     # Use this method for asking a Gemini content model for fact-checking.
     # This uses ask_content_model_with_context_prompt w
     def ask_content_model_to_fact_check_prompt(self, context: str, prev_response: str):
-        question = self.fact_check_question + "\n\nText: "
+        question = self.config.conditions.fact_check_question + "\n\nText: "
         question += prev_response
         return self.ask_content_model_with_context_prompt(
             context=context, question=question, prompt=""
@@ -487,7 +522,7 @@ def generate_embedding(self, text, task_type: str = "SEMANTIC_SIMILARITY"):
 
 
 # Function to give an embedding function for gemini using an API key
-def embedding_function_gemini_retrieval(api_key):
+def embedding_function_gemini_retrieval(api_key, embedding_model: str):
     return embedding_functions.GoogleGenerativeAiEmbeddingFunction(
-        api_key=api_key, model_name="models/embedding-001", task_type="RETRIEVAL_QUERY"
+        api_key=api_key, model_name=embedding_model, task_type="RETRIEVAL_QUERY"
     )
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/README.md b/examples/gemini/python/docs-agent/docs_agent/interfaces/README.md
index 27e2ae9ae..54c31e86e 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/README.md
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/README.md
@@ -56,27 +56,24 @@ Setting up Docs Agent requires the following prerequisite items:
 2. Install the following dependencies:
 
    ```posix-terminal
-   sudo apt install git pip python3-venv
+   sudo apt install git pipx python3-venv
    ```
 
 3. Install `poetry`:
 
    ```posix-terminal
-   curl -sSL https://install.python-poetry.org | python3 -
+   pipx install poetry
    ```
 
-   **Important**: Make sure that `$HOME/.local/bin` is in your `PATH` variable
-   (for example, `export PATH=$PATH:~/.local/bin`).
-
-4. Set the following environment variable:
+4. To add `$HOME/.local/bin` to your `PATH` variable, run the following
+   command:
 
    ```posix-terminal
-   export PYTHON_KEYRING_BACKEND=keyring.backends.null.Keyring
+   pipx ensurepath
    ```
 
-   This is a [known issue][poetry-known-issue] in `poetry`.
-
-5. Set the Google API key as a environment variable:
+5. To set the Google API key as a environment variable, add the following
+   line to your `$HOME/.bashrc` file:
 
    ```
    export GOOGLE_API_KEY=<YOUR_API_KEY_HERE>
@@ -85,8 +82,11 @@ Setting up Docs Agent requires the following prerequisite items:
    Replace `<YOUR_API_KEY_HERE>` with the API key to the
    [Gemini API][genai-doc-site].
 
-   **Tip**: To avoid repeating these `export` lines, add them to your
-   `$HOME/.bashrc` file.
+6. Update your environment:
+
+   ```posix-termainl
+   source ~/.bashrc
+   ```
 
 ## 3. Authorize credentials for Docs Agent
 
@@ -197,6 +197,112 @@ from anywhere in the terminal:
    user@user01:~/temp$ gemini does flutter support material design 3?
    ```
 
+## Appendices
+
+### Set up your terminal to run the helpme command
+
+**Note**: This is an experimental setup.
+
+This new feature allows you to freely navigate a codebase setup in your
+terminal and asks Gemini to perform various tasks while automatically
+referencing the output you see in your terminal.
+
+Similar to the `agent tellme` command, the `agent helpme` command allows you to
+ask a question to Gemini directly from your terminal. However, unlike
+the `tellme` command, the `helpme` command uses the Gemini Pro model
+and doesn't depend on an online corpus to retrieve relevant context.
+Instead, this `helpme` setup can read directly from the output of your terminal
+(that is, the last 150 lines at the moment) and automatically adds it as context
+to your prompt.
+
+These tasks include, but not limited to:
+
+- Rewrite `README` file to be instructional and linear.
+- Rewrite `README` file to be more concise and better structured.
+- Format `README` to collect reference links at the bottom.
+- Write comments for a C++ source file.
+
+**Note**: Since this setup uses the Gemini Pro model, setting up OAuth on your
+host machine is **not required**.
+
+To set up this `helpme` command in your terminal, do the following:
+
+1. (**Optional**) Open the `helpme.sh` file using a text editor, for example:
+
+   ```sh
+   nano helpme.sh
+   ```
+
+   If necessary, adjust the path (`$HOME/docs-agent`) to match your
+   `docs-agent` project directory on the host machine:
+
+   ```
+   #!/bin/bash
+
+   # Check if the POETRY_ACTIVE environment variable is set
+   if [ -z "$POETRY_ACTIVE" ]; then
+       cd $HOME/docs-agent && poetry run agent helpme $@
+   else
+       agent helpme $@
+   fi
+   ```
+
+   Save the file and close text editor.
+
+2. Add the following `alias` lines to your `$HOME/.bashrc` file:
+
+   ```
+   alias gemini-pro='$HOME/docs-agent/helpme.sh'
+   alias start_agent='script -f -o 200MiB -O /tmp/docs_agent_console_input'
+   alias stop_agent='exit'
+   ```
+
+   Similarly, if necessary, you need to adjust the path
+  (`$HOME/docs-agent`) to match your the `docs-agent` project directory
+   on the host machine.
+
+3. Update your environment:
+
+   ```sh
+   source ~/.bashrc
+   ```
+
+4. When you are ready to let Docs Agent to read output from your terminal,
+   run the following command:
+
+   ```sh
+   start_agent
+   ```
+
+   **Note**: To stop this process, run `stop_agent`.
+
+5. Navigate to a directory in your terminal and use the `cat` command
+   (or `head` or `tail`) to print the content of a file to your terminal.
+
+   (In fact, you can run any command that prints output to the terminal.)
+
+   For example:
+
+   ```
+   user@user01:~/my-example-project$ cat test.cc
+   <prints the test.cc file here>
+   ```
+
+6. To use the latest output from your terminal, run the `gemini-pro` command
+   immediately after the output:
+
+   ```sh
+   gemini-pro <REQUEST>
+   ```
+
+   For example:
+
+   ```
+   user@user01:~/my-example-project$ cat test.cc
+   <prints the test.cc file here>
+   user@user01:~/my-example-project$ gemini-pro could you help me write comments for this C++ file above?
+   ```
+
 <!-- Reference links -->
 
 [gemini-aqa]: https://ai.google.dev/docs/semantic_retriever
@@ -205,5 +311,4 @@ from anywhere in the terminal:
 [google-cloud]: https://console.cloud.google.com/
 [oauth-client]: https://ai.google.dev/docs/oauth_quickstart#set-cloud
 [authorize-credentials]: https://ai.google.dev/docs/oauth_quickstart#authorize-credentials
-[poetry-known-issue]: https://github.com/python-poetry/poetry/issues/1917
 [genai-doc-site]: https://ai.google.dev/docs/gemini_api_overview
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/chatui.py b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/chatui.py
index 3e0b351ed..1f0955d18 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/chatui.py
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/chatui.py
@@ -19,11 +19,10 @@
 from flask import Blueprint, render_template, request, redirect, url_for, json, jsonify
 import markdown
 import markdown.extensions.fenced_code
-from bs4 import BeautifulSoup
 import urllib
 import os
+import typing
 from datetime import datetime
-from pytz import timezone
 from absl import logging
 import pytz
 import uuid
@@ -45,13 +44,17 @@
 
 
 # This is used to define the app blueprint using a productConfig
-def construct_blueprint(product_config: config.ProductConfig, app_mode: str = None):
+def construct_blueprint(
+    product_config: config.ProductConfig, app_mode: typing.Optional[str] = None
+):
     bp = Blueprint("chatui", __name__)
     if product_config.db_type == "google_semantic_retriever":
         docs_agent = DocsAgent(config=product_config, init_chroma=False)
     else:
         docs_agent = DocsAgent(config=product_config)
-    logging.info(f"Launching the Flask app for product: {product_config.product_name} with app_mode: {app_mode}")
+    logging.info(
+        f"Launching the Flask app for product: {product_config.product_name} with app_mode: {app_mode}"
+    )
     # Assign templates and redirects
     if app_mode == "web":
         app_template = "chatui/index.html"
@@ -62,6 +65,9 @@ def construct_blueprint(product_config: config.ProductConfig, app_mode: str = No
     elif app_mode == "widget":
         app_template = "chat-widget/index.html"
         redirect_index = "chat-widget.index"
+    elif app_mode == "1.5":
+        app_template = "chatui-1.5/index.html"
+        redirect_index = "chatui-1.5.index"
     else:
         app_template = "chatui/index.html"
         redirect_index = "chatui.index"
@@ -84,16 +90,14 @@ def api():
                     full_prompt,
                     response,
                     context,
-                    sources_ref,
-                    plain_token,
+                    search_result,
                 ) = ask_model_with_sources(input["question"], agent=docs_agent)
                 source_array = []
-                for source in sources_ref:
-                    source_array.append(source.returnDictionary())
+                # for source in search_result:
+                #     source_array.append(source.returnDictionary())
                 dictionary = {
                     "response": response,
                     "full_prompt": full_prompt,
-                    "token_count_context": plain_token,
                     "sources": source_array,
                 }
                 return jsonify(dictionary)
@@ -130,7 +134,7 @@ def rewrite():
             rewrite_captured = json_data.get("rewrite")
             date_format = "%m%d%Y-%H%M%S"
             date = datetime.now(tz=pytz.utc)
-            date = date.astimezone(timezone("US/Pacific"))
+            date = date.astimezone(pytz.timezone("US/Pacific"))
             print(
                 "[" + date.strftime(date_format) + "] A user has submitted a rewrite."
             )
@@ -184,6 +188,11 @@ def question(ask):
         else:
             return redirect(url_for(redirect_index))
 
+    # Render the log view page
+    @bp.route("/logs", methods=["GET", "POST"])
+    def logs():
+        return show_logs(agent=docs_agent)
+
     return bp
 
 
@@ -258,7 +267,10 @@ def ask_model(question, agent, template: str = "chatui/index.html"):
         related_questions_response,
         new_prompt_questions,
     ) = docs_agent.ask_content_model_with_context_prompt(
-        context=final_context, question=new_question, prompt=new_condition
+        context=final_context,
+        question=new_question,
+        prompt=new_condition,
+        model="gemini-pro",
     )
     # Clean up the response to a proper html list
     related_questions = parse_related_questions_response_to_html_list(
@@ -278,8 +290,27 @@ def ask_model(question, agent, template: str = "chatui/index.html"):
             probability = aqa_response.answerable_probability
         except:
             probability = 0.0
+
+    ### The code below is added for the new Gemini 1.5 model.
+    # Ask the Gemini 1.5 model to generate a full summary.
+    if docs_agent.config.app_mode == "1.5":
+        new_condition = f"Read the context below and provide a detailed overview to address the question at the end:"
+        (
+            summary_response,
+            summary_prompt,
+        ) = docs_agent.ask_content_model_with_context_prompt(
+            context=final_context,
+            question=question,
+            prompt=new_condition,
+            model="gemini-1.5-pro",
+        )
+        log_lines = f"{response}\n\nGemini 1.5 response:\n\n{summary_response}"
+    else:
+        summary_response = ""
+        log_lines = f"{response}"
+
     ### LOG THIS REQUEST.
-    log_question(new_uuid, question, response, probability)
+    log_question(new_uuid, question, log_lines, probability)
 
     return render_template(
         template,
@@ -295,6 +326,7 @@ def ask_model(question, agent, template: str = "chatui/index.html"):
         md_to_html=md_to_html,
         final_context=final_context,
         search_result=search_result,
+        summary_response=summary_response,
     )
 
 
@@ -304,7 +336,7 @@ def ask_model(question, agent, template: str = "chatui/index.html"):
 def ask_model_with_sources(question, agent):
     docs_agent = agent
     full_prompt = ""
-    context, plain_token, sources_ref = docs_agent.query_vector_store_to_build(
+    search_result, context = docs_agent.query_vector_store_to_build(
         question=question, token_limit=30000, results_num=10, max_sources=10
     )
     context_with_instruction = docs_agent.add_instruction_to_context(context)
@@ -317,4 +349,33 @@ def ask_model_with_sources(question, agent):
             context_with_instruction, question
         )
 
-    return full_prompt, response, context, sources_ref, plain_token
+    return full_prompt, response, context, search_result
+
+
+# Display a page showing logs
+def show_logs(agent, template: str = "admin/logs.html"):
+    docs_agent = agent
+    product = docs_agent.config.product_name
+    log_filename = "chatui_logs.txt"
+    answerable_log_filename = "answerable_logs.txt"
+    log_contents = ""
+    answerable_contents = ""
+    if docs_agent.config.enable_show_logs == "True":
+        try:
+            with open(log_filename, "r", encoding="utf-8") as file:
+                log_contents = file.read()
+        except:
+            log_contents = "Cannot find or open log files."
+        try:
+            with open(answerable_log_filename, "r", encoding="utf-8") as file:
+                answerable_contents = file.read()
+        except:
+            answerable_contents = (
+                "Cannot find or open a file that contains answerable scores."
+            )
+    return render_template(
+        template,
+        product=product,
+        logs=log_contents,
+        answerable_logs=answerable_contents,
+    )
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui-1.5.css b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui-1.5.css
new file mode 100644
index 000000000..4fd25c627
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui-1.5.css
@@ -0,0 +1,556 @@
+/**
+ * Copyright 2023 Google LLC
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ======= General style for HTML elements ======= */
+
+body {
+    font: 16px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
+    color: #333;
+    font-weight: 300;
+    max-width: 960px;
+    margin: auto;
+    background-color: #d9d9d9;
+    padding-top: 15px;
+    padding-bottom: 15px;
+  }
+
+  a {
+    color: #0a619a;
+  }
+
+  p {
+    margin: 0 0 1em;
+    line-height: 130%;
+  }
+
+  h1 {
+    margin: 0 0 0.5em;
+    font-weight: 500;
+    font-size: 1.3em;
+    margin-left: 1.0em;
+    margin-top: 0.3em;
+  }
+
+  h2 {
+    margin: 0;
+    margin-top: 17px;
+    margin-bottom: 15px;
+    font-weight: normal;
+    font-size: 1.5em;
+  }
+
+  h3 {
+    margin: 0;
+    margin-top: 10px;
+    margin-bottom: 10px;
+  }
+
+  h4 {
+    color: #505050;
+    margin-top: 3px;
+    margin-left: 5px;
+    margin-bottom: 8px;
+  }
+
+  li {
+    margin: 0 0 0.3em;
+  }
+
+  code {
+    font-family: math;
+    color: darkgreen;
+    text-wrap: pretty;
+  }
+
+  /* ======= Style layout by ID ======= */
+
+  #callout-box {
+    margin: auto;
+    max-width: 800px;
+    font: 13px arial, sans-serif;
+    background-color: white;
+    border-style: solid;
+    border-width: 1px;
+    padding: 10px 25px;
+    box-shadow: 5px 5px 5px grey;
+    border-radius: 15px;
+  }
+
+  #important-box {
+    font-size: 0.9em;
+    font-family: system-ui;
+    line-height: 150%;
+    word-break: break-word;
+    #padding: 4px;
+    padding-top: 5px;
+    padding-bottom: 5px;
+    padding-left: 10px;
+    padding-right: 10px;
+    background-color: #fcb8a1;
+    border-radius: 5px;
+    border-width: 2px;
+    border-style: solid;
+  }
+
+  #tldr-response-box {
+    font-size: 0.8em;
+    font-family: sans-serif;
+    line-height: 140%;
+    margin-top: 10px;
+    margin-bottom: 25px;
+    border-width: 2px;
+    border-style: solid;
+    padding-top: 5px;
+    padding-bottom: 5px;
+    padding-left: 10px;
+    padding-right: 10px;
+    background-color: #b1d8f1;
+    border-radius: 5px;
+    border-width: 2px;
+    border-style: solid;
+  }
+
+  #response-box {
+    font-size: 1.0em;
+    font-family: sans-serif;
+    line-height: 140%;
+    margin-top: 10px;
+  }
+  #suggested-questions {
+    font-family: sans-serif;
+    word-break: break-word;
+  }
+
+  #context-content{
+    background: #d7dbd7;
+    font-family: sans-serif;
+    word-break: break-all;
+  }
+
+  #context-pre {
+    font-size: small;
+    font-family: monospace;
+    text-wrap: pretty;
+    margin-top: 0.3px;
+  }
+
+  #probability-box {
+    font-size: small;
+    padding: 4px;
+    margin-bottom: 10px;
+  }
+
+  #grounding-box {
+    font-size: small;
+    padding: 4px;
+    word-break: break-all;
+  }
+
+  #grounding-pre {
+    font-size: small;
+    font-family: monospace;
+    text-wrap: pretty;
+  }
+
+  #reference-box {
+    font-size: 0.9em;
+    font-family: system-ui;
+    text-wrap: pretty;
+    word-break: break-all;
+    margin-bottom: 12px;
+    line-height: 1.5em;
+  }
+
+  #reference-box-no-aqa {
+    font-size: 0.9em;
+    font-family: system-ui;
+    text-wrap: pretty;
+    word-break: break-all;
+    line-height: 1.5em;
+  }
+
+  #aqa-content{
+    background: #9fc7db;
+    font-family: math;
+  }
+
+  #aqa-label{
+    background: #49a5d2;
+  }
+
+  #aqa-json {
+    font-family: system-ui;
+    font-size: small;
+    text-wrap: pretty;
+    word-break: break-all;
+    margin: 0;
+  }
+
+  #rewrite-buttons-box {
+    margin-top: 12px;
+  }
+
+  #answerable-span {
+    font-size: small;
+    font-family: system-ui;
+    float: right;
+    padding: 10px;
+  }
+
+  /* ======= Style by class ======= */
+
+  .hidden {
+    display: none;
+  }
+
+  .disable {
+    display: none;
+  }
+
+  .header-wrapper {
+    display: flex;
+  }
+
+  .loading {
+    font: 15px arial, sans-serif;
+    width: 100%;
+    margin-left: 12px;
+    color: #505050;
+    padding: 2px;
+  }
+
+  .notselected {
+    background-color: #303936e6;
+    padding-top: 3px;
+    padding-bottom: 5px;
+  }
+
+  .notselected:hover {
+    background-color: #121a17e6;
+    cursor:pointer;
+  }
+
+  .selected {
+    background-color: #1e6a9c;
+    padding-top: 7px;
+    padding-bottom: 7px;
+  }
+
+  .selected:hover {
+    background-color: #0a619a;
+    cursor:pointer;
+  }
+
+  .rewrite {
+    padding: 15px;
+    border: 2px solid #000;
+    margin-top: 6px;
+    border-radius: 15px;
+  }
+
+  .question, .response, .response-text, .fact-checked-text {
+    max-width: 700px;
+    margin-left: 3px;
+  }
+
+  .full-response {
+    max-width: 700px;
+    margin-left: 10px;
+  }
+
+  .related-questions {
+    margin-bottom: 20px;
+    font-size: 0.9em;
+    line-height: 140%;
+  }
+
+  /* ======= Style buttons by ID ======= */
+
+  #rewrite-button {
+    border: 0;
+    background-color: #cf633ff2;
+    color: #fff;
+    padding: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+    margin-top: 0.3em;
+  }
+
+  #rewrite-button:hover {
+    background: #ce3705f2;
+    cursor:pointer;
+  }
+
+  #like-button {
+    border: 0;
+    color: #fff;
+    padding-left: 7px;
+    padding-right: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+  }
+
+  #submit-button {
+    border: 0;
+    background: none;
+    background-color: #CF5C3F;
+    color: #fff;
+    padding: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+  }
+
+  #submit-button:hover {
+    background: #ce3705f2;
+    cursor:pointer;
+  }
+
+  #submit-result {
+    color: #027f02d6;
+    font-family: fantasy;
+  }
+
+  #edit-text-area {
+    font: 13px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
+    max-height: 500px;
+    max-width: -webkit-fill-available;
+    height: 300px;
+    width: 650px;
+    padding: 8px;
+  }
+
+  #rewrite-question-header {
+    margin: 0;
+    margin-bottom: 5px;
+  }
+
+  #rewrite-response-header {
+    margin: 0;
+    margin-top: 10px;
+    margin-bottom: 5px;
+  }
+
+  #user-id {
+    margin: 0;
+    margin-top: 10px;
+    margin-bottom: 15px;
+  }
+
+  #fact-check-url {
+    margin: 0 0 0.7em;
+  }
+
+  #source-para {
+    margin: 0 0 0.7em;
+  }
+
+  #distance-para {
+    margin: 0 0 0.7em;
+    font: 11px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
+  }
+  /* ======= Search Box ======= */
+
+  .search {
+    border: 2px solid #CF5C3F;
+    overflow: auto;
+    max-width: 700px;
+    margin-top: 15px;
+    margin-left: 10px;
+    margin-bottom: 10px;
+    border-radius: 5px;
+  }
+
+  .search input[type="text"] {
+    border: 0;
+    width: calc(100% - 65px);
+    padding: 10px;
+  }
+
+  .search input[type="text"]:focus {
+    outline: 0;
+  }
+
+  .search input[type="submit"] {
+    border: 0;
+    background: none;
+    background-color: #CF5C3F;
+    color: #fff;
+    float: right;
+    padding: 10px;
+    -moz-border-radius-top-right: 5px;
+    -webkit-border-radius-top-right: 5px;
+    -moz-border-radius-bottom-right: 5px;
+    -webkit-border-radius-bottom-right: 5px;
+    cursor:pointer;
+  }
+
+  /* ======= Accordion ======= */
+
+  .accordion {
+    max-width: 65em;
+    #margin-bottom: 1em;
+  }
+
+  .accordion > input[type="checkbox"] {
+    position: absolute;
+    left: -100vw;
+  }
+
+  .accordion .content {
+    overflow-y: hidden;
+    height: 0;
+    transition: height 0.3s ease;
+  }
+
+  .accordion .reference-content {
+    font-size: 15px;
+    font-family: serif;
+  }
+
+  .accordion > input[type="checkbox"]:checked ~ .content {
+    height: auto;
+    overflow: visible;
+    padding: 15px;
+    border: 2px solid #000;
+    margin-top: 6px;
+    border-radius: 15px;
+  }
+
+  .accordion .handle {
+    margin: 0;
+    font-size: 1.0em;
+  }
+
+  .accordion label {
+    display: block;
+    font-weight: normal;
+    border: 2px solid #000;
+    #padding: 12px;
+    background: #4490b8ab;
+    #border-radius: 15px;
+    padding: 5px;
+    #background: #027f023b;
+    border-radius: 10px;
+  }
+
+  .accordion label:hover,
+  .accordion label:focus {
+    background: #d9d9d9;
+    cursor:pointer;
+  }
+
+  .accordion .handle label::before {
+    font-family: fontawesome, sans-serif;
+    display: inline-block;
+    content: "\2964";
+    margin-right: 10px;
+    font-size: .58em;
+    line-height: 1.556em;
+    vertical-align: middle;
+  }
+
+  .accordion > input[type="checkbox"]:checked ~ .handle label::before {
+    content: "\2965";
+  }
+
+  .accordion p:last-child {
+    margin-bottom: 0;
+  }
+
+  /* ======= Accordion Source ======= */
+
+  .accordion-source {
+    max-width: 65em;
+    margin-bottom: 1em;
+  }
+
+  .accordion-source > input[type="checkbox"] {
+    position: absolute;
+    left: -100vw;
+  }
+
+  .accordion-source .content {
+    overflow-y: hidden;
+    height: 0;
+    transition: height 0.3s ease;
+  }
+
+  .accordion-source .content{
+    font-size: 13px;
+  }
+
+  .accordion-source > input[type="checkbox"]:checked ~ .content {
+    height: auto;
+    overflow: visible;
+    padding: 15px;
+    border: 2px solid #000;
+    margin-top: 6px;
+    border-radius: 15px;
+  }
+
+  .accordion-source .handle {
+    margin: 0;
+    font-size: 1em;
+    line-height: 1.2em;
+  }
+
+  .accordion-source label {
+    display: block;
+    font-weight: normal;
+    border: 1px solid #000;
+    padding: 6px;
+    background: #4490b8ab;
+    border-radius: 15px;
+  }
+
+  .accordion-source label:hover,
+  .accordion-source label:focus {
+    background: #d9d9d9;
+    cursor:pointer;
+  }
+
+  .accordion-source .handle label::before {
+    font-family: fontawesome, sans-serif;
+    display: inline-block;
+    content: "\2964";
+    margin-right: 10px;
+    font-size: .58em;
+    line-height: .556em;
+    vertical-align: middle;
+  }
+
+  .accordion-source > input[type="checkbox"]:checked ~ .handle label::before {
+    content: "\2965";
+  }
+
+  .accordion-source p:last-child {
+    margin-bottom: 0;
+  }
+
+/* Loader animation */
+/* Source: https://css-loaders.com/classic/ */
+.loader {
+  width: fit-content;
+  font-family: monospace;
+  font-size: 14px;
+  margin-left: 13px;
+  clip-path: inset(0 3ch 0 0);
+  animation: animation 1s steps(4) infinite;
+}
+.loader:before {
+  content:"Generating a response..."
+}
+@keyframes animation {to{clip-path: inset(0 -1ch 0 0)}}
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-logs.css b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-logs.css
new file mode 100644
index 000000000..e0e884c0a
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-logs.css
@@ -0,0 +1,22 @@
+/**
+ * Copyright 2023 Google LLC
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+.log-pre {
+  font-size: small;
+  font-family: monospace;
+  text-wrap: pretty;
+  word-break: break-all;
+}
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/admin/logs.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/admin/logs.html
new file mode 100644
index 000000000..7d1323833
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/admin/logs.html
@@ -0,0 +1,26 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <link rel="stylesheet" href="{{ url_for('static', filename='css/style-widget.css') }}">
+    <link rel="stylesheet" href="{{ url_for('static', filename='css/chatbox.css') }}">
+    <link rel="stylesheet" href="{{ url_for('static', filename='css/style-logs.css') }}">
+    <link rel="shortcut icon" href="{{ url_for('static', filename='images/favicon.png') }}">
+    <title>{{ product }} Docs Agent admin page</title>
+</head>
+<body>
+  {% if logs %}
+  <div id='widget-box'>
+    <div name="title"><h1>{{ product }} Docs Agent</h1></div>
+    <div id='chat-border'>
+      <div class="body-content">
+	<h2>Answerable scores<h2>
+        <pre class="log-pre">{{ answerable_logs }}</pre>
+	<h2>Logs<h2>
+	<pre class="log-pre">{{ logs }}</pre>
+      </div>
+    </div>
+  </div>
+  {% endif %}
+</body>
+</html>
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-1.5/base.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-1.5/base.html
new file mode 100644
index 000000000..51ae74ed9
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-1.5/base.html
@@ -0,0 +1,27 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <link rel="stylesheet" href="{{ url_for('static', filename='css/style-chatui-1.5.css') }}">
+    <link rel="stylesheet" href="{{ url_for('static', filename='css/chatbox.css') }}">
+    <link rel="shortcut icon" href="{{ url_for('static', filename='images/favicon.png') }}">
+    <title>{{ product }} ChatBot</title>
+</head>
+<body>
+  <div id='body-box'>
+    <div name="title"><h1>{{ product }} Docs Agent 1.5</h1></div>
+    <div id='chat-border'>
+      <div class="body-content">
+        {% block content %} {% endblock %}
+      </div>
+    </div>
+  </div>
+  <br>
+  <br>
+  <script>
+    let urlRewrite = "{{ server_url }}rewrite";
+    let urlLike = "{{ server_url }}like";
+  </script>
+  <script src="{{ url_for('static', filename='javascript/app.js') }}"></script>
+</body>
+</html>
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-1.5/index.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-1.5/index.html
new file mode 100644
index 000000000..0a08dc99e
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-1.5/index.html
@@ -0,0 +1,16 @@
+{% extends 'chatui-1.5/base.html' %}
+
+{% block content %}
+  <div class="search">
+    <form class="search-form" method="POST" action="/result">
+      <input type="text" name="question" placeholder="Ask me about topics related to {{product}}.">
+      <input type="submit" value="Ask" id="ask-button">
+    </form>
+  </div>
+  <div class="loader hidden" id="loading-div"></div>
+  {% if response %}
+  <div class="full-response">
+    {% include 'chatui-1.5/result.html' %}
+  </div>
+  {% endif %}
+{% endblock %}
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-1.5/result.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-1.5/result.html
new file mode 100644
index 000000000..c2108e930
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-1.5/result.html
@@ -0,0 +1,161 @@
+<!--Sets defaults-->
+{% if search_result[0] %}
+  {% set check_url = search_result[0].section.url %}
+  {% set probability = search_result[0].probability %}
+{% else %}
+  {% set check_url = "None" %}
+  {% set probability = "0.0" %}
+{% endif %}
+
+{% if aqa_response_in_html != "" %}
+  <!--Gemini AQA template-->
+  <div class="question">
+    <h2>{{ question | replace("+", " ") | replace("%3F", "?")}}</h2>
+  </div>
+  {% if probability|float > 0.7 %}
+  <div class="important-box" id="important-box">
+    <b>Important:</b> The answer below is generated by Gemini 1.5.
+    To verify this answer, please visit: {{named_link_html(check_url, check_url) | safe}}
+  </div>
+  <div class="response-text" id="tldr-response-box">
+    <h3>TL;DR</h3>
+    <span id="gemini-response">
+      {{ md_to_html(response) | safe }}
+    </span>
+  </div>
+  <div class="response-text" id="response-box">
+    <span id="gemini-response">
+      {{ md_to_html(summary_response) | safe }}
+    </span>
+  </div>
+  <div class="related-questions">
+    <h3>Suggested follow-up questions</h3>
+    <span id="suggested-questions">
+      {{ related_questions | safe }}
+    </span>
+  </div>
+  <section class="accordion">
+    <input type="checkbox" name="collapse" id="handle1">
+    <h2 class="handle">
+      <label for="handle1">Context</label>
+    </h2>
+    <div class="content" id="context-content">
+      <div id="probability-box">
+        <b>Answerable probability</b>: {{"%.6f"|format(probability|float)}}
+      </div>
+      <div id="grounding-box">
+        <b>Grounding attributions</b>:
+	<pre id="grounding-pre">{{ final_context | safe }}</pre>
+      </div>
+      <div id="reference-box">
+      {% for source in search_result %}
+        {% set section_url = named_link_html(source.section.url, source.section.url) %}
+        <b>[{{loop.index}}]</b>{{ section_url | safe}}
+        <br>
+      {% endfor %}
+      </div>
+      <div class="hidden" id="aqa-box">
+        <section class="accordion">
+          <input type="checkbox" name="collapse" id="handle2">
+          <h2 class="handle">
+            <label for="handle2" id="aqa-label">AQA model's JSON response</label>
+          </h2>
+          <div class="content" id="aqa-content">
+	    <pre id="aqa-json">{{ aqa_response_in_html | safe }}</pre>
+          </div>
+        </section>
+      </div>
+    </div>
+  </section>
+  {% else %}
+  <div class="response-text" id="response-box">
+    <span id="gemini-response">
+      I'm sorry, but I cannot answer this question at the moment.
+      <b>The answer to your question does not seem to be available in documentation.</b><br>
+      <br>
+      However, based on the documentation available in the corpus, you could ask questions like:
+    </span>
+  </div>
+  <div class="related-questions">
+    <!--<h4>Suggested questions</h4>-->
+    <span id="suggested-questions">
+      {{ related_questions | safe }}
+    </span>
+  </div>
+  {% endif %}
+{% else %}
+  <!--Gemini Pro template-->
+  <div class="question">
+    <h2>{{ question | replace("+", " ") | replace("%3F", "?")}}</h2>
+  </div>
+  <div class="important-box" id="important-box">
+    <!--Uses the URL from the first result-->
+    <b>Important:</b> The answer below is generated by the Gemini 1.5 model.
+    To verify this answer, please visit: {{named_link_html(check_url, check_url) | safe}}
+  </div>
+  <div class="response-text" id="response-box">
+    <span id="gemini-response">
+      {{ md_to_html(response) | safe }}
+    </span>
+  </div>
+  <div class="related-questions">
+    <h4>Suggested questions</h4>
+    <span id="suggested-questions">
+      {{ related_questions | safe }}
+    <span>
+  </div>
+  <section class="accordion">
+    <input type="checkbox" name="collapse" id="handle1">
+    <h2 class="handle">
+      <label for="handle1">Context</label>
+    </h2>
+    <div class="content" id="context-content">
+      <!-- {{ md_to_html(final_context) | safe }} -->
+      {% for source in search_result %}
+      <pre id="context-pre">{{md_to_html(source.section.content) | safe}}Reference <b>[{{loop.index}}]</b></pre>
+      {% endfor %}
+      <div id="reference-box-no-aqa">
+      {% for source in search_result %}
+        {% set section_url = named_link_html(source.section.url, source.section.url) %}
+        <b>[{{loop.index}}]</b>{{ section_url | safe}} ({{ source.distance }})
+        <br>
+      {% endfor %}
+      </div>
+    </div>
+  </section>
+{% endif %}
+
+<!--Common like/rewrite-->
+<div id="rewrite-buttons-box">
+  <input type="button" value="Rewrite this response" id="rewrite-button">
+  <input type="button" class="notselected" value="Like this response &#128077;" id="like-button">
+  {% if aqa_response_in_html != "" %}
+  <span id="answerable-span">
+    (<b>Answerable probability</b>: {{"%.3f"|format(probability|float)}})
+  </span>
+  {% endif %}
+</div>
+<div class="rewrite hidden" id="rewrite-box">
+  <h4 id="rewrite-question-header">Question:</h4>
+  <span id="rewrite-question-span">
+    <p>{{ question | replace("+", " ") | replace("%3F", "?")}}</p>
+  </span>
+  <h4 id="rewrite-response-header">Gemini's response:</h4>
+  <span id="rewrite-original-response-span">
+    {{ md_to_html(response) | safe }}
+  </span>
+  <h4>Rewrite:</h4>
+  <textarea id="edit-text-area">{{ response | safe }}</textarea>
+  <br>
+  <label for="user-id">User ID:</label>
+  <input type="text" id="user-id" name="user-id" placeholder="Optional">
+  <br>
+  <span id="submit-box">
+    <input type="button" value="Submit" id="submit-button">
+  </span>
+  <span id="submit-result">
+  </span>
+</div>
+<div class="hidden" id="uuid-box">
+  {{ uuid | safe }}
+</div>
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui/result.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui/result.html
index 4bc14b304..ea47264e2 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui/result.html
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui/result.html
@@ -106,9 +106,11 @@ <h2 class="handle">
 <div id="rewrite-buttons-box">
   <input type="button" value="Rewrite this response" id="rewrite-button">
   <input type="button" class="notselected" value="Like this response &#128077;" id="like-button">
+  {% if aqa_response_in_html != "" %}
   <span id="answerable-span">
     (<b>Answerable probability</b>: {{"%.3f"|format(probability|float)}})
   </span>
+  {% endif %}
 </div>
 <div class="rewrite hidden" id="rewrite-box">
   <h4 id="rewrite-question-header">Question:</h4>
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/cli.py b/examples/gemini/python/docs-agent/docs_agent/interfaces/cli.py
index 99fefa14c..fcb1ee0db 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/cli.py
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/cli.py
@@ -18,6 +18,7 @@
 
 import click
 import sys
+import typing
 from functools import wraps
 from docs_agent.utilities import config
 from docs_agent.utilities.config import ReadDbConfigs, ConfigFile
@@ -36,6 +37,9 @@
 from docs_agent.storage.google_semantic_retriever import SemanticRetriever
 from docs_agent.storage.chroma import ChromaEnhanced
 import socket
+import os
+import string
+import re
 
 
 def common_options(func):
@@ -60,7 +64,7 @@ def wrapper(*args, **kwargs):
 @click.group(invoke_without_command=True)
 @common_options
 @click.pass_context
-def cli(ctx, config_file, product):
+def cli_admin(ctx, config_file, product):
     """With Docs Agent, you can populate vector databases,
     manage online corpora, and interact with Google's Gemini
     models."""
@@ -71,9 +75,28 @@ def cli(ctx, config_file, product):
         show_config()
 
 
-@cli.command()
+@click.group(invoke_without_command=True)
+@common_options
+@click.pass_context
+def cli_client(ctx, config_file, product):
+    """With Docs Agent, you can interact with Google's Gemini
+    models as well as content stored in Google Semantic Retriever Corpora."""
+    ctx.ensure_object(dict)
+    # Print config.yaml if agent is run without a command.
+    if ctx.invoked_subcommand is None:
+        click.echo("Docs Agent configuration:\n")
+        show_config()
+
+
+cli = click.CommandCollection(
+    sources=[cli_admin, cli_client],
+    help="With Docs Agent, you can populate vector databases, manage online corpora, and interact with Google's Gemini models.",
+)
+
+
+@cli_admin.command()
 @common_options
-def chunk(config_file: str = None, product: list[str] = [""]):
+def chunk(config_file: typing.Optional [str], product: list[str] = [""]):
     """Convert files to plain text chunks."""
     loaded_config, product_config = return_config_and_product(
         config_file=config_file, product=product
@@ -82,9 +105,9 @@ def chunk(config_file: str = None, product: list[str] = [""]):
     click.echo("The files are successfully converted into text chunks.")
 
 
-@cli.command()
+@cli_admin.command()
 @common_options
-def populate(config_file: str = None, product: list[str] = [""]):
+def populate(config_file: typing.Optional [str], product: list[str] = [""]):
     """Populate a vector database using text chunks."""
     # Loads configurations from common options
     loaded_config, product_config = return_config_and_product(
@@ -96,18 +119,24 @@ def populate(config_file: str = None, product: list[str] = [""]):
         click.echo(f"The text chunks are successfully added to {item.db_type}.")
 
 
-@cli.command()
+@cli_admin.command()
 @click.option("--hostname", default=socket.gethostname(), show_default=True)
 @click.option("--port", default=5000, show_default=True, type=int)
 @click.option("--debug", default=True, show_default=True, type=bool)
-@click.option("--app_mode", default=None, show_default=True, type=click.Choice(["web", "widget", "experimental"], case_sensitive=False), help="Specify a mode for the chatbot.")
+@click.option(
+    "--app_mode",
+    default=None,
+    show_default=True,
+    type=click.Choice(["web", "widget", "experimental"], case_sensitive=False),
+    help="Specify a mode for the chatbot.",
+)
 @common_options
 def chatbot(
     hostname: str,
     port: str,
     debug: str,
     app_mode: str,
-    config_file: str = None,
+    config_file: typing.Optional [str],
     product: list[str] = [""],
 ):
     """Launch the Flask-based chatbot app."""
@@ -120,13 +149,15 @@ def chatbot(
     if app_mode == None:
         app_mode = product_file.app_mode
     app = chatbot_flask.create_app(product=product_file, app_mode=app_mode)
-    click.echo(f"Launching the chatbot UI for product {product_file.product_name} in {app_mode} mode.")
+    click.echo(
+        f"Launching the chatbot UI for product {product_file.product_name} in {app_mode} mode."
+    )
     app.run(host=hostname, port=port, debug=debug)
 
 
-@cli.command()
+@cli_admin.command()
 @common_options
-def show_config(config_file: str = None, product: list[str] = [""]):
+def show_config(config_file: typing.Optional [str], product: list[str] = [""]):
     """Print the Docs Agent configuration."""
     # Loads configurations from common options
     loaded_config, product_config = return_config_and_product(
@@ -137,10 +168,10 @@ def show_config(config_file: str = None, product: list[str] = [""]):
         print(item)
 
 
-@cli.command()
+@cli_client.command(name="tellme")
 @click.argument("words", nargs=-1)
 @common_options
-def tellme(words, config_file: str = None, product: list[str] = [""]):
+def tellme(words, config_file: typing.Optional [str], product: list[str] = [""]):
     """Answer a question related to the product."""
     # Loads configurations from common options
     loaded_config, product_configs = return_config_and_product(
@@ -157,9 +188,91 @@ def tellme(words, config_file: str = None, product: list[str] = [""]):
     console.ask_model(question.strip(), product_config)
 
 
-@cli.command()
+@cli_client.command()
+@click.argument("words", nargs=-1)
+@click.option(
+    "--file",
+    type=click.Path(),
+    help="Only available with Gemini 1.5 Pro. Specify a file with which a task is performed.",
+)
+@click.option(
+    "--rag",
+    is_flag=True,
+    help="Only works with --file. Augments the context input with content from your configured RAG system.",
+)
+@common_options
+def helpme(
+    words,
+    config_file: typing.Optional [str],
+    file: typing.Optional [str] = None,
+    rag: bool = False,
+    product: list[str] = [""],
+):
+    """(Experimental) Ask AI to perform a task using console output.
+    Use --file to perform a task on a specific file."""
+    # Loads configurations from common options
+    loaded_config, product_configs = return_config_and_product(
+        config_file=config_file, product=product
+    )
+    if product == ():
+        # Extracts the first product by default
+        product_config = ConfigFile(products=[product_configs.products[0]])
+    else:
+        product_config = product_configs
+    question = ""
+    for word in words:
+        question += word + " "
+    if file:
+        # This feature is only available in gemini 1.5 pro (large context)
+        if product_config.products[0].models.language_model.startswith(
+            "models/gemini-1.5-pro"
+        ):
+            try:
+                with open(file, "r", encoding="utf-8") as auto:
+                    # Read the input content
+                    content = auto.read()
+                    auto.close()
+                final_file = f"The content of the {file} file:\n" + content
+                console.ask_model_with_file(
+                    question.strip(), product_config, file=final_file, rag=rag
+                )
+            except FileNotFoundError:
+                click.echo(f"File not found: {file}")
+        else:
+            click.echo(
+                f"File mode is not supported with this model: {product_config.products[0].models.language_model}"
+            )
+    else:
+        terminal_output = ""
+        # Set the default filename created from the `script` command.
+        file_path = "/tmp/docs_agent_console_input"
+        # Set the maximum number of lines to read from the terminal.
+        lines_limit = -150
+        if product_config.products[0].models.language_model.startswith(
+            "models/gemini-1.5-pro"
+        ):
+            # Read, at the most, 5000 lines printed from the latest command ran on the terminal.
+            lines_limit = -5000
+        try:
+            with open(file_path, "r", encoding="utf-8") as file:
+                for line in file.readlines()[int(lines_limit) : -2]:
+                    # Filter non-ascii and control characters.
+                    printable = set(string.printable)
+                    line = "".join(filter(lambda x: x in printable, line))
+                    terminal_output += line
+                    # if an Enter key is detected, remove all saved lines.
+                    # This allows to read the output of the last commandline only.
+                    if re.search("^\[\?2004", line):
+                        terminal_output = ""
+        except:
+            terminal_output = "No console output is provided."
+        context = "THE FOLLOWING IS MY CONSOLE OUTPUT:\n\n" + terminal_output
+        console.ask_model_for_help(question.strip(), context, product_config)
+
+
+@cli_admin.command()
 @common_options
-def benchmark(config_file: str = None, product: list[str] = [""]):
+def benchmark(config_file: typing.Optional [str], product: list[str] = [""]):
     """Run the Docs Agent benchmark test."""
     # Loads configurations from common options
     loaded_config, product_config = return_config_and_product(
@@ -168,24 +281,27 @@ def benchmark(config_file: str = None, product: list[str] = [""]):
     benchmarks.run_benchmarks()
 
 
-@cli.command()
+@cli_admin.command()
 @common_options
-def list_corpora(config_file: str = None, product: list[str] = [""]):
+def list_corpora(config_file: typing.Optional [str], product: list[str] = [""]):
     """List all existing online corpora."""
     # Loads configurations from common options
     loaded_config, product_config = return_config_and_product(
         config_file=config_file, product=product
     )
     semantic = SemanticRetriever()
-    corpora_response = semantic.list_existing_corpora()
+    response = semantic.list_existing_corpora()
     click.echo(f"Found the following corpora:\n")
-    click.echo(f"{corpora_response}")
+    click.echo(f"{response}")
+    while hasattr(response, "next_page_token") and response.next_page_token != "":
+        response = semantic.list_existing_corpora(response.next_page_token)
+        click.echo(f"{response}")
 
 
-@cli.command()
+@cli_admin.command()
 @click.option("--name", default=None)
 @common_options
-def delete_corpus(name: str, config_file: str = None, product: list[str] = [""]):
+def delete_corpus(name: str, config_file: typing.Optional [str], product: list[str] = [""]):
     """Delete an online corpus."""
     # Loads configurations from common options
     loaded_config, product_config = return_config_and_product(
@@ -204,10 +320,10 @@ def delete_corpus(name: str, config_file: str = None, product: list[str] = [""])
             click.echo(f"Corpora list:\n{corpora_response}")
 
 
-@cli.command()
+@cli_admin.command()
 @click.option("--name", default=None)
 @common_options
-def open_corpus(name: str, config_file: str = None, product: list[str] = [""]):
+def open_corpus(name: str, config_file: typing.Optional [str], product: list[str] = [""]):
     """Share an online corpus with everyone."""
     # Loads configurations from common options
     loaded_config, product_config = return_config_and_product(
@@ -224,13 +340,13 @@ def open_corpus(name: str, config_file: str = None, product: list[str] = [""]):
             click.echo("Successfully shared " + name)
 
 
-@cli.command()
+@cli_admin.command()
 @click.option("--name", default=None)
 @click.option("--email", default=None)
 @click.option("--role", default="READER")
 @common_options
 def share_corpus(
-    name: str, email: str, role: str, config_file: str = None, product: list[str] = [""]
+    name: str, email: str, role: str, config_file: typing.Optional [str], product: list[str] = [""]
 ):
     """Share an online corpus with a user."""
     # Loads configurations from common options
@@ -250,11 +366,11 @@ def share_corpus(
             click.echo("Successfully shared " + name)
 
 
-@cli.command()
+@cli_admin.command()
 @click.option("--name", default=None)
 @common_options
 def remove_corpus_permission(
-    name: str, config_file: str = None, product: list[str] = [""]
+    name: str, config_file: typing.Optional [str], product: list[str] = [""]
 ):
     """Remove a user permission from an online corpus."""
     # Loads configurations from common options
@@ -272,14 +388,31 @@ def remove_corpus_permission(
             click.echo("Successfully removed " + name)
 
 
-@cli.command()
+@cli_admin.command()
+@click.option("--name", default=None)
+@common_options
+def get_all_docs(name: str, config_file: typing.Optional [str], product: list[str] = [""]):
+    """Get the list of all docs in an online corpus."""
+    # Loads configurations from common options
+    loaded_config, product_config = return_config_and_product(
+        config_file=config_file, product=product
+    )
+    semantic = SemanticRetriever()
+    if name == None:
+        click.echo("Usage: agent get_all_docs --name <CORPUS_NAME>")
+    else:
+        click.echo("Getting the list of all docs from " + name)
+        semantic.get_all_docs(corpus_name=name, print_output=True)
+
+
+@cli_admin.command()
 @click.option("--input_chroma", default=None)
 @click.option("--output_dir", default=None)
 @common_options
 def backup_chroma(
     input_chroma: str,
-    output_dir: str = None,
-    config_file: str = None,
+    output_dir: typing.Optional [str],
+    config_file: typing.Optional [str],
     product: list[str] = [""],
 ):
     """Backup a chroma database to an output directory."""
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/run_console.py b/examples/gemini/python/docs-agent/docs_agent/interfaces/run_console.py
index e1c600a74..0b11d9bc5 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/run_console.py
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/run_console.py
@@ -16,19 +16,47 @@
 
 """Run the Docs Agent console in the terminal"""
 
+import typing
 from absl import logging
 from rich.console import Console
 from rich.markdown import Markdown
 from rich.panel import Panel
-import sys
+from rich.text import Text
 from rich.progress import Progress
-import time
 
 from docs_agent.agents.docs_agent import DocsAgent
-from docs_agent.utilities.config import ProductConfig, ReadConfig, ConfigFile
-from docs_agent.interfaces.cli import return_config_and_product
+from docs_agent.utilities.config import ConfigFile
 
 
+# This function is used by the `helpme` command to ask the Gemini Pro model
+# to perform a task based on the console ouput.
+def ask_model_for_help(question: str, context: str, product_configs: ConfigFile):
+    # Initialize Rich console
+    ai_console = Console(width=160)
+    # Filter the input context into text.
+    context_text = Text(context)
+    # Print the console output to be provided as context.
+    ai_console.print("[Context from console output]")
+    ai_console.print(Panel(context_text), markup=False)
+    # Initialize Docs Agent
+    docs_agent = DocsAgent(config=product_configs.products[0], init_chroma=False)
+    (
+        good_response,
+        new_prompt,
+    ) = docs_agent.ask_content_model_with_context_prompt(
+        context=context,
+        question=question,
+        prompt="Examine the console output below and respond to the user's request specified at the end of this prompt:",
+    )
+    # Print the Gemini response
+    ai_console.print()
+    ai_console.print("[Gemini response]")
+    ai_console.print()
+    ai_console.print(good_response)
+
+
+# This function is used by the `tellme` command to ask the Gemini AQA model
+# a question from an online corpus.
 def ask_model(question: str, product_configs: ConfigFile):
     # Initialize Rich console
     ai_console = Console(width=160)
@@ -177,63 +205,200 @@ def ask_model(question: str, product_configs: ConfigFile):
         ai_console.print(Markdown("To verify this information, see:\n"))
         md_links = ""
         for item in final_links:
-            if not item.startswith("UUID"):
-                md_links += f"\n* [{item}]({item})\n"
-                count += 1
+            if isinstance(item, str):
+                if not item.startswith("UUID"):
+                    md_links += f"\n* [{item}]({item})\n"
+                    count += 1
         ai_console.print(Markdown(md_links))
 
 
-def main():
-    # Set logging level to WARNING or above to disable progress bar
-    logging.set_verbosity(logging.WARNING)
-
+# This function is used by the `helpme` command to ask the Gemini AQA model
+# a question from an online corpus.
+def ask_model_with_file(
+    question: str,
+    product_configs: ConfigFile,
+    file: typing.Optional[str] = None,
+    rag: bool = False,
+):
     # Initialize Rich console
     ai_console = Console(width=160)
-    ai_console.rule("Fold")
-
+    full_prompt = ""
+    final_context = ""
+    results_num = 5
     # Initialize Docs Agent
-    ai_console.print("STATE: Initializing Docs Agent.")
-    docs_agent = DocsAgent()
-    ai_console.print("\nHello! I'm PaLM 2.\n")
-
-    # First question (for quick testing)
-    question = "What are some differences between apples and oranges?"
-
-    # User input loop
-    while True:
-        # Get context from the vector database
-        result = docs_agent.query_vector_store(question)
-        context = result.fetch_formatted(Format.CONTEXT)
-        # Add instruction to the context
-        context_with_prefix = docs_agent.add_instruction_to_context(context)
-        # Print the context
-        ai_console.print(Panel.fit(Markdown("\nContext: " + context_with_prefix)))
-        # Get URLs of the context from the vector database
-        metadatas = result.fetch_formatted(Format.URL)
-        ai_console.print(Panel.fit(Markdown(metadatas)))
-        # Print the question
-        ai_console.print(Panel.fit("Question: " + question))
-        ai_console.print("\nPaLM 2:")
-        # Pass the context and question to PaLM 2 (Text)
-        response_text = docs_agent.ask_text_model_with_context(
-            context_with_prefix, question
-        )
-        ai_console.print("\n[Text answer]:")
-        ai_console.print(Panel.fit(Markdown(response_text)))
-        # Pass the context and question to PaLM 2 (Chat)
-        response_chat = docs_agent.ask_chat_model_with_context(
-            context_with_prefix, question
+    with Progress(transient=True) as progress:
+        search_results = []
+        responses = []
+        links = []
+        task_docs_agent = progress.add_task(
+            "[turquoise4 bold]Starting Docs Agent ", total=None, refresh=True
         )
-        ai_console.print("\n[Chat answer]:")
-        ai_console.print(Panel.fit(Markdown(response_chat)))
-        # Keep asking questions to PaLM 2
-        ai_console.print("\n######## Ask PaLM 2 ########")
-        question = input("How can I help?\n> ")
-        ai_console.print("")
-        if question.startswith("exit"):
-            ai_console.print("Goodbye!")
-            sys.exit()
-
-
-if __name__ == "__main__":
-    main()
+        for product in product_configs.products:
+            if "gemini" in product.models.language_model:
+                if rag and product.db_type == "chroma":
+                    docs_agent = DocsAgent(config=product, init_chroma=True)
+                else:
+                    docs_agent = DocsAgent(config=product, init_chroma=False)
+                progress.update(
+                    task_docs_agent,
+                    description=f"[turquoise4 bold]Asking Gemini (model: {product.models.language_model}, source: {docs_agent.return_chroma_collection()}) ",
+                    total=None,
+                )
+                if docs_agent.config.docs_agent_config == "experimental":
+                    results_num = 10
+                    new_question_count = 5
+                else:
+                    results_num = 5
+                    new_question_count = 5
+                if file is not None:
+                    results_num = 15
+                    if rag:
+                        (
+                            search_result,
+                            final_context,
+                        ) = docs_agent.query_vector_store_to_build(
+                            question=question,
+                            token_limit=500000,
+                            results_num=results_num,
+                            max_sources=results_num,
+                        )
+                        final_context = (
+                            "This is the file: \n" + str(file) + "\n\n" + final_context
+                        )
+                    else:
+                        search_result = []
+                        link = ""
+                        final_context = "This is the file: \n" + str(file) + "\n"
+                else:
+                    # Issue if max_sources > results_num, so leave the same for now
+                    (
+                        search_result,
+                        final_context,
+                    ) = docs_agent.query_vector_store_to_build(
+                        question=question,
+                        token_limit=30000,
+                        results_num=results_num,
+                        max_sources=results_num,
+                    )
+                (
+                    response,
+                    full_prompt,
+                ) = docs_agent.ask_content_model_with_context_prompt(
+                    context=final_context, question=question
+                )
+                if len(search_result) >= 1:
+                    if search_result[0].section.url == "":
+                        link = str(search_result[0].section)
+                    else:
+                        link = search_result[0].section.url
+                search_results.append(search_result)
+                responses.append(response)
+                links.append(link)
+            elif "aqa" in product.models.language_model:
+                if product.db_type == "google_semantic_retriever":
+                    docs_agent = DocsAgent(config=product, init_chroma=False)
+                    label = f"[turquoise4 bold]Asking Gemini (model: {product.models.language_model}, "
+                    corpus_name = ""
+                    link = ""
+                    for db_config in product.db_configs:
+                        if db_config.db_type == "google_semantic_retriever":
+                            corpus_name = db_config.corpus_name
+                    if corpus_name != "":
+                        label += "source: " + corpus_name + ") "
+                    progress.update(
+                        task_docs_agent, description=label, total=None, refresh=True
+                    )
+                    (response, search_result) = docs_agent.ask_aqa_model_using_corpora(
+                        question=question
+                    )
+                    if len(search_result) >= 1:
+                        if search_result[0].section.url == "":
+                            link = str(search_result[0].section)
+                        else:
+                            link = search_result[0].section.url
+                    search_results.append(search_result)
+                    responses.append(response)
+                    link = ""
+                    links.append(link)
+                elif product.db_type == "chroma":
+                    docs_agent = DocsAgent(config=product, init_chroma=True)
+                    progress.update(
+                        task_docs_agent,
+                        description=f"[turquoise4 bold]Asking Gemini (model: {product.models.language_model}, source: {docs_agent.return_chroma_collection()}) ",
+                        total=None,
+                    )
+                    (
+                        response,
+                        search_result,
+                    ) = docs_agent.ask_aqa_model_using_local_vector_store(
+                        question=question, results_num=results_num
+                    )
+                    if len(search_result) >= 1:
+                        if search_result[0].section.url == "":
+                            link = str(search_result[0].section)
+                        else:
+                            link = search_result[0].section.url
+                    search_results.append(search_result)
+                    responses.append(response)
+                    links.append(link)
+                else:
+                    logging.error(f"Unknown db_type: {product.db_type}")
+        final_search = []
+        final_responses = []
+        final_response_md = ""
+        final_links = []
+        count = 0
+        # Prune the generated answers as needed
+        for item in search_results:
+            # Gemini-pro + chroma will give distance instead of probability
+            # if (item[0].probability != 0):
+            final_search.append(search_results[count])
+            final_responses.append(responses[count])
+            final_response_md += responses[count] + "\n"
+            final_links.append(links[count])
+            count += 1
+        count = 0
+        synthesize = False
+        synthesize_product = None
+        # Currently only triggers from a gemini entry into the provided products
+        for product in product_configs.products:
+            if "gemini" in product.models.language_model:
+                synthesize_product = product
+        if synthesize and not (synthesize_product == None):
+            docs_agent = DocsAgent(config=synthesize_product, init_chroma=False)
+            progress.update(
+                task_docs_agent,
+                description=f"[turquoise4 bold]Asking {docs_agent.context_model} to synthesize a response",
+                total=None,
+            )
+            new_question = f"Help me synthesize the context above into a cohesive response to help me answer my original question. Only use content from the provided context. My original question: {question}"
+            (
+                good_response,
+                new_prompt,
+            ) = docs_agent.ask_content_model_with_context_prompt(
+                context=final_response_md, question=new_question, prompt=""
+            )
+            progress.update(task_docs_agent, visible=False, refresh=True)
+            # Final printing to console
+            ai_console.print()
+            ai_console.print(Markdown(good_response))
+        else:
+            # This returns the responses as is for each agent interaction
+            progress.update(task_docs_agent, visible=False, refresh=True)
+            ai_console.print()
+            ai_console.print(Markdown(final_response_md))
+        # Print results
+        for item in final_search:
+            count += 1
+        count = 0
+        ai_console.print()
+        md_links = ""
+        for item in final_links:
+            if isinstance(item, str):
+                if not item.startswith("UUID"):
+                    md_links += f"\n* [{item}]({item})\n"
+                    count += 1
+        # Avoid printing links if first link is blank
+        if final_links[0] != "":
+            ai_console.print(Markdown("To verify this information, see:\n"))
+            ai_console.print(Markdown(md_links))
diff --git a/examples/gemini/python/docs-agent/docs_agent/memory/logging.py b/examples/gemini/python/docs-agent/docs_agent/memory/logging.py
index 9b47c49cf..a6b7ef9f5 100644
--- a/examples/gemini/python/docs-agent/docs_agent/memory/logging.py
+++ b/examples/gemini/python/docs-agent/docs_agent/memory/logging.py
@@ -16,7 +16,6 @@
 
 from datetime import datetime
 import pytz
-from pytz import timezone
 
 """Module to log interactions with the chatbot"""
 
@@ -25,7 +24,7 @@
 def log_question(uid, user_question: str, response: str, probability: str = "None", save: str = "txt"):
     date_format = "%m/%d/%Y %H:%M:%S %Z"
     date = datetime.now(tz=pytz.utc)
-    date = date.astimezone(timezone("US/Pacific"))
+    date = date.astimezone(pytz.timezone("US/Pacific"))
     print("UID: " + str(uid))
     print("Question: " + user_question.strip() + "\n")
     print("Response:")
@@ -40,6 +39,8 @@ def log_question(uid, user_question: str, response: str, probability: str = "Non
             )
             log_file.write("# " + user_question.strip() + "\n\n")
             log_file.write(response.strip() + "\n\n")
+            if probability != "None":
+                log_file.write("Answerable probability: " + str(probability) + "\n\n")
             log_file.close()
         # Added to track the answerable_probability scores.
         if probability != "None":
@@ -55,7 +56,7 @@ def log_question(uid, user_question: str, response: str, probability: str = "Non
 def log_like(is_like, uid, save: str = "txt"):
     date_format = "%m/%d/%Y %H:%M:%S %Z"
     date = datetime.now(tz=pytz.utc)
-    date = date.astimezone(timezone("US/Pacific"))
+    date = date.astimezone(pytz.timezone("US/Pacific"))
     print("UID: " + str(uid))
     print("Like: " + str(is_like))
     if save == "txt":
diff --git a/examples/gemini/python/docs-agent/docs_agent/models/google_genai.py b/examples/gemini/python/docs-agent/docs_agent/models/google_genai.py
index f12e1d942..f8ff4b634 100644
--- a/examples/gemini/python/docs-agent/docs_agent/models/google_genai.py
+++ b/examples/gemini/python/docs-agent/docs_agent/models/google_genai.py
@@ -16,16 +16,19 @@
 
 """Rate limited Gemini wrapper"""
 
-import os
+import typing
+from typing import List
 
 import google.generativeai
-from ratelimit import limits, sleep_and_retry
-from typing import List
+from ratelimit import limits
+from ratelimit import sleep_and_retry
+
 from docs_agent.utilities.config import Models
+from docs_agent.utilities.config import Conditions
 
 
 class Error(Exception):
-    """Base error class for Gemini"""
+    """Base error class for Gemini."""
 
 
 class GoogleNoAPIKeyError(Error, RuntimeError):
@@ -49,15 +52,15 @@ def __init__(self, model, api_endpoint) -> None:
 
 
 class Gemini:
-    """Rate limited Gemini wrapper
-
-    This class exposes Gemini's chat, text, and embedding API, but with a rate limit.
-    Besides the rate limit, the `chat` and `generate_text` method has the same name and
-    behavior as `google.generativeai.chat` and `google.generativeai.generate_text`,
-    respectively. The `embed` method is different from
-    `google.generativeai.generate_embeddings` since `embed` returns List[float]
-    while `google.generativeai.generate_embeddings` returns a dict. And that's why it
-    has a different name.
+    """Rate limited Gemini wrapper.
+
+    This class exposes Gemini's chat, text, and embedding API, but with a rate
+    limit. Besides the rate limit, the `chat` and `generate_text` method has the
+    same name and behavior as `google.generativeai.chat` and
+    `google.generativeai.generate_text`, respectively. The `embed` method is
+    different from `google.generativeai.generate_embeddings` since `embed`
+    returns List[float] while `google.generativeai.generate_embeddings` returns a
+    dict. And that's why it has a different name.
     """
 
     minute = 60  # seconds in a minute
@@ -65,7 +68,17 @@ class Gemini:
     max_text_per_minute = 30
 
     # MAX_MESSAGE_PER_MINUTE = 30
-    def __init__(self, models_config: Models) -> None:
+    def __init__(
+        self,
+        models_config: Models,
+        conditions: typing.Optional[Conditions] = None,
+    ) -> None:
+        if conditions is None:
+            self.model_error_message = "Gemini model failed to generate"
+            self.prompt_condition = ""
+        else:
+            self.model_error_message = conditions.model_error_message
+            self.prompt_condition = conditions.condition_text
         self.api_endpoint = models_config.api_endpoint
         self.api_key = models_config.api_key
         self.embed_model = models_config.embedding_model
@@ -78,20 +91,26 @@ def __init__(self, models_config: Models) -> None:
             api_key=self.api_key, client_options={"api_endpoint": self.api_endpoint}
         )
         # Check whether the specified models are supported
-        supported_models = set(
-            model.name for model in google.generativeai.list_models()
-        )
-        for model in (models_config.language_model, models_config.embedding_model):
-            if model not in supported_models:
-                raise GoogleUnsupportedModelError(model, self.api_endpoint)
-
-    # TODO bring in limit values from config files
+        # supported_models = set(
+        #    model.name for model in google.generativeai.list_models()
+        # )
+        # for model in (models_config.language_model, models_config.embedding_model):
+        #  if model not in supported_models:
+        #    raise GoogleUnsupportedModelError(model, self.api_endpoint)
+
+    # TODO: bring in limit values from config files
     @sleep_and_retry
     @limits(calls=max_embed_per_minute, period=minute)
     def embed(
-        self, content, task_type: str = "RETRIEVAL_QUERY", title: str = None
+        self,
+        content,
+        task_type: str = "RETRIEVAL_QUERY",
+        title: typing.Optional[str] = None,
     ) -> List[float]:
-        if self.embed_model == "models/embedding-001":
+        if (
+            self.embed_model == "models/embedding-001"
+            or self.embed_model == "models/text-embedding-004"
+        ):
             return [
                 google.generativeai.embed_content(
                     model=self.embed_model,
@@ -101,34 +120,32 @@ def embed(
                 )["embedding"]
             ]
         else:
-            raise GoogleNoModelError(func_name="embed", attr="embed_model")
+            raise GoogleUnsupportedModelError(self.embed_model, self.api_endpoint)
 
-    # TODO bring in limit values from config files
+    # TODO: bring in limit values from config files
     @sleep_and_retry
     @limits(calls=max_text_per_minute, period=minute)
     def generate_content(self, contents):
         if self.language_model is None:
-            raise GoogleNoModelError(func_name="generate_content", attr="content_model")
+            raise GoogleUnsupportedModelError(self.language_model, self.api_endpoint)
         model = google.generativeai.GenerativeModel(model_name=self.language_model)
         return model.generate_content(contents)
 
     # Use this method for talking to a Gemini content model
     # Optionally provide a prompt, if not use the one from config.yaml
-    # If prompt is "fact_checker" it will use the fact_check_question from
-    # config.yaml for the prompt
     def ask_content_model_with_context_prompt(
-        self, context: str, question: str, prompt: str = None
+        self, context: str, question: str, prompt: typing.Optional[str] = None
     ):
         if prompt == None:
             prompt = self.prompt_condition
-        elif prompt == "fact_checker":
-            prompt = self.fact_check_question
+        # elif prompt == "fact_checker":
+        #   prompt = self.fact_check_question
         new_prompt = f"{prompt}\n\nQuestion: {question}\n\nContext:\n{context}"
         # Print the prompt for debugging if the log level is VERBOSE.
-        if LOG_LEVEL == "VERBOSE":
-            self.print_the_prompt(new_prompt)
+        # if LOG_LEVEL == "VERBOSE":
+        #   self.print_the_prompt(new_prompt)
         try:
-            response = palm.generate_content(new_prompt)
+            response = self.generate_content(new_prompt)
         except google.api_core.exceptions.InvalidArgument:
             return self.model_error_message
         for chunk in response:
@@ -136,8 +153,9 @@ def ask_content_model_with_context_prompt(
                 return self.model_error_message
         return response.text, new_prompt
 
-    # Use this method for asking a Gemini content model for fact-checking
-    def ask_content_model_to_fact_check(self, context, prev_response):
-        question = self.fact_check_question + "\n\nText: "
-        question += prev_response
-        return self.ask_content_model_with_context(context, question)
+
+#   # Use this method for asking a Gemini content model for fact-checking
+#   def ask_content_model_to_fact_check(self, context, prev_response):
+#     question = self.fact_check_question + "\n\nText: "
+#     question += prev_response
+#     return self.ask_content_model_with_context(context, question)
diff --git a/examples/gemini/python/docs-agent/docs_agent/models/tokenCount.py b/examples/gemini/python/docs-agent/docs_agent/models/tokenCount.py
index b54c14d6c..8561e5966 100644
--- a/examples/gemini/python/docs-agent/docs_agent/models/tokenCount.py
+++ b/examples/gemini/python/docs-agent/docs_agent/models/tokenCount.py
@@ -14,7 +14,6 @@
 # limitations under the License.
 #
 from docs_agent.utilities import config
-from docs_agent.models import palm
 
 # Per the docs 60-80 words ~= 100 tokens
 lower_word_limit = 60 / 100
@@ -31,47 +30,47 @@
 # This value should be set in config.yaml file to avoid unecessary API calls as
 # it will mostly remain static until a different model is used
 # todo: clean these functions
-def returnMaxInputTokenModelAPI() -> float:
-    model = palm.returnModel("models/text-bison-001")
-    maxInputToken = model.input_token_limit
-    max_context_tokens_api = maxInputToken - max_token_count_api
-    print("Max input token: " + str(maxInputToken))
-    max_context_tokens = maxInputToken - max_token_count_prompt
-    print("Max input token removing prompt: " + str(max_context_tokens))
-    print("Max input token removing prompt API: " + str(max_context_tokens_api))
+# def returnMaxInputTokenModelAPI() -> float:
+#     model = "models/gemini-pro"
+#     maxInputToken = model.input_token_limit
+#     max_context_tokens_api = maxInputToken - max_token_count_api
+#     print("Max input token: " + str(maxInputToken))
+#     max_context_tokens = maxInputToken - max_token_count_prompt
+#     print("Max input token removing prompt: " + str(max_context_tokens))
+#     print("Max input token removing prompt API: " + str(max_context_tokens_api))
 
-    number_of_context_per_prompt = 3
+#     number_of_context_per_prompt = 3
 
-    # Remove 5 tokens to give additional token buffer
-    max_chunk_size = max_context_tokens / number_of_context_per_prompt - 5
-    print("Max chunk size is: " + str(max_chunk_size))
-    max_chunk_size_api = max_context_tokens_api / number_of_context_per_prompt - 5
-    print("Max chunk size is API: " + str(max_chunk_size_api))
-    max_token_count_prompt = estimateTokensAverage(CONDITION_TEXT) + 5
-    max_token_count_api = palm.tokenCount(CONDITION_TEXT) + 5
+#     # Remove 5 tokens to give additional token buffer
+#     max_chunk_size = max_context_tokens / number_of_context_per_prompt - 5
+#     print("Max chunk size is: " + str(max_chunk_size))
+#     max_chunk_size_api = max_context_tokens_api / number_of_context_per_prompt - 5
+#     print("Max chunk size is API: " + str(max_chunk_size_api))
+#     max_token_count_prompt = estimateTokensAverage(CONDITION_TEXT) + 5
+#     max_token_count_api = palm.tokenCount(CONDITION_TEXT) + 5
 
-    return max_token_count_api
+#     return max_token_count_api
 
 
-def returnMaxInputTokenModelEstimate() -> float:
-    model = palm.returnModel("models/text-bison-001")
-    maxInputToken = model.input_token_limit
-    max_context_tokens_api = maxInputToken - max_token_count_api
-    print("Max input token: " + str(maxInputToken))
-    max_context_tokens = maxInputToken - max_token_count_prompt
-    print("Max input token removing prompt: " + str(max_context_tokens))
-    print("Max input token removing prompt API: " + str(max_context_tokens_api))
+# def returnMaxInputTokenModelEstimate() -> float:
+# #    model = "models/gemini-pro"
+#     maxInputToken = model.input_token_limit
+#     max_context_tokens_api = maxInputToken - max_token_count_api
+#     print("Max input token: " + str(maxInputToken))
+#     max_context_tokens = maxInputToken - max_token_count_prompt
+#     print("Max input token removing prompt: " + str(max_context_tokens))
+#     print("Max input token removing prompt API: " + str(max_context_tokens_api))
 
-    number_of_context_per_prompt = 3
+#     number_of_context_per_prompt = 3
 
-    # Remove 5 tokens to give additional token buffer
-    max_chunk_size = max_context_tokens / number_of_context_per_prompt - 5
-    print("Max chunk size is: " + str(max_chunk_size))
-    max_chunk_size_api = max_context_tokens_api / number_of_context_per_prompt - 5
-    print("Max chunk size is API: " + str(max_chunk_size_api))
-    max_token_estimate = estimateTokensAverage(CONDITION_TEXT) + 5
+#     # Remove 5 tokens to give additional token buffer
+#     max_chunk_size = max_context_tokens / number_of_context_per_prompt - 5
+#     print("Max chunk size is: " + str(max_chunk_size))
+#     max_chunk_size_api = max_context_tokens_api / number_of_context_per_prompt - 5
+#     print("Max chunk size is API: " + str(max_chunk_size_api))
+#     max_token_estimate = estimateTokensAverage(CONDITION_TEXT) + 5
 
-    return max_token_estimate
+#     return max_token_estimate
 
 
 # Function to return a character count of a string
@@ -124,4 +123,4 @@ def returnHighestTokens(input):
     if char_estimate >= word_estimate:
         return char_estimate
     else:
-        return word_estimate
+        return word_estimate
\ No newline at end of file
diff --git a/examples/gemini/python/docs-agent/docs_agent/postprocess/docs_retriever.py b/examples/gemini/python/docs-agent/docs_agent/postprocess/docs_retriever.py
index c6c27539f..885436f77 100644
--- a/examples/gemini/python/docs-agent/docs_agent/postprocess/docs_retriever.py
+++ b/examples/gemini/python/docs-agent/docs_agent/postprocess/docs_retriever.py
@@ -51,10 +51,10 @@ def __init__(self, section_list: list[Section]):
     def __str__(self):
         return f"This is a page with the following content:\n"
 
-    def buildPage(section_list):
+    def buildPage(self):
         final_page = ""
         total_token_count = 0
-        for item in section_list:
+        for item in self.section_list:
             # It is possible to check attributes such as token_count of chunks here
             # (i.e)if item.token_count > 800:
             final_page += item.content
@@ -81,6 +81,7 @@ def returnSelfSection(self, section_id):
     def returnChildrenSections(self, section_id, token_limit: float = float("inf")):
         # Finds the Section given a section_id
         match = False
+        updated_list = []
         for item in self.section_list:
             if section_id == item.id:
                 given_section = item
@@ -92,7 +93,7 @@ def returnChildrenSections(self, section_id, token_limit: float = float("inf")):
             return FullPage(section_list=updated_list)
         # Start token count at 0
         curr_token = 0
-        updated_list = []
+        # updated_list = []
         for item in self.section_list:
             direct_parent = item.returnDirectParentId()
             if int(direct_parent) == int(given_section.id):
@@ -114,6 +115,7 @@ def returnChildrenSections(self, section_id, token_limit: float = float("inf")):
     def returnSiblingSections(self, section_id, token_limit: float = float("inf")):
         # Finds the Section given a section_id
         match = False
+        updated_list = []
         for item in self.section_list:
             if section_id == item.id:
                 given_section = item
@@ -126,7 +128,7 @@ def returnSiblingSections(self, section_id, token_limit: float = float("inf")):
             return FullPage(section_list=updated_list)
         # Start token count at 0
         curr_token = 0
-        updated_list = []
+        # updated_list = []
         given_parent_tree = eval(given_section.parent_tree)
         for item in self.section_list:
             direct_parent = item.returnDirectParentId()
@@ -153,20 +155,22 @@ def returnSiblingSections(self, section_id, token_limit: float = float("inf")):
     def returnParentSection(self, section_id, token_limit: float = float("inf")):
         # Finds the Section given a section_id
         match = False
+        updated_list = []
         for item in self.section_list:
             if section_id == item.id:
                 given_section = item
                 match = True
                 # Break out of loop once a match is found
+                given_parent = given_section.returnDirectParentId()
                 break
         # If Section doesn't match, just return a FullPage with a blank list
         if not match:
             print(f"Could not find a section with the provided ID {section_id}")
-            return FullPage(section_list=updated_list)
+            # return FullPage(section_list=updated_list)
+            return None
         # Start token count at 0
         curr_token = 0
-        updated_list = []
-        given_parent = given_section.returnDirectParentId()
+        # updated_list = []
         for item in self.section_list:
             direct_parent = item.returnDirectParentId()
             if int(given_parent) == int(item.id) and given_parent != 0:
@@ -207,8 +211,9 @@ def buildSections(
         section_token_count = 0
         if selfSection:
             self_section = self.returnSelfSection(section_id=section_id)
-            final_sections.append(self_section)
-            section_token_count += self_section.token_count
+            if self_section is not None:
+                final_sections.append(self_section)
+                section_token_count += self_section.token_count
         if children:
             children_sections = self.returnChildrenSections(
                 section_id=section_id, token_limit=(token_limit - section_token_count)
@@ -234,4 +239,4 @@ def buildSections(
                 if item is not None:
                     section_token_count += item.token_count
         final_page = FullPage(final_sections).sortSections(reverse=reverse)
-        return final_page
+        return final_page
\ No newline at end of file
diff --git a/examples/gemini/python/docs-agent/docs_agent/preprocess/files_to_plain_text.py b/examples/gemini/python/docs-agent/docs_agent/preprocess/files_to_plain_text.py
index a3d99ec6f..1b06c6006 100644
--- a/examples/gemini/python/docs-agent/docs_agent/preprocess/files_to_plain_text.py
+++ b/examples/gemini/python/docs-agent/docs_agent/preprocess/files_to_plain_text.py
@@ -16,29 +16,37 @@
 
 """Process Markdown files into plain text"""
 
-from markdown import markdown
 import shutil
 import os
 import re
 import json
+import typing
+import uuid
 from absl import logging
 
-from tqdm import tqdm
-import frontmatter
+import tqdm
 from docs_agent.utilities import config
-from docs_agent.utilities.config import ProductConfig, ReadConfig, Input
+from docs_agent.utilities.config import ProductConfig, ConfigFile, Input
 from docs_agent.models.tokenCount import returnHighestTokens
 from docs_agent.utilities.helpers import (
-    get_project_path,
     resolve_path,
     add_scheme_url,
     end_path_backslash,
     start_path_no_backslash,
 )
-from docs_agent.preprocess.splitters import markdown_splitter, html_splitter, fidl_splitter
-from docs_agent.models import palm as palmModule
-import uuid
+from docs_agent.preprocess.splitters import (
+    markdown_splitter,
+    html_splitter,
+    fidl_splitter,
+)
 
+# Construct a URL from a URL prefix and a relative path.
+def construct_a_url(url_prefix: str, relative_path: str):
+    temp_url = end_path_backslash(add_scheme_url(url=url_prefix, scheme="https"))
+    built_url = temp_url + start_path_no_backslash(relative_path)
+    strip_ext_url = re.search(r"(.*)\.md$", built_url)
+    built_url = strip_ext_url[1]
+    return built_url
 
 # This function pre-processes files before they are actually chunked.
 # This allows it to resolve includes of includes, Jinja templates, etc...
@@ -61,7 +69,7 @@ def pre_process_doc_files(
     file_count = sum(
         len(files) for _, _, files in os.walk(resolve_path(inputpathitem.path))
     )
-    progress_bar = tqdm(
+    progress_bar = tqdm.tqdm(
         total=file_count,
         position=0,
         bar_format="{percentage:3.0f}% | {n_fmt}/{total_fmt} | {elapsed}/{remaining}| {desc}",
@@ -124,23 +132,269 @@ def pre_process_doc_files(
     return temp_output
 
 
-# This function processes files in the `input_path` directory
-# into plain text files.
-# Supports: Markdown files
+# This function processes a Markdown file and
+# splits it into smaller text chunks.
+def process_markdown_file(
+    filename: str,
+    root: str,
+    inputpathitem: Input,
+    splitter: str,
+    new_path: str,
+    file: str,
+    namespace_uuid: uuid.UUID,
+    relative_path: str,
+    url_prefix: str,
+):
+    file_metadata = {}
+    # Read the input Markdown content
+    to_file = ""
+    with open(filename, "r", encoding="utf-8") as auto:
+        to_file = auto.read()
+        auto.close()
+    # Process includes lines in Markdown
+    file_with_include = markdown_splitter.process_markdown_includes(to_file, root)
+    # Process include lines in HTML
+    file_with_include = html_splitter.process_html_includes(
+        file_with_include, inputpathitem.include_path_html
+    )
+    # Estimate of the token count
+    page_token_estimate = returnHighestTokens(file_with_include)
+    # Get the original input path
+    original_input = inputpathitem.path
+    if splitter == "token_splitter":
+        # Returns an array of Section objects along with a Page
+        # Object that contains metadata
+        (
+            page_sections,
+            page,
+        ) = markdown_splitter.process_markdown_page(
+            markdown_text=file_with_include, header_id_spaces="-"
+        )
+        # Process this page's sections into plain text chunks.
+        chunk_number = 0
+        for section in page_sections:
+            filename_to_save = make_chunk_name(
+                new_path=new_path,
+                file=file,
+                index=chunk_number,
+                extension="md",
+            )
+            # Generate UUID for each plain text chunk and collect its metadata,
+            # which will be written to the top-level `file_index.json` file.
+            md_hash = uuid.uuid3(namespace_uuid, section.content)
+            uuid_file = uuid.uuid3(namespace_uuid, filename_to_save)
+            origin_uuid = uuid.uuid3(namespace_uuid, relative_path)
+            # If no URL came from frontmatter, assign URL from config
+            if page.URL == "":
+                page.URL = end_path_backslash(
+                    add_scheme_url(url=url_prefix, scheme="https")
+                )
+            # Strip extension of .md from url
+            # Makes sure that relative_path starts without backslash
+            # page.url will have backslash
+            built_url = page.URL + start_path_no_backslash(relative_path)
+            strip_ext_url = re.search(r"(.*)\.md$", built_url)
+            built_url = strip_ext_url[1]
+            # Build the valid URL for a section including header
+            # Do not add a # if section 1
+            if section.name_id != "" and int(section.level) != 1:
+                built_url = built_url + "#" + section.name_id
+            # Adds additional info so that the section can know its origin
+            file_metadata[filename_to_save] = {
+                "UUID": str(uuid_file),
+                "origin_uuid": str(origin_uuid),
+                "source": str(original_input),
+                "source_file": str(relative_path),
+                "page_title": str(section.page_title),
+                "section_title": str(section.section_title),
+                "section_name_id": str(section.name_id),
+                "section_id": int(section.id),
+                "section_level": int(section.level),
+                "previous_id": int(section.previous_id),
+                "URL": str(built_url),
+                "md_hash": str(md_hash),
+                "token_estimate": float(section.token_count),
+                "full_token_estimate": float(page_token_estimate),
+                "parent_tree": list(section.parent_tree),
+                "metadata": dict(page.metadata),
+            }
+            with open(filename_to_save, "w", encoding="utf-8") as new_file:
+                new_file.write(section.content)
+                new_file.close()
+            chunk_number += 1
+    elif splitter == "process_sections":
+        # Use a custom Markdown splitter to split a Markdown file
+        # into small text chunks
+        to_file = markdown_splitter.process_markdown_includes(to_file, root)
+        # Add the page title and section title into each text chunk
+        (
+            to_file,
+            metadata,
+        ) = markdown_splitter.process_page_and_section_titles(to_file)
+        # Process this page's sections into plain text chunks.
+        docs = markdown_splitter.process_document_into_sections(to_file)
+        # Process each text chunk.
+        chunk_number = 0
+        for doc in docs:
+            filename_to_save = make_chunk_name(
+                new_path=new_path,
+                file=file,
+                index=chunk_number,
+                extension="md",
+            )
+            # Generate UUID for each plain text chunk and collect its metadata,
+            # which will be written to the top-level `file_index.json` file.
+            md_hash = uuid.uuid3(namespace_uuid, file_with_include)
+            uuid_file = uuid.uuid3(namespace_uuid, filename_to_save)
+            # Clean up Markdown and HTML syntax
+            content = markdown_splitter.markdown_to_text(doc)
+            # Contruct a URL
+            built_url = construct_a_url(url_prefix, relative_path)
+            # Get the page title
+            page_title = "None"
+            if "title" in  metadata:
+                page_title = metadata["title"]
+            # Construct metadata.
+            file_metadata[filename_to_save] = {
+                "UUID": str(uuid_file),
+                "origin_uuid": str(uuid_file),
+                "source": str(original_input),
+                "source_file": str(relative_path),
+                "page_title": str(page_title),
+                "section_title": str("None"),
+                "section_name_id": str("None"),
+                "section_id": int(1),
+                "section_level": int(1),
+                "previous_id": int(1),
+                "URL": str(built_url),
+                "md_hash": str(md_hash),
+                "token_estimate": float(1.0),
+                "full_token_estimate": float(page_token_estimate),
+                "metadata": dict(metadata),
+            }
+            with open(filename_to_save, "w", encoding="utf-8") as new_file:
+                new_file.write(content)
+                new_file.close()
+            chunk_number += 1
+    else:
+        # Exits if no valid markdown splitter
+        logging.error(
+            f"Select a valid markdown_splitter option in your configuration for your product\n"
+        )
+        exit()
+    return file_metadata
+
+
+# This function processes a FIDL file (.fidl) into small text chunks.
+def process_fidl_file(
+    filename: str,
+    root: str,
+    inputpathitem: Input,
+    splitter: str,
+    new_path: str,
+    file: str,
+    namespace_uuid: uuid.UUID,
+    relative_path: str,
+    url_prefix: str,
+):
+    # Local variables
+    file_metadata = {}
+    library_name = ""
+    filename_prefix = "index"
+    chunk_number = 0
+    # Get the original input path
+    original_input = inputpathitem.path
+    # Read the input FIDL content
+    to_file = ""
+    with open(filename, "r", encoding="utf-8") as auto:
+        to_file = auto.read()
+        auto.close()
+    # Split the FIDL file into a list of FIDL protocols.
+    fidl_protocols = fidl_splitter.split_file_to_protocols(to_file)
+    # Iterate the list of FIDL protocols.
+    for fidl_protocol in fidl_protocols:
+        # Identify the new FIDL chunk file path and name.
+        filename_to_save = make_file_chunk_name(
+            new_path=new_path, filename_prefix=filename_prefix, index=chunk_number
+        )
+        # Prepare metadata for this FIDL protocol chunk.
+        md_hash = uuid.uuid3(namespace_uuid, fidl_protocol)
+        uuid_file = uuid.uuid3(namespace_uuid, filename_to_save)
+        origin_uuid = uuid.uuid3(namespace_uuid, relative_path)
+        # Contruct the URL for this FIDL protocol.
+        match_library = re.search(r"^Library\sname:\s+(.*)\n", fidl_protocol)
+        if match_library:
+            library_name = match_library.group(1)
+        # If no library name is found,
+        # the library name from the previous protocol is used.
+        fidl_url = url_prefix + library_name
+        file_metadata[filename_to_save] = {
+            "UUID": str(uuid_file),
+            "origin_uuid": str(origin_uuid),
+            "source": str(original_input),
+            "source_file": str(relative_path),
+            "source_id": int(chunk_number),
+            "page_title": str(library_name),
+            "section_title": str(library_name),
+            "section_name_id": str("None"),
+            "section_id": int(1),
+            "section_level": int(1),
+            "previous_id": int(1),
+            "URL": str(fidl_url),
+            "md_hash": str(md_hash),
+            "token_estimate": float(1.0),
+            "full_token_estimate": float(1.0),
+        }
+        # Save the FIDL protocol content as a text chunk.
+        with open(filename_to_save, "w", encoding="utf-8") as new_file:
+            new_file.write(fidl_protocol)
+            new_file.close()
+        chunk_number += 1
+    return file_metadata
+
+
+# This function processes a HTML file into small text chunks.
+def process_html_file(
+    filename: str,
+    root: str,
+    inputpathitem: Input,
+    splitter: str,
+    new_path: str,
+    file: str,
+    namespace_uuid: uuid.UUID,
+    relative_path: str,
+    url_prefix: str,
+):
+    # Local variables
+    file_metadata = {}
+    # Read the input HTML content
+    to_file = ""
+    with open(filename, "r", encoding="utf-8") as auto:
+        to_file = auto.read()
+        auto.close()
+    # Process includes lines in HTML
+    file_with_include = html_splitter.process_html_includes(
+        to_file, inputpathitem.include_path_html
+    )
+    return file_metadata
+
+
+# This function processes files specified in the `inputs` field
+# in the config.yaml file into small plain text files.
 # Includes are processed again since preprocess resolves the includes in
-# Files prefixed with _ which indicates they are not standalone
-# inputpath is optional to walk a temporary directory that has been pre-processed
-# If not, defaults to path of inputpathitem
+# files prefixed with _, which indicates they are not standalone.
+# inputpath is optional to walk a temporary directory that has been pre-processed.
+# If not, it defaults to path of inputpathitem.
 def process_files_from_input(
     product_config: ProductConfig,
     inputpathitem: Input,
     splitter: str,
-    inputpath: str = None,
+    inputpath: typing.Optional[str] = None,
 ):
     # If inputpath isn't specified assign path from item
     if inputpath is None:
         inputpath = inputpathitem.path
-    f_count = 0
+    file_count = 0
     md_count = 0
     html_count = 0
     fidl_count = 0
@@ -149,11 +403,13 @@ def process_files_from_input(
     resolved_output_path = resolve_path(product_config.output_path)
     # Pre-calculates file count
     file_count = sum(len(files) for _, _, files in os.walk(resolve_path(inputpath)))
-    progress_bar = tqdm(
+    # Set up a status bar for the terminal display.
+    progress_bar = tqdm.tqdm(
         total=file_count,
         position=0,
         bar_format="{percentage:3.0f}% | {n_fmt}/{total_fmt} | {elapsed}/{remaining}| {desc}",
     )
+    # Process each input path provided in config.yaml.
     for root, dirs, files in os.walk(resolve_path(inputpath)):
         if inputpathitem.exclude_path is not None:
             dirs[:] = [d for d in dirs if d not in inputpathitem.exclude_path]
@@ -161,216 +417,96 @@ def process_files_from_input(
             # Makes sure that URL ends in backslash
             url_prefix = end_path_backslash(inputpathitem.url_prefix)
             namespace_uuid = uuid.uuid3(uuid.NAMESPACE_DNS, url_prefix)
-        original_input = inputpathitem.path
+        # Process the files found in this input path provided in config.yaml.
         for file in files:
             # Displays status bar
             progress_bar.set_description_str(f"Processing file {file}", refresh=True)
             progress_bar.update(1)
+            # Get the full path to this input file.
             filename_to_open = os.path.join(root, file)
-            # Construct a new sub-directory for storing output plain text files
+            # Construct a new sub-directory for storing output plain text files.
             new_path = resolved_output_path + re.sub(
                 resolve_path(inputpath), "", os.path.join(root, "")
             )
             is_exist = os.path.exists(new_path)
             if not is_exist:
                 os.makedirs(new_path)
+            # Get the relative path to this input file.
             relative_path = make_relative_path(
                 file=file, root=root, inputpath=inputpath
             )
+            # Check the input file type: Markdown, FIDL, or HTML.
             if file.endswith(".md") and not file.startswith("_"):
-                md_count += 1
                 # Add filename to a list
                 file_index.append(relative_path)
-                with open(filename_to_open, "r", encoding="utf-8") as auto:
-                    # Read the input Markdown content
-                    to_file = auto.read()
-                    auto.close()
-                    # Process includes lines in Markdown
-                    file_with_include = markdown_splitter.process_markdown_includes(
-                        to_file, root
-                    )
-                    # Process include lines in HTML
-                    file_with_include = html_splitter.process_html_includes(
-                        file_with_include, inputpathitem.include_path_html
-                    )
-                    # This is an estimate of the token count
-                    page_token_estimate = returnHighestTokens(file_with_include)
-                    if splitter == "token_splitter":
-                        # Returns an array of Section objects along with a Page
-                        # Object that contains metadata
-                        (
-                            page_sections,
-                            page,
-                        ) = markdown_splitter.process_markdown_page(
-                            markdown_text=file_with_include, header_id_spaces="-"
-                        )
-                        chunk_number = 0
-                        for section in page_sections:
-                            filename_to_save = make_chunk_name(
-                                new_path=new_path,
-                                file=file,
-                                index=chunk_number,
-                                extension="md",
-                            )
-                            # Generate UUID for each plain text chunk and collect its metadata,
-                            # which will be written to the top-level `file_index.json` file.
-                            md_hash = uuid.uuid3(namespace_uuid, section.content)
-                            uuid_file = uuid.uuid3(namespace_uuid, filename_to_save)
-                            origin_uuid = uuid.uuid3(namespace_uuid, relative_path)
-                            # If no URL came from frontmatter, assign URL from config
-                            if page.URL == "":
-                                page.URL = end_path_backslash(
-                                    add_scheme_url(url=url_prefix, scheme="https")
-                                )
-                            # Strip extension of .md from url
-                            # Makes sure that relative_path starts without backslash
-                            # page.url will have backslash
-                            built_url = page.URL + start_path_no_backslash(
-                                relative_path
-                            )
-                            strip_ext_url = re.search(r"(.*)\.md$", built_url)
-                            built_url = strip_ext_url[1]
-                            # Build the valid URL for a section including header
-                            # Do not add a # if section 1
-                            if section.name_id != "" and int(section.level) != 1:
-                                built_url = built_url + "#" + section.name_id
-                            # Adds additional info so that the section can know its origin
-                            full_file_metadata[filename_to_save] = {
-                                "UUID": str(uuid_file),
-                                "origin_uuid": str(origin_uuid),
-                                "source": str(original_input),
-                                "source_file": str(relative_path),
-                                "page_title": str(section.page_title),
-                                "section_title": str(section.section_title),
-                                "section_name_id": str(section.name_id),
-                                "section_id": int(section.id),
-                                "section_level": int(section.level),
-                                "previous_id": int(section.previous_id),
-                                "URL": str(built_url),
-                                "md_hash": str(md_hash),
-                                "token_estimate": float(section.token_count),
-                                "full_token_estimate": float(page_token_estimate),
-                                "parent_tree": list(section.parent_tree),
-                                "metadata": dict(page.metadata),
-                            }
-                            with open(
-                                filename_to_save, "w", encoding="utf-8"
-                            ) as new_file:
-                                new_file.write(section.content)
-                                new_file.close()
-                            chunk_number += 1
-                    elif splitter == "process_sections":
-                        # Use a custom splitter to split into small chunks
-                        (
-                            to_file,
-                            metadata,
-                        ) = markdown_splitter.process_page_and_section_titles(to_file)
-                        to_file = markdown_splitter.process_markdown_includes(
-                            to_file, root
-                        )
-                        docs = markdown_splitter.process_document_into_sections(to_file)
-                        # doc = []
-                        chunk_number = 0
-                        for doc in docs:
-                            filename_to_save = make_chunk_name(
-                                new_path=new_path,
-                                file=file,
-                                index=chunk_number,
-                                extension="md",
-                            )
-                            # Clean up Markdown and HTML syntax
-                            content = markdown_splitter.markdown_to_text(doc)
-                            # Generate UUID for each plain text chunk and collect its metadata,
-                            # which will be written to the top-level `file_index.json` file.
-                            md_hash = uuid.uuid3(namespace_uuid, file_with_include)
-                            uuid_file = uuid.uuid3(namespace_uuid, filename_to_save)
-                            full_file_metadata[filename_to_save] = {
-                                "UUID": str(uuid_file),
-                                "source": original_input,
-                                "source_file": relative_path,
-                                "URL": url_prefix,
-                                "md_hash": str(md_hash),
-                                "metadata": metadata,
-                            }
-                            with open(
-                                filename_to_save, "w", encoding="utf-8"
-                            ) as new_file:
-                                new_file.write(content)
-                                new_file.close()
-                            chunk_number += 1
-                        auto.close()
-                    else:
-                        # Exits if no valid markdown splitter
-                        logging.error(
-                            f"Select a valid markdown_splitter option in your configuration for {product_config.product_name}\n"
-                        )
-                        exit()
+                # Increment the Markdown file count.
+                md_count += 1
+                # Process a Markdown file.
+                this_file_metadata = process_markdown_file(
+                    filename_to_open,
+                    root,
+                    inputpathitem,
+                    splitter,
+                    new_path,
+                    file,
+                    namespace_uuid,
+                    relative_path,
+                    url_prefix,
+                )
+                # Merge this file's metadata to the global metadata.
+                full_file_metadata.update(this_file_metadata)
             elif file.endswith(".fidl") and not file.startswith("_"):
-                filename_prefix = "index"
-                chunk_number = 0
-                fidl_count += 1
                 # Add filename to a list
                 file_index.append(relative_path)
-                with open(os.path.join(root, file), "r", encoding="utf-8") as auto:
-                    # Read the input FIDL file
-                    to_file = auto.read()
-                    # Split the FIDL file into a list of FIDL protocols.
-                    fidl_protocols = fidl_splitter.split_file_to_protocols(to_file)
-                    library_name = ""
-                    # Iterate the list of FIDL protocols.
-                    for fidl_protocol in fidl_protocols:
-                        # Identify the new FIDL chunk file path and name.
-                        filename_to_save = make_file_chunk_name(new_path=new_path, filename_prefix=filename_prefix, index=chunk_number)
-                        with open(filename_to_save, "w", encoding="utf-8") as new_file:
-                            new_file.write(fidl_protocol)
-                            new_file.close()
-                        chunk_number += 1
-                        # Handle metadata for the FIDL chunk
-                        md_hash = uuid.uuid3(namespace_uuid, fidl_protocol)
-                        uuid_file = uuid.uuid3(namespace_uuid, filename_to_save)
-                        origin_uuid = uuid.uuid3(namespace_uuid, relative_path)
-                        # Contruct URLs
-                        match_library = re.search(r"^Library\sname:\s+(.*)\n", fidl_protocol)
-                        if match_library:
-                            library_name = match_library.group(1)
-                        fidl_url = url_prefix + library_name
-                        full_file_metadata[filename_to_save] = {
-                            "UUID": str(uuid_file),
-                            "origin_uuid": str(origin_uuid),
-                            "source": str(original_input),
-                            "source_file": str(relative_path),
-                            "source_id": int(fidl_count),
-                            "page_title": str("None"),
-                            "section_title": str("None"),
-                            "section_name_id": str("None"),
-                            "section_id": int(1),
-                            "section_level": int(1),
-                            "previous_id": int(1),
-                            "URL": str(fidl_url),
-                            "md_hash": str(md_hash),
-                            "token_estimate": float(1.0),
-                            "full_token_estimate": float(1.0),
-                        }
+                # Increment the FIDL file count.
+                fidl_count += 1
+                # Process a FIDL protocol file.
+                this_file_metadata = process_fidl_file(
+                    filename_to_open,
+                    root,
+                    inputpathitem,
+                    splitter,
+                    new_path,
+                    file,
+                    namespace_uuid,
+                    relative_path,
+                    url_prefix,
+                )
+                # Merge this file's metadata to the global metadata.
+                full_file_metadata.update(this_file_metadata)
             elif (
                 file.endswith(".htm") or file.endswith(".html")
             ) and not file.startswith("_"):
-                html_count += 1
                 # Add filename to a list
                 file_index.append(relative_path)
-                with open(filename_to_open, "r", encoding="utf-8") as auto:
-                    # Read the input HTML content
-                    to_file = auto.read()
-                    # Process includes lines in HTML
-                    file_with_include = html_splitter.process_html_includes(
-                        to_file, inputpathitem.include_path_html
-                    )
-                    # print (to_file)
-    # Counts actually processed files
-    f_count = md_count + html_count
-    print("\nProcessed " + str(f_count) + " files from the source: " + inputpath)
+                # Increment the HTML file count.
+                html_count += 1
+                # Process a HTML file.
+                this_file_metadata = process_html_file(
+                    filename_to_open,
+                    root,
+                    inputpathitem,
+                    splitter,
+                    new_path,
+                    file,
+                    namespace_uuid,
+                    relative_path,
+                    url_prefix,
+                )
+                # Merge this file's metadata to the global metadata.
+                full_file_metadata.update(this_file_metadata)
+
+    # The processing of input files is finished.
+    # Count all processed files.
+    file_count = md_count + html_count + fidl_count
+    # Print the summary of the processed files.
+    print()
+    print("Processed " + str(file_count) + " files from the source: " + str(inputpath))
     print(str(md_count) + " Markdown files.")
     print(str(html_count) + " HTML files.")
-    return f_count, md_count, html_count, file_index, full_file_metadata
+    if fidl_count > 0:
+        print(str(fidl_count) + " FIDL files.")
+    return file_count, md_count, html_count, file_index, full_file_metadata
 
 
 # Write the recorded input variables into a file: `file_index.json`
@@ -384,7 +520,7 @@ def save_file_index_json(output_path, output_content):
 
 
 # Given a file, root, and inputpath, make a relative path
-def make_relative_path(file: str, inputpath: str, root: str = None) -> str:
+def make_relative_path(file: str, inputpath: str, root: typing.Optional[str] = None) -> str:
     file_slash = "/" + file
     if root is None:
         relative_path = os.path.relpath(file_slash, inputpath)
@@ -395,8 +531,10 @@ def make_relative_path(file: str, inputpath: str, root: str = None) -> str:
 
 # Given a path, filename_prefix, chunk index, and an optional path extension (to save chunk)
 # Create a file chunk name
-def make_file_chunk_name(new_path: str, filename_prefix: str, index: int, extension: str = "md") -> str:
-    filename_to_save = (filename_prefix + "_" + str(index) + "." + extension)
+def make_file_chunk_name(
+    new_path: str, filename_prefix: str, index: int, extension: str = "md"
+) -> str:
+    filename_to_save = filename_prefix + "_" + str(index) + "." + extension
     full_filename = os.path.join(new_path, filename_to_save)
     return full_filename
 
@@ -495,7 +633,7 @@ def process_inputs_from_product(input_product: ProductConfig, temp_process_path:
 # temp_process_path is where temporary files will be processed and then deleted
 # defaults to /tmp
 def process_all_products(
-    config_file: ReadConfig = config.ReadConfig().returnProducts(),
+    config_file: ConfigFile = config.ReadConfig().returnProducts(),
     temp_process_path: str = "/tmp",
 ):
     print(f"Starting chunker for {str(len(config_file.products))} products.\n")
diff --git a/examples/gemini/python/docs-agent/docs_agent/preprocess/populate_vector_database.py b/examples/gemini/python/docs-agent/docs_agent/preprocess/populate_vector_database.py
index ad3ed6bc6..c8829138b 100644
--- a/examples/gemini/python/docs-agent/docs_agent/preprocess/populate_vector_database.py
+++ b/examples/gemini/python/docs-agent/docs_agent/preprocess/populate_vector_database.py
@@ -14,39 +14,27 @@
 # limitations under the License.
 #
 
-"""Populate the vector database with embeddings generated from text chunks"""
+"""Populate vector databases with embeddings generated from text chunks."""
 
+import json
 import os
-import sys
 import re
-import json
-import chromadb
-import flatdict
-import uuid
-from tqdm import tqdm
-from time import sleep
-from absl import logging
-from ratelimit import limits, sleep_and_retry
+import sys
 
+from absl import logging
+import chromadb
 from chromadb.utils import embedding_functions
-from chromadb.api.types import D, Embeddings
+import flatdict
+import tqdm
 
 from docs_agent.models.google_genai import Gemini
-from docs_agent.utilities import config
-from docs_agent.utilities.config import ProductConfig, ReadConfig, Input
-from docs_agent.utilities.helpers import (
-    get_project_path,
-    resolve_path,
-    end_path_backslash,
-    add_scheme_url,
-)
 from docs_agent.preprocess.splitters import markdown_splitter
 from docs_agent.storage.google_semantic_retriever import SemanticRetriever
-
-
-# Main function
-def main():
-    process_all_products()
+from docs_agent.utilities import config
+from docs_agent.utilities.config import ConfigFile
+from docs_agent.utilities.config import ProductConfig
+from docs_agent.utilities.helpers import end_path_backslash
+from docs_agent.utilities.helpers import resolve_path
 
 
 class chromaAddSection:
@@ -58,16 +46,41 @@ def __init__(
         self.metadata = metadata
 
 
-# Read plain text files (.md) from an input dir and
-# add their content to the vector database.
-# Embeddings are generated automatically as they are added to the database.
-def populateToDbFromProduct(product_config: ProductConfig):
-    """Populates the vector database with product documentation.
+# Get the total number of files in a directory and its subdirectories.
+def get_file_count_in_a_dir(path):
+    file_count = sum(len(files) for _, _, files in os.walk(path))
+    return file_count
 
-    Args:
-        product_config: A ProductConfig object containing configuration details.
-    """
-    # Initialize Gemini object for embed function
+
+# Prepare progres bars for showing files being processed and uploaded.
+def init_progress_bars(file_count):
+    main = tqdm.tqdm(
+        total=file_count,
+        position=0,
+        bar_format="{percentage:3.0f}% | {n_fmt}/{total_fmt} | {elapsed}/{remaining} | {desc}",
+    )
+    new_file = tqdm.tqdm(position=1, desc="Total new files 0", bar_format="{desc}")
+    unchanged_file = tqdm.tqdm(
+        position=2, desc="Total unchanged files 0", bar_format="{desc}"
+    )
+    update_file = tqdm.tqdm(
+        position=3, desc="Total updated files 0", bar_format="{desc}"
+    )
+    return main, new_file, unchanged_file, update_file
+
+
+# Open a file and return its content.
+def get_file_content(full_path):
+    content_file = ""
+    with open(full_path, "r", encoding="utf-8") as auto:
+        content_file = auto.read()
+        content_file.strip()
+        auto.close()
+    return content_file
+
+
+# Initialize Gemini objects for generating embeddings.
+def init_gemini_model(product_config: ProductConfig):
     gemini_new = Gemini(models_config=product_config.models)
     # Use a chromadb function to initialize db
     embedding_function_gemini = embedding_functions.GoogleGenerativeAiEmbeddingFunction(
@@ -75,11 +88,62 @@ def populateToDbFromProduct(product_config: ProductConfig):
         model_name=product_config.models.embedding_model,
         task_type="RETRIEVAL_DOCUMENT",
     )
+    return gemini_new, embedding_function_gemini
 
-    # Temporary - This enables us to start chroma even with google_semantic_retriever
-    # returning None
+
+# Upload a text chunk to an online stroage using the Semantic Retrieval API.
+def upload_an_entry_to_a_corpus(
+    semantic, corpus_name, document_name_in_corpus, this_item, is_this_first_chunk
+):
+    document_name = document_name_in_corpus
+    # Check if a document for this chunk exists.
+    if is_this_first_chunk == True:
+        origin_uuid = ""
+        if hasattr(this_item.section, "origin_uuid"):
+            origin_uuid = this_item.section.origin_uuid
+        try:
+            # Create a new document
+            document_name = semantic.create_a_doc(
+                corpus_name=corpus_name,
+                page_title=this_item.section.page_title,
+                uuid=origin_uuid,
+            )
+        except:
+            logging.error(
+                f"Cannot create a new document using the Semantic Retrieval API: {str(this_item.section.page_title)}"
+            )
+    uuid_dict = {"UUID": this_item.section.uuid}
+    dict_with_uuid = this_item.metadata | uuid_dict
+    try:
+        # Create a new chunk
+        semantic.create_a_chunk(
+            doc_name=document_name,
+            text=this_item.section.content,
+            metadata=dict_with_uuid,
+        )
+        logging.info("Added the text chunk using the Semantic Retrieval API.")
+    except:
+        logging.error(dict_with_uuid)
+        logging.error(this_item.section)
+        logging.error("Cannot add the text chunk using the Semantic Retrieval API.")
+    return document_name
+
+
+# Read plain text files (.md) from an input dir and
+# add their content to the vector database.
+# Embeddings are generated automatically as they are added to the database.
+def populateToDbFromProduct(product_config: ProductConfig):
+    """Populates the vector database with product documentation.
+    Args:
+        product_config: A ProductConfig object containing configuration details.
+    """
+    # Initialize Gemini objects.
+    (gemini_new, embedding_function_gemini) = init_gemini_model(product_config)
+
+    # Initialize the Chroma database.
     for item in product_config.db_configs:
         if "chroma" in item.db_type:
+            logging.info("Initializing Chroma for a local storage.")
             chroma_client = chromadb.PersistentClient(
                 path=resolve_path(item.vector_db_dir)
             )
@@ -87,71 +151,91 @@ def populateToDbFromProduct(product_config: ProductConfig):
                 name=item.collection_name,
                 embedding_function=embedding_function_gemini,
             )
-        # Hacky way of getting corpus_name from other db_type configs
-        if "google_semantic_retriever" in item.db_type:
-            corpus_name = item.corpus_name
+
+    # Initialzie the Semantic Retreival API.
+    corpus_name = ""
+    all_docs = []
     if product_config.db_type == "google_semantic_retriever":
-        print("Initializing the Semantic Retrieval API for creating an online storage.")
+        logging.info("Initializing the Semantic Retrieval API for an online storage.")
         semantic = SemanticRetriever()
         for item in product_config.db_configs:
             if "google_semantic_retriever" in item.db_type:
-                if semantic.does_this_corpus_exist(item.corpus_name) == False:
-                    semantic.create_a_new_corpus(item.corpus_display, item.corpus_name)
-    index, full_index_path = load_index(input_path=product_config.output_path)
-    # Pre-calculates file count
-    file_count = sum(len(files) for _, _, files in os.walk(product_config.output_path))
-    # Prepare progress bars
-    progress_bar = tqdm(
-        total=file_count,
-        position=0,
-        bar_format="{percentage:3.0f}% | {n_fmt}/{total_fmt} | {elapsed}/{remaining} | {desc}",
-    )
-    progress_new_file = tqdm(position=1, desc="Total new files 0", bar_format="{desc}")
-    progress_unchanged_file = tqdm(
-        position=2, desc="Total unchanged files 0", bar_format="{desc}"
-    )
-    progress_update_file = tqdm(
-        position=3, desc="Total updated files 0", bar_format="{desc}"
-    )
+                corpus_name = item.corpus_name
+                if semantic.does_this_corpus_exist(corpus_name) == False:
+                    # Create a new corpus.
+                    semantic.create_a_new_corpus(item.corpus_display, corpus_name)
+                else:
+                    # Get all documents from the existing corpus.
+                    # This array is used for detecting duplicate entries.
+                    all_docs = semantic.get_all_docs(corpus_name=corpus_name)
+
+    # These variables track the resource names of documents for the Semantic Retrieval API.
+    document_name_in_corpus = ""
+    dict_document_names_in_corpus = {}
+
+    # Load the file index information from the file_index.json file.
+    (index, full_index_path) = load_index(input_path=product_config.output_path)
+
+    # Get the total number of files for the progress bar.
+    file_count = get_file_count_in_a_dir(product_config.output_path)
+    # Prepare progress bars.
+    (
+        progress_bar,
+        progress_new_file,
+        progress_unchanged_file,
+        progress_update_file,
+    ) = init_progress_bars(file_count)
+
+    # Local variables for counting files.
     total_files = 0
     updated_count = 0
     new_count = 0
     unchanged_count = 0
-    add_id_array = []
-    add_content_array = []
-    add_title_array = []
-    add_metadata_array = []
-    add_embedding_array = []
-    delete_id_array = []
+
+    # Main loop that goes through each `path` in config.yaml.
     for root, dirs, files in os.walk(product_config.output_path):
-        # Makes the output path a fully resolved path
+        # Convert `output_path` to be a fully resolved path.
         fully_resolved_output = end_path_backslash(
             resolve_path(product_config.output_path)
         )
+        # Loop through all files found in the `output_path` directory.
         for file in files:
             # Displays status bar, sleep helps to stick the progress
             progress_bar.update(1)
             progress_bar.set_description_str(f"Processing file {file}", refresh=True)
-            # Persists every nth time and if there was an actual update or added file.
-            # However, we don't need to persist, which takes time, if there are no updates.
-            # Persist is now automatic
+            # Get the full path for the file.
             full_file_name = resolve_path(os.path.join(root, "")) + file
+            # Process only files with `.md` extension.
             if file.endswith(".md"):
-                with open(os.path.join(root, file), "r", encoding="utf-8") as auto:
-                    # Extract the original filename used (without a file extension)
-                    content_file = auto.read()
-                    auto.close()
-                content_file.strip()
-                counter = 0
-                for product in index:
-                    # Returns Section object along with any additional metadata in a seperate dictionary, and a doc_title
-                    # Title is built from page title and section title
-                    # Can update progress bar with section info for debug
-                    chroma_add_item = findFileinDict(
-                        input_file_name=full_file_name,
-                        dictionary_input=index[product],
-                        content_file=content_file,
-                    )
+                # Open the file and get the content.
+                content_file = get_file_content(os.path.join(root, file))
+                # Get a Section object from the file index object.
+                chroma_add_item = findFileinDict(
+                    input_file_name=full_file_name,
+                    index_object=index,
+                    content_file=content_file,
+                )
+                # Quick fix: If the filename ends with `_##.md`, extract the file prefix
+                # Then check if this prefix exists in a local dict, which tracks document
+                # resource names for the Semantic Retrieval API call.
+                file_page_prefix = ""
+                is_this_first_chunk = False
+                match_file_page = re.search(r"(.*)_\d+\.md$", full_file_name)
+                if match_file_page:
+                    file_page_prefix = match_file_page.group(1)
+                    if file_page_prefix in dict_document_names_in_corpus:
+                        # If the prefix exists in the dict, retrieve the document resource name.
+                        document_name_in_corpus = dict_document_names_in_corpus.get(
+                            file_page_prefix
+                        )
+                    else:
+                        # if not, set the flag to indicate that a new `document` needs
+                        # to be created.
+                        is_this_first_chunk = True
+                        document_name_in_corpus = ""
+                else:
+                    # If the file is not in a group, treat it as its own document.
+                    file_page_prefix = full_file_name
                 # Skip if the file size is larger than 10000 bytes (API limit)
                 if (
                     chroma_add_item.section.content != ""
@@ -159,140 +243,62 @@ def populateToDbFromProduct(product_config: ProductConfig):
                     and chroma_add_item.section.md_hash != ""
                     and chroma_add_item.section.uuid != ""
                 ):
-                    # The query looks for the UUID, which is unique and
-                    # compares to see if the hash has changed
-                    # Extract any id whose content may have changed
-                    counter += 1
-                    # Prepare for arrays, right now use a single value array
-                    # TODO batch embedding creation
-                    if counter == 1 or counter == len(index):
-                        id_to_update = collection.get(
-                            include=["metadatas"],
-                            ids=chroma_add_item.section.uuid,
-                            where={"md_hash": {"$ne": chroma_add_item.section.md_hash}},
-                        )["ids"]
-                        id_to_not_change = collection.get(
-                            include=["metadatas"],
-                            ids=chroma_add_item.section.uuid,
-                            where={"md_hash": {"$eq": chroma_add_item.section.md_hash}},
-                        )["ids"]
-                        # Delete and Re-add content
-                        if id_to_update != []:
-                            # Add ids once to delete array, and then to add array
-                            delete_id_array.append(chroma_add_item.section.uuid)
-                            add_id_array.append(chroma_add_item.section.uuid)
-                            add_content_array.append(chroma_add_item.section.content)
-                            add_title_array.append(chroma_add_item.doc_title)
-                            add_metadata_array.append(chroma_add_item.metadata)
-                            try:
-                                add_embedding_array.append(
-                                    gemini_new.embed(
-                                        content=chroma_add_item.section.content,
-                                        task_type="RETRIEVAL_DOCUMENT",
-                                        title=chroma_add_item.doc_title,
-                                    )[0]
-                                )
-                            except:
-                                next
-                            if len(outdated) == 1 or counter == len(index):
-                                logging.info(f"{str(len(outdated))} updated files.")
-                                collection.delete(ids=oudated)
-                                # Add a new entry
-                                collection.add(
-                                    documents=add_content_array,
-                                    embeddings=add_embedding_array,
-                                    metadatas=add_metadata_array,
-                                    ids=add_id_array,
-                                )
-                                updated_count += len(id_to_update)
-                                progress_bar_updated_file.update(1)
-                                progress_bar_updated_file.set_description_str(
-                                    f"Updated files {updated_count}", refresh=True
-                                )
-                                ### Also add this text chunk using the Semantic Retrieval API
-                                if (
-                                    product_config.db_type
-                                    == "google_semantic_retriever"
-                                ):
-                                    # logging.error(f"Added a semantic chunk.")
-                                    uuid_dict = {"UUID": chroma_add_item.section.uuid}
-                                    dict_with_uuid = (
-                                        chroma_add_item.metadata | uuid_dict
-                                    )
-                                    semantic.create_a_doc_chunk(
-                                        corpus_name=corpus_name,
-                                        page_title=chroma_add_item.section.page_title,
-                                        metadata=dict_with_uuid,
-                                        text=chroma_add_item.section.content,
-                                    )
-                                    logging.info(
-                                        "Added the text chunk using the Semantic Retrieval API."
-                                    )
-                        # If section remained static
-                        elif id_to_not_change != []:
-                            qty_change = len(id_to_not_change)
-                            progress_unchanged_file.update(qty_change)
-                            unchanged_count += qty_change
-                            progress_unchanged_file.set_description_str(
-                                f"Total unchanged file {unchanged_count}",
-                                refresh=True,
-                            )
-                        else:
-                            add_id_array.append(chroma_add_item.section.uuid)
-                            add_content_array.append(chroma_add_item.section.content)
-                            add_title_array.append(chroma_add_item.doc_title)
-                            add_metadata_array.append(chroma_add_item.metadata)
-                            add_embedding_array.append(
-                                gemini_new.embed(
-                                    content=chroma_add_item.section.content,
-                                    task_type="RETRIEVAL_DOCUMENT",
-                                    title=chroma_add_item.doc_title,
-                                )[0]
+                    # Compare the text chunk entries in the local Chroma database
+                    # to check if the hash value has changed.
+                    id_to_not_change = collection.get(
+                        include=["metadatas"],
+                        ids=chroma_add_item.section.uuid,
+                        where={"md_hash": {"$eq": chroma_add_item.section.md_hash}},
+                    )["ids"]
+                    if id_to_not_change != []:
+                        # This text chunk is unchanged. Skip this text chunk.
+                        qty_change = len(id_to_not_change)
+                        progress_unchanged_file.update(qty_change)
+                        unchanged_count += qty_change
+                        progress_unchanged_file.set_description_str(
+                            f"Total unchanged file {unchanged_count}",
+                            refresh=True,
+                        )
+                    else:
+                        # Process this text chunk and store it into the databases.
+                        # Generate an embedding
+                        this_embedding = gemini_new.embed(
+                            content=chroma_add_item.section.content,
+                            task_type="RETRIEVAL_DOCUMENT",
+                            title=chroma_add_item.doc_title,
+                        )[0]
+                        # Store this text chunk entry in Chroma.
+                        collection.add(
+                            documents=[chroma_add_item.section.content],
+                            embeddings=[this_embedding],
+                            metadatas=[chroma_add_item.metadata],
+                            ids=[chroma_add_item.section.uuid],
+                        )
+                        # Update the progress bar.
+                        new_count += 1
+                        progress_new_file.update(1)
+                        progress_new_file.set_description_str(
+                            f"Total new files {new_count}", refresh=True
+                        )
+                        # Add this text chunk to the online storage.
+                        if product_config.db_type == "google_semantic_retriever":
+                            document_name = upload_an_entry_to_a_corpus(
+                                semantic,
+                                corpus_name,
+                                document_name_in_corpus,
+                                chroma_add_item,
+                                is_this_first_chunk,
                             )
-                            if len(add_id_array) == 1:
-                                collection.add(
-                                    documents=add_content_array,
-                                    embeddings=add_embedding_array,
-                                    metadatas=add_metadata_array,
-                                    ids=add_id_array,
-                                )
-                                new_count += len(add_id_array)
-                                progress_new_file.update(len(add_id_array))
-                                progress_new_file.set_description_str(
-                                    f"Total new files {new_count}", refresh=True
-                                )
-                                # Reset all arrays
-                                add_id_array = []
-                                add_content_array = []
-                                add_title_array = []
-                                add_metadata_array = []
-                                add_embedding_array = []
-                            ### Also add this text chunk using the Semantic Retrieval API
-                            if product_config.db_type == "google_semantic_retriever":
-                                uuid_dict = {"UUID": chroma_add_item.section.uuid}
-                                dict_with_uuid = chroma_add_item.metadata | uuid_dict
-                                try:
-                                    semantic.create_a_doc_chunk(
-                                        corpus_name=corpus_name,
-                                        page_title=chroma_add_item.section.page_title,
-                                        metadata=dict_with_uuid,
-                                        text=chroma_add_item.section.content,
-                                    )
-                                    logging.info(
-                                        "Added the text chunk using the Semantic Retrieval API."
-                                    )
-                                except:
-                                    logging.error(dict_with_uuid)
-                                    logging.error(chroma_add_item.section)
-                                    logging.error(
-                                        "Cannot add the text chunk using the Semantic Retrieval API."
-                                    )
+                            # Store the document resource name
+                            dict_document_names_in_corpus[
+                                file_page_prefix
+                            ] = document_name
                     total_files += 1
                 else:
                     if chroma_add_item.section.content == "":
-                        logging.info(f"Skipped {file} because the file is empty.")
+                        logging.error(f"Skipped {file} because the file is empty.")
                     else:
-                        logging.info(
+                        logging.error(
                             f"Skipped {file} because the file is is too large {str(len(chroma_add_item.section.content))}"
                         )
             # Skips logging a warning if the file being walked is the index file
@@ -311,8 +317,10 @@ def populateToDbFromProduct(product_config: ProductConfig):
     print("Total number of entries: " + str(total_files))
 
 
-def findFileinDict(input_file_name: str, dictionary_input: dict, content_file):
+def findFileinDict(input_file_name: str, index_object, content_file):
     metadata_dict_final = {}
+    for product in index_object:
+        dictionary_input = index_object[product]
     if input_file_name in dictionary_input:
         # If metadata exists, add these to a dictionary that is then
         # merged with other metadata values
@@ -348,11 +356,11 @@ def findFileinDict(input_file_name: str, dictionary_input: dict, content_file):
     return chroma_add
 
 
+# Load the file index information from the file_index.json file.
 def load_index(
     input_path: str, input_index_name: str = "file_index.json"
 ) -> tuple[dict, str]:
     """Loads the file index.
-
     Args:
         input_path: The path to the input directory.
 
@@ -377,7 +385,7 @@ def load_index(
 # temp_process_path is where temporary files will be processed and then deleted
 # defaults to /tmp
 def process_all_products(
-    config_file: ReadConfig = config.ReadConfig().returnProducts(),
+    config_file: ConfigFile = config.ReadConfig().returnProducts(),
 ):
     print(
         f"Starting to verify files to populate database for {str(len(config_file.products))} products.\n"
@@ -400,7 +408,3 @@ def extract_extra_metadata(input_dictionary):
     )
     metadata_dict_extra = dict(metadata_dict_extra)
     return metadata_dict_extra
-
-
-if __name__ == "__main__":
-    main()
diff --git a/examples/gemini/python/docs-agent/docs_agent/preprocess/splitters/fidl_splitter.py b/examples/gemini/python/docs-agent/docs_agent/preprocess/splitters/fidl_splitter.py
index 4ea84779b..8b7322d2b 100644
--- a/examples/gemini/python/docs-agent/docs_agent/preprocess/splitters/fidl_splitter.py
+++ b/examples/gemini/python/docs-agent/docs_agent/preprocess/splitters/fidl_splitter.py
@@ -16,10 +16,80 @@
 
 import re
 import os
+from absl import logging
 
+
+# Get the byte size of lines.
+def get_byte_size(lines):
+    buffer_size = 0
+    for line in lines:
+        buffer_size += len(line.encode("utf-8"))
+    return buffer_size
+
+
+# Prepare a FIDL protocol into a text chunk to be stored.
+def construct_a_chunk(library_name: str, protocol_name: str, lines):
+    content_to_store = ""
+    content_to_store += "Library name: " + library_name + "\n"
+    content_to_store += "Protocol name: " + protocol_name + "\n\n"
+    content_to_store += (
+        "In Fuchsia's "
+        + library_name
+        + " FIDL library, the "
+        + protocol_name
+        + " protocol interface is defined as the following:\n\n"
+    )
+    content_to_store += "<code>\n"
+    for line in lines:
+        content_to_store += line + "\n"
+    content_to_store += "</code>\n"
+    return content_to_store
+
+
+# Divide a large protocol into two text chunks.
+def divide_a_protocol(lines):
+    half_point = len(lines) // 2
+    first_half = lines[:half_point]
+    second_half = lines[half_point:]
+    return first_half, second_half
+
+
+# Recursively process a FIDL protocol into text chunks.
+def construct_chunks(library_name: str, protocol_name: str, lines):
+    contents = []
+    buffer_size = get_byte_size(lines)
+    if int(buffer_size) > 6000:
+        # If the protocol is larget than 6KB, divide it into two.
+        logging.info(
+            "Found a text chunk ("
+            + str(protocol_name)
+            + ") is greater than 6KB (size: "
+            + str(buffer_size)
+            + ")."
+        )
+        (first_half, second_half) = divide_a_protocol(lines)
+        first_content = construct_chunks(library_name, protocol_name, first_half)
+        second_content = construct_chunks(library_name, protocol_name, second_half)
+        contents += first_content
+        contents += second_content
+    else:
+        # Prepare a text chunk that describes a FIDL protocol.
+        content = construct_a_chunk(library_name, protocol_name, lines)
+        logging.info(
+            "Created a text chunk for "
+            + str(protocol_name)
+            + " (size: "
+            + str(buffer_size)
+            + ")."
+        )
+        contents.append(content)
+    return contents
+
+
+# Split a FIDL file into protocols as text chunks.
 def split_file_to_protocols(this_file):
     protocols = []
-    line_buffer = ""
+    line_buffer = []
     protocol_name = ""
     library_name = ""
     index = 0
@@ -30,35 +100,30 @@ def split_file_to_protocols(this_file):
         match_new_line = re.search(r"^\s*$", line)
         match_end_bracket = re.search(r"^};", line)
         if match_protocol:
-            #print("MATCHED [Protocol]: " + match_protocol.group(1))
+            # print("MATCHED [Protocol]: " + match_protocol.group(1))
             protocol_name = match_protocol.group(1)
-            line_buffer += line + "\n"
+            line_buffer.append(line)
         elif match_library:
-            #print("MATCHED [Library]: " + match_library.group(1))
+            # print("MATCHED [Library]: " + match_library.group(1))
             library_name = match_library.group(1)
         elif match_comment:
-            #print("MATCHED [Comment]: " + match_comment.group(1))
-            line_buffer += line + "\n"
+            # print("MATCHED [Comment]: " + match_comment.group(1))
+            line_buffer.append(line)
         elif match_new_line:
-            #print("MATCHED [New line]")
-            line_buffer += line + "\n"
+            # print("MATCHED [New line]")
+            line_buffer.append(line)
         elif match_end_bracket:
-            #print("MATCHED [End bracket]")
-            line_buffer += line + "\n"
-            content_to_store = "Library name: " + library_name + "\n"
-            content_to_store += "Protocol name: " + protocol_name + "\n\n"
-            content_to_store += "In Fuchsia's " + library_name + " FIDL library, the " + protocol_name + " protocol interface is defined as the following:\n\n"
-            content_to_store += "<code>\n"
-            content_to_store += line_buffer.strip() + "\n"
-            content_to_store += "</code>\n"
+            # print("MATCHED [End bracket]")
+            line_buffer.append(line)
             if library_name != "" and protocol_name != "":
-                protocols.append(content_to_store)
-            # Clear the line butter when an end bracket is found.
-            line_buffer = ""
-            # Clear the protocol name when an end bracket is found.
+                # Prepre a captured FIDL protocl into small text chunks.
+                contents = construct_chunks(library_name, protocol_name, line_buffer)
+                for content in contents:
+                    protocols.append(content)
+            # Clear the line butter and protocol name when an end bracket is found.
+            line_buffer.clear()
             protocol_name = ""
         else:
-            line_buffer += line + "\n"
+            line_buffer.append(line)
         index += 0
     return protocols
-
diff --git a/examples/gemini/python/docs-agent/docs_agent/preprocess/splitters/markdown_splitter.py b/examples/gemini/python/docs-agent/docs_agent/preprocess/splitters/markdown_splitter.py
index 0e8834f79..fc8f84292 100644
--- a/examples/gemini/python/docs-agent/docs_agent/preprocess/splitters/markdown_splitter.py
+++ b/examples/gemini/python/docs-agent/docs_agent/preprocess/splitters/markdown_splitter.py
@@ -14,7 +14,8 @@
 # limitations under the License.
 #
 
-from markdown import markdown
+import typing
+import markdown
 import bs4
 import re, os
 from absl import logging
@@ -35,10 +36,10 @@ def __init__(
         parent_tree: list[int],
         token_count: float,
         content: str,
-        url: str = None,
-        origin_uuid: str = None,
-        md_hash: str = None,
-        uuid: str = None,
+        url: typing.Optional[str] = None,
+        origin_uuid: typing.Optional[str] = None,
+        md_hash: typing.Optional[str] = None,
+        uuid: typing.Optional[str] = None,
     ):
         self.id = id
         self.name_id = name_id
@@ -114,7 +115,7 @@ def createChunkTitle(self):
         return doc_title
 
     def return_id(self):
-        return self.section_id
+        return self.id
 
 
 def DictionarytoSection(metadata: dict) -> Section:
@@ -191,7 +192,7 @@ def DictionarytoSection(metadata: dict) -> Section:
 
 
 class Page:
-    def __init__(self, title: str, URL: str, section_count: int, metadata: dict = None):
+    def __init__(self, title: str, URL: str, section_count: int, metadata: typing.Optional[dict] = None):
         self.title = title
         self.URL = URL
         self.section_count = section_count
@@ -210,7 +211,7 @@ def markdown_to_text(markdown_string):
     # Remove <!-- --> lines in Markdown
     markdown_string = re.sub(r"<\!--(.*?)-->", "", markdown_string)
     # md -> html -> text since BeautifulSoup can extract text cleanly
-    html = markdown(markdown_string)
+    html = markdown.markdown(markdown_string)
     # Extract text
     soup = bs4.BeautifulSoup(html, "html.parser")
     text = "".join(soup.findAll(string=True))
@@ -535,7 +536,7 @@ def process_page_and_section_titles(markdown_text):
 # three levels of Markdown headings (#, ##, and ###) into just a single #.
 def process_document_into_sections(markdown_text):
     sections = []
-    buffer = ""
+    buffer = []
     first_section = True
     for line in markdown_text.split("\n"):
         if line.startswith("#"):
@@ -550,9 +551,57 @@ def process_document_into_sections(markdown_text):
                 else:
                     # When a new `#` is detected, store the text in `buffer` into
                     # an array entry and clear the buffer for the next section.
-                    sections.append(buffer)
-                    buffer = ""
-        buffer += line + "\n"
+                    contents = construct_chunks(buffer)
+                    for content in contents:
+                        sections.append(content)
+                    buffer.clear()
+        buffer.append(line)
     # Add the last section on the page.
-    sections.append(buffer)
+    content = convert_array_to_buffer(buffer)
+    sections.append(content)
     return sections
+
+
+# Process an array of Markdwon text into an array of string buffers
+# whose size is smaller than 6KB.
+def construct_chunks(lines):
+    contents = []
+    buffer_size = get_byte_size(lines)
+    if int(buffer_size) > 6000:
+        # If the protocol is larget than 6KB, divide it into two.
+        logging.info(
+            "Found a text chunk greater than 6KB (size: " + str(buffer_size) + ")."
+        )
+        (first_half, second_half) = divide_an_array(lines)
+        first_content = construct_chunks(first_half)
+        second_content = construct_chunks(second_half)
+        contents += first_content
+        contents += second_content
+    else:
+        chunk = convert_array_to_buffer(lines)
+        contents.append(chunk)
+    return contents
+
+# Convert an array into a string buffer.
+def convert_array_to_buffer(lines):
+   content = ""
+   for line in lines:
+        content += line + "\n"
+   return content
+
+
+# Get the byte size of lines.
+def get_byte_size(lines):
+    buffer_size = 0
+    for line in lines:
+        buffer_size += len(line.encode("utf-8"))
+    return buffer_size
+
+
+# Divide a large array into two arrays.
+def divide_an_array(lines):
+    half_point = len(lines) // 2
+    first_half = lines[:half_point]
+    second_half = lines[half_point:]
+    return first_half, second_half
+
diff --git a/examples/gemini/python/docs-agent/docs_agent/storage/chroma.py b/examples/gemini/python/docs-agent/docs_agent/storage/chroma.py
index a5e69b646..de15005bf 100644
--- a/examples/gemini/python/docs-agent/docs_agent/storage/chroma.py
+++ b/examples/gemini/python/docs-agent/docs_agent/storage/chroma.py
@@ -20,16 +20,15 @@
 import os
 import string
 import shutil
+import typing
 
 from absl import logging
 import chromadb
-from chromadb.config import Settings
 from chromadb.utils import embedding_functions
 from chromadb.api.models import Collection
 from chromadb.api.types import QueryResult
 
 from docs_agent.models.palm import PaLM
-from docs_agent.preprocess.splitters import markdown_splitter
 from docs_agent.preprocess.splitters.markdown_splitter import Section as Section
 from docs_agent.postprocess.docs_retriever import FullPage as FullPage
 from docs_agent.utilities.helpers import resolve_path, parallel_backup_dir
@@ -147,7 +146,7 @@ def __init__(self, result: QueryResult, index: int) -> None:
         self.metadata = result["metadatas"][0][index]
         self.distance = result["distances"][0][index]
 
-    def format(self, format_type: Format, ref_index: int = None):
+    def format(self, format_type: Format, ref_index: typing.Optional[int] = None):
         d = {
             "document": self.document.strip(),
             "ref_index": ref_index,
@@ -199,7 +198,7 @@ def fetch_at_formatted(self, index, format_type: Format):
 class ChromaCollection:
     """Chroma collection wrapper"""
 
-    def __init__(self, collection: Collection, embedding_function) -> None:
+    def __init__(self, collection, embedding_function) -> None:
         self.collection = collection
         self.embedding_function = embedding_function
 
@@ -228,31 +227,35 @@ class SectionDB(Enum):
 
     def decodeSection(self):
         if self.SECTION_ID:
-            section_id = self.SECTION_ID
+            section_id = self.SECTION_ID.value
         else:
             section_id = ""
         if self.SECTION_NAME_ID:
-            section_name_id = self.SECTION_NAME_ID
+            section_name_id = self.SECTION_NAME_ID.value
         else:
             section_name_id = ""
         if self.SECTION_TITLE:
-            section_title = self.SECTION_TITLE
+            section_title = self.SECTION_TITLE.value
         else:
             section_title = ""
         if self.SECTION_LEVEL:
-            section_level = self.SECTION_LEVEL
+            section_level = self.SECTION_LEVEL.value
         else:
             section_level = ""
         if self.PREVIOUS_ID:
-            previous_id = self.PREVIOUS_ID
+            previous_id = self.PREVIOUS_ID.value
         else:
             previous_id = ""
         if self.PARENT_TREE:
-            parent_tree = self.PARENT_TREE
+            parent_tree = self.PARENT_TREE.value
+            parent_tree_str = str(parent_tree).strip("[]").split(",")
+            parent_tree = []
+            for num in parent_tree_str:
+                parent_tree.append(int(num))
         else:
             parent_tree = ""
-        if self.token_estimate:
-            token_estimate = self.token_estimate
+        if self.TOKEN_ESTIMATE:
+            token_estimate = self.TOKEN_ESTIMATE.value
         else:
             token_estimate = ""
         if self.CONTENT:
@@ -260,14 +263,15 @@ def decodeSection(self):
         else:
             content = ""
         section = Section(
-            section_id,
-            section_name_id,
-            section_title,
-            section_level,
-            previous_id,
-            parent_tree,
-            token_estimate,
-            content,
+            id=int(section_id),
+            name_id=str(section_name_id),
+            section_title=str(section_title),
+            page_title="",
+            level=int(section_level),
+            previous_id=int(previous_id),
+            parent_tree=parent_tree,
+            token_count=float(token_estimate),
+            content=str(content),
         )
         return section
 
@@ -319,13 +323,13 @@ def __init__(self, result: QueryResult, index: int) -> None:
         self.distance = result["distances"][0][index]
         self.id = result["ids"][0][index]
 
-    def __str__(self) -> None:
+    def __str__(self):
         return f"This is a section with the following properties:\n\
 Section ID: {SectionDB.SECTION_ID}\n\
 Section Title: {SectionDB.SECTION_TITLE}\n\
 Section URL: {SectionDB.URL}\n"
 
-    def format(self, format_type: SectionDB, ref_index: int = None):
+    def format(self, format_type: SectionDB, ref_index: typing.Optional[int] = None):
         d = {
             "content": self.document.strip(),
             "ref_index": ref_index,
@@ -357,7 +361,7 @@ def list_collections(self):
 
     # Returns output_dir if backup was successful, None if it failed
     # Output dir can only be a child to chroma_dir
-    def backup_chroma(self, chroma_dir: str, output_dir: str = None):
+    def backup_chroma(self, chroma_dir: str, output_dir: typing.Optional[str] = None):
         try:
             chroma_dir = resolve_path(chroma_dir)
             if output_dir == None:
@@ -385,13 +389,13 @@ def get_collection(self, name, embedding_function=None, embedding_model=None):
             embedding_model = collection.metadata.get("embedding_model", None)
             if embedding_model is None:
                 # If embedding_model is not found in the metadata,
-                # use `models/embedding-gecko-001` by default.
+                # use `models/embedding-001` by default.
                 logging.info(
                     "Embedding model is not specified in the metadata of "
-                    "the collection %s. Using the default PaLM embedding model.",
+                    "the collection %s. Using the default embedding model: models/embedding-001",
                     name,
                 )
-                embedding_model = "models/embedding-gecko-001"
+                embedding_model = "models/embedding-001"
         if embedding_model == "local/all-mpnet-base-v2":
             base_dir = os.path.dirname(os.path.abspath(__file__))
             local_model_dir = os.path.join(base_dir, "models/all-mpnet-base-v2")
@@ -425,7 +429,7 @@ def get_collection(self, name, embedding_function=None, embedding_model=None):
 class ChromaCollectionEnhanced:
     """Chroma collection wrapper"""
 
-    def __init__(self, collection: Collection, embedding_function) -> None:
+    def __init__(self, collection, embedding_function) -> None:
         self.collection = collection
         self.embedding_function = embedding_function
 
@@ -562,8 +566,8 @@ def returnDBObjListGet(self):
     def fetch_nearest(self):
         return ChromaSectionDBItem(self.result, 0)
 
-    def fetch_nearest_formatted(self, format_type: Format):
+    def fetch_nearest_formatted(self, format_type: SectionDB):
         return self.fetch_nearest().format(format_type)
 
-    def return_response(self):
-        return ChromaSectionDBItem.returnSection(self)
+    # def return_response(self):
+    #     return ChromaSectionDBItem.returnSection(self)
diff --git a/examples/gemini/python/docs-agent/docs_agent/storage/google_semantic_retriever.py b/examples/gemini/python/docs-agent/docs_agent/storage/google_semantic_retriever.py
index 198830651..d95c180cf 100644
--- a/examples/gemini/python/docs-agent/docs_agent/storage/google_semantic_retriever.py
+++ b/examples/gemini/python/docs-agent/docs_agent/storage/google_semantic_retriever.py
@@ -18,7 +18,7 @@
 
 import google.ai.generativelanguage as glm
 from absl import logging
-import sys
+import typing
 
 
 class SemanticRetriever:
@@ -33,28 +33,28 @@ def get_corpus(self, corpus_name: str):
         response = self.retriever_service_client.get_corpus(get_corpus_request)
         return response
 
-    def get_document(self, document_name: str, metadata):
-        get_document_request = glm.GetDocumentRequest(name=corpus_name)
-        try:
-            response = self.retriever_service_client.get_document(get_document_request)
-            return response
-        except:
-            logging.error(
-                f"Document {document_name} does not exist or you do not have permissions"
-            )
+    # def get_document(self, document_name: str, metadata):
+    #     get_document_request = glm.GetDocumentRequest(name=corpus_name)
+    #     try:
+    #         response = self.retriever_service_client.get_document(get_document_request)
+    #         return response
+    #     except:
+    #         logging.error(
+    #             f"Document {document_name} does not exist or you do not have permissions"
+    #         )
 
-    def get_chunk(self, chunk_name: str, metadata):
-        get_chunk_request = glm.GetChunkRequest(name=corpus_name)
-        try:
-            response = self.retriever_service_client.get_chunk(get_chunk_request)
-            return response
-        except:
-            logging.error(
-                f"Chunk {chunk_name} does not exist or you do not have permissions"
-            )
+    # def get_chunk(self, chunk_name: str, metadata):
+    #     get_chunk_request = glm.GetChunkRequest(name=corpus_name)
+    #     try:
+    #         response = self.retriever_service_client.get_chunk(get_chunk_request)
+    #         return response
+    #     except:
+    #         logging.error(
+    #             f"Chunk {chunk_name} does not exist or you do not have permissions"
+    #         )
 
-    def list_existing_corpora(self):
-        corpora_list = glm.ListCorporaRequest()
+    def list_existing_corpora(self, page_token: str = ""):
+        corpora_list = glm.ListCorporaRequest(page_size=20, page_token=page_token)
         response = self.retriever_service_client.list_corpora(corpora_list)
         return response
 
@@ -90,16 +90,17 @@ def create_a_doc(
         self,
         corpus_name: str,
         page_title: str,
-        page_url: str = None,
-        metadata: dict = None,
+        page_url: typing.Optional[str] = None,
+        uuid: typing.Optional[str] = None,
+        metadata: typing.Optional[dict] = None,
     ):
         document_resource_name = ""
         try:
             # Create a new document with a custom display name.
             example_document = glm.Document(display_name=page_title)
             # Add metadata.
+            document_metadata = []
             if metadata is not None:
-                document_metadata = []
                 for key_dict, value_dict in metadata.items():
                     if isinstance(value_dict, int) or isinstance(value_dict, float):
                         document_metadata.append(
@@ -115,12 +116,13 @@ def create_a_doc(
                                 key=key_dict, string_value=str(value_dict)
                             )
                         )
+                example_document.custom_metadata.extend(document_metadata)
             else:
-                if page_url is not None:
+                if uuid is not None and uuid != "":
                     document_metadata = [
-                        glm.CustomMetadata(key="url", string_value=page_url)
+                        glm.CustomMetadata(key="uuid", string_value=str(uuid))
                     ]
-            example_document.custom_metadata.extend(document_metadata)
+                    example_document.custom_metadata.extend(document_metadata)
             # Make the request
             create_document_request = glm.CreateDocumentRequest(
                 parent=corpus_name, document=example_document
@@ -131,20 +133,26 @@ def create_a_doc(
             # Set the `document_resource_name` for subsequent sections.
             document_resource_name = create_document_response.name
         except:
+            logging.error(f"Cannot create a new doucment: {page_title}")
+            exit(1)
+        return document_resource_name
+
+    def retrieve_a_doc(self, document_resource_name: str):
+        response = []
+        try:
             get_document_request = glm.GetDocumentRequest(name=document_resource_name)
             # Make the request
-            get_document_response = self.retriever_service_client.get_document(
-                get_document_request
-            )
-            document_resource_name = get_document_response.name
-        return document_resource_name
+            response = self.retriever_service_client.get_document(get_document_request)
+        except:
+            logging.error(f"Cannot retrieve a doucment: {document_resource_name}")
+        return response
 
     def create_a_chunk(
-        self, doc_name, text, metadata: dict = None, page_url: str = None
+        self, doc_name, text, metadata, page_url: typing.Optional[str] = None
     ):
         response = ""
+        document_metadata = []
         if metadata is not None:
-            document_metadata = []
             for key_dict, value_dict in metadata.items():
                 if isinstance(value_dict, int) or isinstance(value_dict, float):
                     document_metadata.append(
@@ -208,8 +216,8 @@ def create_a_doc_chunk(
         corpus_name: str,
         page_title: str,
         text,
-        page_url: str = None,
-        metadata: dict = None,
+        page_url: typing.Optional[str] = None,
+        metadata: typing.Optional[dict] = None,
     ):
         try:
             doc_name = self.create_a_doc(
@@ -230,6 +238,52 @@ def create_a_doc_chunk(
             logging.error("Error in creaing a doc chunk: " + page_title)
             return None
 
+    def get_all_docs(self, corpus_name: str, print_output: bool = False):
+        all_docs = []
+        try:
+            request = glm.ListDocumentsRequest(parent=corpus_name, page_size=20)
+            response = self.retriever_service_client.list_documents(request)
+            index = 0
+            for docs in response.documents:
+                if print_output:
+                    index += 1
+                    print(f"\nDocument # {index}")
+                    print(f"Name: {docs.name}")
+                    print(f"Display name: {docs.display_name}")
+                    metadata = docs.custom_metadata
+                    for item in metadata:
+                        if item.key == "uuid":
+                            print(f"uuid: {item.string_value}")
+                        elif item.key == "md_hash":
+                            print(f"md_hash: {item.string_value}")
+                all_docs.append(docs)
+            while (
+                hasattr(response, "next_page_token") and response.next_page_token != ""
+            ):
+                request = glm.ListDocumentsRequest(
+                    parent=corpus_name,
+                    page_size=20,
+                    page_token=response.next_page_token,
+                )
+                response = self.retriever_service_client.list_documents(request)
+                for docs in response.documents:
+                    if print_output:
+                        index += 1
+                        print(f"\nDocument # {index}")
+                        print(f"Name: {docs.name}")
+                        print(f"Display name: {docs.display_name}")
+                        metadata = docs.custom_metadata
+                        for item in metadata:
+                            if item.key == "uuid":
+                                print(f"uuid: {item.string_value}")
+                            elif item.key == "md_hash":
+                                print(f"md_hash: {item.string_value}")
+                    all_docs.append(docs)
+            return all_docs
+        except:
+            logging.error("Error in listing all docs: " + corpus_name)
+            return all_docs
+
     def share_a_corpus(self, corpus_name: str, email: str, role: str):
         shared_user_email = email
         user_type = "USER"
@@ -271,4 +325,4 @@ def delete_permission(self, permission_name: str):
         delete_permission_response = self.permission_service_client.delete_permission(
             request
         )
-        return delete_permission_response
+        return delete_permission_response
\ No newline at end of file
diff --git a/examples/gemini/python/docs-agent/docs_agent/utilities/config.py b/examples/gemini/python/docs-agent/docs_agent/utilities/config.py
index 9031e821a..c56456da2 100644
--- a/examples/gemini/python/docs-agent/docs_agent/utilities/config.py
+++ b/examples/gemini/python/docs-agent/docs_agent/utilities/config.py
@@ -19,7 +19,8 @@
 import os
 import sys
 import yaml
-import pathlib
+import typing
+
 from absl import logging
 from docs_agent.utilities.helpers import get_project_path
 
@@ -30,12 +31,12 @@ def __init__(
         # Currently 'chroma' or 'google_semantic_retriever'
         db_type: str,
         # These for 'chroma'
-        vector_db_dir: str = None,
-        collection_name: str = None,
+        vector_db_dir: typing.Optional[str] = None,
+        collection_name: typing.Optional[str] = None,
         # These for 'google_semantic_retriever'
-        corpus_name: str = None,
+        corpus_name: typing.Optional[str] = None,
         # Only used when creating a corpus
-        corpus_display: str = None,
+        corpus_display: typing.Optional[str] = None,
     ):
         self.db_type = db_type
         self.vector_db_dir = vector_db_dir
@@ -88,8 +89,8 @@ def returnDbConfigs(self) -> list[DbConfig]:
 
     def return_chroma_db(self):
         for item in self.input_list:
-            if item.vector_db_dir:
-                return item.vector_db_dir
+            if item["vector_db_dir"]:
+                return item["vector_db_dir"]
             else:
                 return None
 
@@ -101,9 +102,9 @@ class Input:
     def __init__(
         self,
         path: str,
-        url_prefix: str = None,
-        include_path_html: str = None,
-        exclude_path: str = None,
+        url_prefix: typing.Optional[str] = None,
+        include_path_html: typing.Optional[str] = None,
+        exclude_path: typing.Optional[str] = None,
     ):
         self.path = path
         self.url_prefix = url_prefix
@@ -149,10 +150,10 @@ def __init__(
         self,
         language_model: str,
         embedding_model: str,
-        api_endpoint: str = None,
-        api_key: str = None,
-        embedding_api_call_limit: int = None,
-        embedding_api_call_period: int = None,
+        api_endpoint: typing.Optional[str] = None,
+        api_key: typing.Optional[str] = None,
+        embedding_api_call_limit: typing.Optional[int] = None,
+        embedding_api_call_period: typing.Optional[int] = None,
     ):
         self.language_model = language_model
         self.embedding_model = embedding_model
@@ -224,11 +225,20 @@ class Conditions:
     def __init__(
         self,
         condition_text: str,
-        fact_check_question: str = None,
-        model_error_message: str = None,
+        fact_check_question: typing.Optional[str] = None,
+        model_error_message: typing.Optional[str] = None,
     ):
-        default_fact_check_question: "Can you compare the text below to the information provided in this prompt above and write a short message that warns the readers about which part of the text they should consider fact-checking? (Please keep your response concise, focus on only one important item, but DO NOT USE BOLD TEXT IN YOUR RESPONSE.)"
-        default_model_error_message: "Gemini is not able to answer this question at the moment. Rephrase the question and try asking again."
+        default_fact_check_question = (
+            "Can you compare the text below to the information provided in this"
+            " prompt above and write a short message that warns the readers"
+            " about which part of the text they should consider"
+            " fact-checking? (Please keep your response concise, focus on only"
+            " one important item, but DO NOT USE BOLD TEXT IN YOUR RESPONSE.)"
+        )
+        default_model_error_message = (
+            "Gemini is not able to answer this question at the moment."
+            " Rephrase the question and try asking again."
+        )
         self.condition_text = condition_text
         if fact_check_question is None:
             self.fact_check_question = default_fact_check_question
@@ -281,14 +291,15 @@ def __init__(
         product_name: str,
         models: Models,
         output_path: str,
-        db_configs: DbConfig,
+        db_configs: list[DbConfig],
         inputs: list[Input],
         conditions: Conditions,
-        log_level: str = None,
-        docs_agent_config: str = None,
+        log_level: typing.Optional[str] = None,
+        docs_agent_config: typing.Optional[str] = None,
         markdown_splitter: str = "token_splitter",
         db_type: str = "chroma",
         app_mode: str = "web",
+        enable_show_logs: str = "False",
     ):
         self.product_name = product_name
         self.docs_agent_config = docs_agent_config
@@ -301,6 +312,7 @@ def __init__(
         self.log_level = log_level
         self.inputs = inputs
         self.app_mode = app_mode
+        self.enable_show_logs = enable_show_logs
 
     def __str__(self):
         # Extracts the list of Inputs
@@ -316,6 +328,7 @@ def __str__(self):
         return f"Product: {self.product_name}\n\
 Docs Agent config: {self.docs_agent_config}\n\
 App mode: {self.app_mode}\n\
+Enable show logs: {self.enable_show_logs}\n\
 Markdown splitter: {self.markdown_splitter}\n\
 Database type: {self.db_type}\n\
 Output path: {self.output_path}\n\
@@ -335,9 +348,9 @@ def __init__(
     ):
         self.products = products
 
-    def __str__():
+    def __str__(self):
         output = ["Products:"]
-        for item in config_file:
+        for item in self.products:
             output.append(item)
         return "\n".join(output)
 
@@ -356,10 +369,11 @@ class ReadConfig:
     def __init__(
         self, yaml_path: str = os.path.join(get_project_path(), "config.yaml")
     ):
+        self.yaml_path = yaml_path
         try:
             with open(yaml_path, "r", encoding="utf-8") as inp_yaml:
                 self.config_values = yaml.safe_load(inp_yaml)
-                self.yaml_path = yaml_path
+                # self.yaml_path = yaml_path
         except FileNotFoundError:
             logging.error(f"The config file {self.yaml_path} does not exist.")
             # Exits the scripts if there is no valid config file
@@ -368,7 +382,7 @@ def __init__(
     def __str__(self):
         return self.yaml_path
 
-    def returnProducts(self, product: str = None) -> list[ConfigFile]:
+    def returnProducts(self, product: typing.Optional[str] = None) -> ConfigFile:
         products = []
         try:
             for item in self.config_values["configs"]:
@@ -379,7 +393,7 @@ def returnProducts(self, product: str = None) -> list[ConfigFile]:
                     logging.error(f"Your configuration is missing a {error}")
                     return sys.exit()
                 # Set the default value of `app_mode` to "web"
-                supported_app_modes = ["web", "experimental", "widget"]
+                supported_app_modes = ["web", "experimental", "widget", "1.5"]
                 try:
                     app_mode = item["app_mode"]
                 except KeyError:
@@ -387,6 +401,10 @@ def returnProducts(self, product: str = None) -> list[ConfigFile]:
                 if app_mode not in supported_app_modes:
                     logging.error(f"Your configuration is using an invalid mode: {app_mode}. Valid modes are {supported_app_modes}")
                     return sys.exit(1)
+                try:
+                    enable_show_logs = item["enable_show_logs"]
+                except KeyError:
+                    enable_show_logs = "False"
                 try:
                     product_config = ProductConfig(
                         product_name=item["product_name"],
@@ -400,23 +418,28 @@ def returnProducts(self, product: str = None) -> list[ConfigFile]:
                         conditions=item["conditions"],
                         inputs=item["inputs"],
                         app_mode=app_mode,
+                        enable_show_logs=enable_show_logs,
                     )
                     # This is done for keys with children
                     # Inputs
-                    new_inputs = ReadInputs(product_config.inputs).returnInputs()
+                    new_inputs = ReadInputs(
+                        input_list=item["inputs"]
+                    ).returnInputs()
                     product_config.inputs = new_inputs
                     product_config.inputs = new_inputs
                     # Conditions
                     new_conditions = ReadConditions(
-                        product_config.conditions
+                        input_list=item["conditions"]
                     ).returnConditions()
                     product_config.conditions = new_conditions
                     # Models
-                    new_models = ReadModels(product_config.models).returnModels()
+                    new_models = ReadModels(
+                        input_list=item["models"]
+                    ).returnModels()
                     product_config.models = new_models
                     # DbConfigs
                     new_db_configs = ReadDbConfigs(
-                        product_config.db_configs
+                        input_list=item["db_configs"]
                     ).returnDbConfigs()
                     product_config.db_configs = new_db_configs
                     # Append
@@ -452,7 +475,7 @@ def returnProducts(self, product: str = None) -> list[ConfigFile]:
 
 
 # Function to make using common_options simpler
-def return_config_and_product(config_file: str = None, product: list[str] = [""]):
+def return_config_and_product(config_file: typing.Optional[str] = None, product: list[str] = [""]):
     if config_file is None:
         loaded_config = ReadConfig()
     else:
diff --git a/examples/gemini/python/docs-agent/docs_agent/utilities/helpers.py b/examples/gemini/python/docs-agent/docs_agent/utilities/helpers.py
index 8d316a73b..2e51d777f 100644
--- a/examples/gemini/python/docs-agent/docs_agent/utilities/helpers.py
+++ b/examples/gemini/python/docs-agent/docs_agent/utilities/helpers.py
@@ -19,6 +19,7 @@
 import urllib, os
 from flask import url_for
 import bs4
+import typing
 from pathlib import Path, PurePath
 import markdown
 
@@ -29,7 +30,7 @@ def get_project_path() -> Path:
 
 
 # Function to resolve path. If no base_dir is specified, use the project root
-def resolve_path(rel_or_abs_path: str, base_dir: str = get_project_path()):
+def resolve_path(rel_or_abs_path: str, base_dir: Path = get_project_path()):
     path = rel_or_abs_path.strip()
     if path.startswith("/"):
         return path
@@ -123,8 +124,8 @@ def build_list_html_links(
     section_titles: list,
     page_titles: list,
     distances: list,
-    section_content: list = None,
-    max_count: int = None,
+    section_content: typing.Optional[list] = None,
+    max_count: typing.Optional[int] = None,
 ):
     if max_count == None:
         max_count = len(urls)
diff --git a/examples/gemini/python/docs-agent/helpme.sh b/examples/gemini/python/docs-agent/helpme.sh
new file mode 100755
index 000000000..96e0716e1
--- /dev/null
+++ b/examples/gemini/python/docs-agent/helpme.sh
@@ -0,0 +1,25 @@
+#!/bin/bash
+
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# Check if the POETRY_ACTIVE environment variable is set
+if [ -z "$POETRY_ACTIVE" ]; then
+   cd $HOME/docs-agent && poetry run agent helpme $@
+else
+   agent helpme $@
+fi
+
diff --git a/examples/gemini/python/docs-agent/poetry.lock b/examples/gemini/python/docs-agent/poetry.lock
index 18f35cecf..4992b9b55 100644
--- a/examples/gemini/python/docs-agent/poetry.lock
+++ b/examples/gemini/python/docs-agent/poetry.lock
@@ -46,29 +46,37 @@ trio = ["trio (>=0.23)"]
 
 [[package]]
 name = "array-record"
-version = "0.5.0"
+version = "0.5.1"
 description = "A file format that achieves a new frontier of IO efficiency"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "array_record-0.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7940fda50bbd1ac6d5f0c3c3d4c885dc886bfb3622a123a8e8fd798582303d8a"},
-    {file = "array_record-0.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ac09a423710864f6225ebc45a307c30b419fde4470d2e5f48ba2b1e99bbe84d3"},
-    {file = "array_record-0.5.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:07330e56e7bb1354b5fb9f974dc3c42e4a3cd6e8dcf4c6e6a8b84db14ee9a8ed"},
+    {file = "array_record-0.5.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b9f2e304e59a17af9f5bf2a86b93ad4700d0eeb85d742a884aa38dc0b54dda5b"},
+    {file = "array_record-0.5.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:897362036f2920093eff3d729c2a6e1844e3077f513d6bd29640cd02f98e07c7"},
+    {file = "array_record-0.5.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6ebe99f37e3a797322f4f5cfc6902b5e852012ba2729fac628aad6affb225247"},
+    {file = "array_record-0.5.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ea3969b9f954f6f01ddac64f59eea45392dc4eb8c1bf3d1ca5c9bcfd7f8d46e7"},
+    {file = "array_record-0.5.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f08eea9a4afbfa05fb7fafaa007b89e286a8a27a7775cb80338199576ffe07b4"},
+    {file = "array_record-0.5.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:248fb29086cb3a6322a5d8b8332d77713a030bc54f0bacdf215a6d3185f73f90"},
+    {file = "array_record-0.5.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8ebd7c12c0a159a44c6c8fdaf915036fcddfdfa499a878ddcff9761c6d1af685"},
+    {file = "array_record-0.5.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9922862216a9d3be76fdc27968af1ec0ea20f986329998ba45b0f01ee3e646fa"},
 ]
 
 [package.dependencies]
 absl-py = "*"
 etils = {version = "*", extras = ["epath"]}
 
+[package.extras]
+beam = ["apache-beam[gcp] (>=2.50.0)", "google-cloud-storage (>=2.11.0)", "tensorflow (>=2.14.0)"]
+
 [[package]]
 name = "asgiref"
-version = "3.7.2"
+version = "3.8.1"
 description = "ASGI specs, helper code, and adapters"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "asgiref-3.7.2-py3-none-any.whl", hash = "sha256:89b2ef2247e3b562a16eef663bc0e2e703ec6468e2fa8a5cd61cd449786d4f6e"},
-    {file = "asgiref-3.7.2.tar.gz", hash = "sha256:9e0ce3aa93a819ba5b45120216b23878cf6e8525eb3848653452b4192b92afed"},
+    {file = "asgiref-3.8.1-py3-none-any.whl", hash = "sha256:3e1e3ecc849832fe52ccf2cb6686b7a55f82bb1d6aee72a58826471390335e47"},
+    {file = "asgiref-3.8.1.tar.gz", hash = "sha256:c343bd80a0bec947a9860adb4c432ffa7db769836c64238fc34bdc3fec84d590"},
 ]
 
 [package.dependencies]
@@ -260,26 +268,27 @@ files = [
 
 [[package]]
 name = "build"
-version = "1.1.1"
+version = "1.2.1"
 description = "A simple, correct Python build frontend"
 optional = false
-python-versions = ">= 3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "build-1.1.1-py3-none-any.whl", hash = "sha256:8ed0851ee76e6e38adce47e4bee3b51c771d86c64cf578d0c2245567ee200e73"},
-    {file = "build-1.1.1.tar.gz", hash = "sha256:8eea65bb45b1aac2e734ba2cc8dad3a6d97d97901a395bd0ed3e7b46953d2a31"},
+    {file = "build-1.2.1-py3-none-any.whl", hash = "sha256:75e10f767a433d9a86e50d83f418e83efc18ede923ee5ff7df93b6cb0306c5d4"},
+    {file = "build-1.2.1.tar.gz", hash = "sha256:526263f4870c26f26c433545579475377b2b7588b6f1eac76a001e873ae3e19d"},
 ]
 
 [package.dependencies]
 colorama = {version = "*", markers = "os_name == \"nt\""}
 importlib-metadata = {version = ">=4.6", markers = "python_full_version < \"3.10.2\""}
-packaging = ">=19.0"
+packaging = ">=19.1"
 pyproject_hooks = "*"
 tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
 
 [package.extras]
 docs = ["furo (>=2023.08.17)", "sphinx (>=7.0,<8.0)", "sphinx-argparse-cli (>=1.5)", "sphinx-autodoc-typehints (>=1.10)", "sphinx-issues (>=3.0.0)"]
-test = ["filelock (>=3)", "pytest (>=6.2.4)", "pytest-cov (>=2.12)", "pytest-mock (>=2)", "pytest-rerunfailures (>=9.1)", "pytest-xdist (>=1.34)", "setuptools (>=42.0.0)", "setuptools (>=56.0.0)", "setuptools (>=56.0.0)", "setuptools (>=67.8.0)", "wheel (>=0.36.0)"]
-typing = ["importlib-metadata (>=5.1)", "mypy (>=1.5.0,<1.6.0)", "tomli", "typing-extensions (>=3.7.4.3)"]
+test = ["build[uv,virtualenv]", "filelock (>=3)", "pytest (>=6.2.4)", "pytest-cov (>=2.12)", "pytest-mock (>=2)", "pytest-rerunfailures (>=9.1)", "pytest-xdist (>=1.34)", "setuptools (>=42.0.0)", "setuptools (>=56.0.0)", "setuptools (>=56.0.0)", "setuptools (>=67.8.0)", "wheel (>=0.36.0)"]
+typing = ["build[uv]", "importlib-metadata (>=5.1)", "mypy (>=1.9.0,<1.10.0)", "tomli", "typing-extensions (>=3.7.4.3)"]
+uv = ["uv (>=0.1.18)"]
 virtualenv = ["virtualenv (>=20.0.35)"]
 
 [[package]]
@@ -416,13 +425,13 @@ files = [
 
 [[package]]
 name = "chex"
-version = "0.1.85"
+version = "0.1.86"
 description = "Chex: Testing made fun, in JAX!"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "chex-0.1.85-py3-none-any.whl", hash = "sha256:32c96719aa94045339174138a6aec14aed2630a8a17fb2633ad3eb868890551d"},
-    {file = "chex-0.1.85.tar.gz", hash = "sha256:a27cfe87119d6e1fe24ccc1438a59195e6dc1d6e0e10099fcf618c3f64771faf"},
+    {file = "chex-0.1.86-py3-none-any.whl", hash = "sha256:251c20821092323a3d9c28e1cf80e4a58180978bec368f531949bd9847eee568"},
+    {file = "chex-0.1.86.tar.gz", hash = "sha256:e8b0f96330eba4144659e1617c0f7a57b161e8cbb021e55c6d5056c7378091d1"},
 ]
 
 [package.dependencies]
@@ -527,13 +536,13 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
 [[package]]
 name = "clu"
-version = "0.0.11"
+version = "0.0.12"
 description = "Set of libraries for ML training loops in JAX."
 optional = false
 python-versions = "*"
 files = [
-    {file = "clu-0.0.11-py3-none-any.whl", hash = "sha256:02003c3c4821429e6612be596912e93ac190492a3591a443286e41d1a595897e"},
-    {file = "clu-0.0.11.tar.gz", hash = "sha256:457d0d44bdea03cb38ed9672c8342b4c12a30950d89b6b1028978af6f12d396c"},
+    {file = "clu-0.0.12-py3-none-any.whl", hash = "sha256:0d183e7d25f7dd0700444510a264e24700e2f068bdabd199ed22866f7e54edba"},
+    {file = "clu-0.0.12.tar.gz", hash = "sha256:f71eaa1afbd30f57f7709257ba7e1feb8ad5c1c3dcae3606672a138678bb3ce4"},
 ]
 
 [package.dependencies]
@@ -701,13 +710,13 @@ files = [
 
 [[package]]
 name = "docstring-parser"
-version = "0.15"
+version = "0.16"
 description = "Parse Python docstrings in reST, Google and Numpydoc format"
 optional = false
 python-versions = ">=3.6,<4.0"
 files = [
-    {file = "docstring_parser-0.15-py3-none-any.whl", hash = "sha256:d1679b86250d269d06a99670924d6bce45adc00b08069dae8c47d98e89b667a9"},
-    {file = "docstring_parser-0.15.tar.gz", hash = "sha256:48ddc093e8b1865899956fcc03b03e66bb7240c310fac5af81814580c55bf682"},
+    {file = "docstring_parser-0.16-py3-none-any.whl", hash = "sha256:bf0a1387354d3691d102edef7ec124f219ef639982d096e26e3b60aeffa90637"},
+    {file = "docstring_parser-0.16.tar.gz", hash = "sha256:538beabd0af1e2db0146b6bd3caa526c35a34d61af9fd2887f3a8a27a739aa6e"},
 ]
 
 [[package]]
@@ -858,18 +867,18 @@ tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipyth
 
 [[package]]
 name = "fastapi"
-version = "0.110.0"
+version = "0.110.1"
 description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "fastapi-0.110.0-py3-none-any.whl", hash = "sha256:87a1f6fb632a218222c5984be540055346a8f5d8a68e8f6fb647b1dc9934de4b"},
-    {file = "fastapi-0.110.0.tar.gz", hash = "sha256:266775f0dcc95af9d3ef39bad55cff525329a931d5fd51930aadd4f428bf7ff3"},
+    {file = "fastapi-0.110.1-py3-none-any.whl", hash = "sha256:5df913203c482f820d31f48e635e022f8cbfe7350e4830ef05a3163925b1addc"},
+    {file = "fastapi-0.110.1.tar.gz", hash = "sha256:6feac43ec359dfe4f45b2c18ec8c94edb8dc2dfc461d417d9e626590c071baad"},
 ]
 
 [package.dependencies]
 pydantic = ">=1.7.4,<1.8 || >1.8,<1.8.1 || >1.8.1,<2.0.0 || >2.0.0,<2.0.1 || >2.0.1,<2.1.0 || >2.1.0,<3.0.0"
-starlette = ">=0.36.3,<0.37.0"
+starlette = ">=0.37.2,<0.38.0"
 typing-extensions = ">=4.8.0"
 
 [package.extras]
@@ -877,18 +886,18 @@ all = ["email-validator (>=2.0.0)", "httpx (>=0.23.0)", "itsdangerous (>=1.1.0)"
 
 [[package]]
 name = "filelock"
-version = "3.13.1"
+version = "3.13.4"
 description = "A platform independent file lock."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "filelock-3.13.1-py3-none-any.whl", hash = "sha256:57dbda9b35157b05fb3e58ee91448612eb674172fab98ee235ccb0b5bee19a1c"},
-    {file = "filelock-3.13.1.tar.gz", hash = "sha256:521f5f56c50f8426f5e03ad3b281b490a87ef15bc6c526f168290f0c7148d44e"},
+    {file = "filelock-3.13.4-py3-none-any.whl", hash = "sha256:404e5e9253aa60ad457cae1be07c0f0ca90a63931200a47d9b6a6af84fd7b45f"},
+    {file = "filelock-3.13.4.tar.gz", hash = "sha256:d13f466618bfde72bd2c18255e269f72542c6e70e7bac83a0232d6b1cc5c8cf4"},
 ]
 
 [package.extras]
-docs = ["furo (>=2023.9.10)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1.24)"]
-testing = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "diff-cover (>=8)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)", "pytest-timeout (>=2.2)"]
+docs = ["furo (>=2023.9.10)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1.25.2)"]
+testing = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "diff-cover (>=8.0.1)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)", "pytest-timeout (>=2.2)"]
 typing = ["typing-extensions (>=4.8)"]
 
 [[package]]
@@ -915,13 +924,13 @@ dotenv = ["python-dotenv"]
 
 [[package]]
 name = "flatbuffers"
-version = "1.12"
+version = "24.3.25"
 description = "The FlatBuffers serialization format for Python"
 optional = false
 python-versions = "*"
 files = [
-    {file = "flatbuffers-1.12-py2.py3-none-any.whl", hash = "sha256:9e9ef47fa92625c4721036e7c4124182668dc6021d9e7c73704edd395648deb9"},
-    {file = "flatbuffers-1.12.tar.gz", hash = "sha256:63bb9a722d5e373701913e226135b28a6f6ac200d5cc7b4d919fa38d73b44610"},
+    {file = "flatbuffers-24.3.25-py2.py3-none-any.whl", hash = "sha256:8dbdec58f935f3765e4f7f3cf635ac3a77f83568138d6a2311f524ec96364812"},
+    {file = "flatbuffers-24.3.25.tar.gz", hash = "sha256:de2ec5b203f21441716617f38443e0a8ebf3d25bf0d9c0bb0ce68fa00ad546a4"},
 ]
 
 [[package]]
@@ -936,13 +945,13 @@ files = [
 
 [[package]]
 name = "flax"
-version = "0.8.1"
+version = "0.8.2"
 description = "Flax: A neural network library for JAX designed for flexibility"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "flax-0.8.1-py3-none-any.whl", hash = "sha256:8cf9ef11859eef252470377556a8cc48db287fc6647407ab34f1fc01461925dd"},
-    {file = "flax-0.8.1.tar.gz", hash = "sha256:ce3d99e9b4c0d2e4d9fc28bc56cced8ba953adfd695aabd24f096b4c8a7e2f92"},
+    {file = "flax-0.8.2-py3-none-any.whl", hash = "sha256:911d83e01380fdb3135c309e70981aabd15e7ca038014d7989ddc3cfaf4d0d45"},
+    {file = "flax-0.8.2.tar.gz", hash = "sha256:1c4e43ac3cb32e8e15c733cfa3df8d827b61d9ce29b50a7035920bfe9fdaa5b0"},
 ]
 
 [package.dependencies]
@@ -965,13 +974,13 @@ testing = ["black[jupyter] (==23.7.0)", "clu", "clu (<=0.0.9)", "einops", "gymna
 
 [[package]]
 name = "fsspec"
-version = "2024.2.0"
+version = "2024.3.1"
 description = "File-system specification"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "fsspec-2024.2.0-py3-none-any.whl", hash = "sha256:817f969556fa5916bc682e02ca2045f96ff7f586d45110fcb76022063ad2c7d8"},
-    {file = "fsspec-2024.2.0.tar.gz", hash = "sha256:b6ad1a679f760dda52b1168c859d01b7b80648ea6f7f7c7f5a8a91dc3f3ecb84"},
+    {file = "fsspec-2024.3.1-py3-none-any.whl", hash = "sha256:918d18d41bf73f0e2b261824baeb1b124bcf771767e3a26425cd7dec3332f512"},
+    {file = "fsspec-2024.3.1.tar.gz", hash = "sha256:f39780e282d7d117ffb42bb96992f8a90795e4d0fb0f661a70ca39fe9c43ded9"},
 ]
 
 [package.extras]
@@ -1000,28 +1009,29 @@ tqdm = ["tqdm"]
 
 [[package]]
 name = "gast"
-version = "0.4.0"
+version = "0.5.4"
 description = "Python AST that abstracts the underlying Python version"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 files = [
-    {file = "gast-0.4.0-py3-none-any.whl", hash = "sha256:b7adcdd5adbebf1adf17378da5ba3f543684dbec47b1cda1f3997e573cd542c4"},
-    {file = "gast-0.4.0.tar.gz", hash = "sha256:40feb7b8b8434785585ab224d1568b857edb18297e5a3047f1ba012bc83b42c1"},
+    {file = "gast-0.5.4-py3-none-any.whl", hash = "sha256:6fc4fa5fa10b72fb8aab4ae58bcb023058386e67b6fa2e3e34cec5c769360316"},
+    {file = "gast-0.5.4.tar.gz", hash = "sha256:9c270fe5f4b130969b54174de7db4e764b09b4f7f67ccfc32480e29f78348d97"},
 ]
 
 [[package]]
 name = "google-ai-generativelanguage"
-version = "0.4.0"
+version = "0.6.1"
 description = "Google Ai Generativelanguage API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-ai-generativelanguage-0.4.0.tar.gz", hash = "sha256:c8199066c08f74c4e91290778329bb9f357ba1ea5d6f82de2bc0d10552bf4f8c"},
-    {file = "google_ai_generativelanguage-0.4.0-py3-none-any.whl", hash = "sha256:e4c425376c1ee26c78acbc49a24f735f90ebfa81bf1a06495fae509a2433232c"},
+    {file = "google-ai-generativelanguage-0.6.1.tar.gz", hash = "sha256:4abf37000718b20c43f4b90672b3ab8850738b02457efffd11f3184e03272ed2"},
+    {file = "google_ai_generativelanguage-0.6.1-py3-none-any.whl", hash = "sha256:d2afc991c47663bdf65bd4aabcd89723550b81ad0a6d0be8bfb0160755da4cf0"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
@@ -1051,13 +1061,13 @@ grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0dev)"]
 
 [[package]]
 name = "google-api-core"
-version = "2.17.1"
+version = "2.18.0"
 description = "Google API client core library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-api-core-2.17.1.tar.gz", hash = "sha256:9df18a1f87ee0df0bc4eea2770ebc4228392d8cc4066655b320e2cfccb15db95"},
-    {file = "google_api_core-2.17.1-py3-none-any.whl", hash = "sha256:610c5b90092c360736baccf17bd3efbcb30dd380e7a6dc28a71059edb8bd0d8e"},
+    {file = "google-api-core-2.18.0.tar.gz", hash = "sha256:62d97417bfc674d6cef251e5c4d639a9655e00c45528c4364fbfebb478ce72a9"},
+    {file = "google_api_core-2.18.0-py3-none-any.whl", hash = "sha256:5a63aa102e0049abe85b5b88cb9409234c1f70afcda21ce1e40b285b9629c1d6"},
 ]
 
 [package.dependencies]
@@ -1065,6 +1075,7 @@ google-auth = ">=2.14.1,<3.0.dev0"
 googleapis-common-protos = ">=1.56.2,<2.0.dev0"
 grpcio = {version = ">=1.33.2,<2.0dev", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""}
 grpcio-status = {version = ">=1.33.2,<2.0.dev0", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""}
+proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0.dev0"
 requests = ">=2.18.0,<3.0.0.dev0"
 
@@ -1073,15 +1084,33 @@ grpc = ["grpcio (>=1.33.2,<2.0dev)", "grpcio (>=1.49.1,<2.0dev)", "grpcio-status
 grpcgcp = ["grpcio-gcp (>=0.2.2,<1.0.dev0)"]
 grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0.dev0)"]
 
+[[package]]
+name = "google-api-python-client"
+version = "2.125.0"
+description = "Google API Client Library for Python"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "google-api-python-client-2.125.0.tar.gz", hash = "sha256:51a0385cff65ec135106e8be60ee7112557396dde5f44113ae23912baddda143"},
+    {file = "google_api_python_client-2.125.0-py2.py3-none-any.whl", hash = "sha256:0a62b60fbd61b61a455f15d925264b3301099b67cafd2d33cf8bf151f1fca4f4"},
+]
+
+[package.dependencies]
+google-api-core = ">=1.31.5,<2.0.dev0 || >2.3.0,<3.0.0.dev0"
+google-auth = ">=1.32.0,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0.dev0"
+google-auth-httplib2 = ">=0.2.0,<1.0.0"
+httplib2 = ">=0.19.0,<1.dev0"
+uritemplate = ">=3.0.1,<5"
+
 [[package]]
 name = "google-auth"
-version = "2.28.2"
+version = "2.29.0"
 description = "Google Authentication Library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-auth-2.28.2.tar.gz", hash = "sha256:80b8b4969aa9ed5938c7828308f20f035bc79f9d8fb8120bf9dc8db20b41ba30"},
-    {file = "google_auth-2.28.2-py2.py3-none-any.whl", hash = "sha256:9fd67bbcd40f16d9d42f950228e9cf02a2ded4ae49198b27432d0cded5a74c38"},
+    {file = "google-auth-2.29.0.tar.gz", hash = "sha256:672dff332d073227550ffc7457868ac4218d6c500b155fe6cc17d2b13602c360"},
+    {file = "google_auth-2.29.0-py2.py3-none-any.whl", hash = "sha256:d452ad095688cd52bae0ad6fafe027f6a6d6f560e810fec20914e17a09526415"},
 ]
 
 [package.dependencies]
@@ -1097,38 +1126,37 @@ reauth = ["pyu2f (>=0.1.5)"]
 requests = ["requests (>=2.20.0,<3.0.0.dev0)"]
 
 [[package]]
-name = "google-auth-oauthlib"
-version = "0.4.6"
-description = "Google Authentication Library"
+name = "google-auth-httplib2"
+version = "0.2.0"
+description = "Google Authentication Library: httplib2 transport"
 optional = false
-python-versions = ">=3.6"
+python-versions = "*"
 files = [
-    {file = "google-auth-oauthlib-0.4.6.tar.gz", hash = "sha256:a90a072f6993f2c327067bf65270046384cda5a8ecb20b94ea9a687f1f233a7a"},
-    {file = "google_auth_oauthlib-0.4.6-py2.py3-none-any.whl", hash = "sha256:3f2a6e802eebbb6fb736a370fbf3b055edcb6b52878bf2f26330b5e041316c73"},
+    {file = "google-auth-httplib2-0.2.0.tar.gz", hash = "sha256:38aa7badf48f974f1eb9861794e9c0cb2a0511a4ec0679b1f886d108f5640e05"},
+    {file = "google_auth_httplib2-0.2.0-py2.py3-none-any.whl", hash = "sha256:b65a0a2123300dd71281a7bf6e64d65a0759287df52729bdd1ae2e47dc311a3d"},
 ]
 
 [package.dependencies]
-google-auth = ">=1.0.0"
-requests-oauthlib = ">=0.7.0"
-
-[package.extras]
-tool = ["click (>=6.0.0)"]
+google-auth = "*"
+httplib2 = ">=0.19.0"
 
 [[package]]
 name = "google-generativeai"
-version = "0.3.2"
+version = "0.5.0"
 description = "Google Generative AI High level API client library and tools."
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "google_generativeai-0.3.2-py3-none-any.whl", hash = "sha256:8761147e6e167141932dc14a7b7af08f2310dd56668a78d206c19bb8bd85bcd7"},
+    {file = "google_generativeai-0.5.0-py3-none-any.whl", hash = "sha256:207ed12c6a2eeab549a45abbf5373c82077f62b16030bdb502556c78f6d1b5d2"},
 ]
 
 [package.dependencies]
-google-ai-generativelanguage = "0.4.0"
+google-ai-generativelanguage = "0.6.1"
 google-api-core = "*"
-google-auth = "*"
+google-api-python-client = "*"
+google-auth = ">=2.15.0"
 protobuf = "*"
+pydantic = "*"
 tqdm = "*"
 typing-extensions = "*"
 
@@ -1262,41 +1290,51 @@ files = [
 
 [[package]]
 name = "h5py"
-version = "3.10.0"
+version = "3.11.0"
 description = "Read and write HDF5 files from Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "h5py-3.10.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:b963fb772964fc1d1563c57e4e2e874022ce11f75ddc6df1a626f42bd49ab99f"},
-    {file = "h5py-3.10.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:012ab448590e3c4f5a8dd0f3533255bc57f80629bf7c5054cf4c87b30085063c"},
-    {file = "h5py-3.10.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:781a24263c1270a62cd67be59f293e62b76acfcc207afa6384961762bb88ea03"},
-    {file = "h5py-3.10.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f42e6c30698b520f0295d70157c4e202a9e402406f50dc08f5a7bc416b24e52d"},
-    {file = "h5py-3.10.0-cp310-cp310-win_amd64.whl", hash = "sha256:93dd840bd675787fc0b016f7a05fc6efe37312a08849d9dd4053fd0377b1357f"},
-    {file = "h5py-3.10.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:2381e98af081b6df7f6db300cd88f88e740649d77736e4b53db522d8874bf2dc"},
-    {file = "h5py-3.10.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:667fe23ab33d5a8a6b77970b229e14ae3bb84e4ea3382cc08567a02e1499eedd"},
-    {file = "h5py-3.10.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:90286b79abd085e4e65e07c1bd7ee65a0f15818ea107f44b175d2dfe1a4674b7"},
-    {file = "h5py-3.10.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6c013d2e79c00f28ffd0cc24e68665ea03ae9069e167087b2adb5727d2736a52"},
-    {file = "h5py-3.10.0-cp311-cp311-win_amd64.whl", hash = "sha256:92273ce69ae4983dadb898fd4d3bea5eb90820df953b401282ee69ad648df684"},
-    {file = "h5py-3.10.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:3c97d03f87f215e7759a354460fb4b0d0f27001450b18b23e556e7856a0b21c3"},
-    {file = "h5py-3.10.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:86df4c2de68257b8539a18646ceccdcf2c1ce6b1768ada16c8dcfb489eafae20"},
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 ]
 
 [package.dependencies]
 numpy = ">=1.17.3"
 
+[[package]]
+name = "httplib2"
+version = "0.22.0"
+description = "A comprehensive HTTP client library."
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+files = [
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+]
+
+[package.dependencies]
+pyparsing = {version = ">=2.4.2,<3.0.0 || >3.0.0,<3.0.1 || >3.0.1,<3.0.2 || >3.0.2,<3.0.3 || >3.0.3,<4", markers = "python_version > \"3.0\""}
+
 [[package]]
 name = "httptools"
 version = "0.6.1"
@@ -1347,13 +1385,13 @@ test = ["Cython (>=0.29.24,<0.30.0)"]
 
 [[package]]
 name = "huggingface-hub"
-version = "0.21.4"
+version = "0.22.2"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 optional = false
 python-versions = ">=3.8.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -1366,15 +1404,16 @@ tqdm = ">=4.42.1"
 typing-extensions = ">=3.7.4.3"
 
 [package.extras]
-all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "mypy (==1.5.1)", "numpy", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.1.3)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "minijinja (>=1.0)", "mypy (==1.5.1)", "numpy", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.3.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
 cli = ["InquirerPy (==0.3.4)"]
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 fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
 hf-transfer = ["hf-transfer (>=0.1.4)"]
-inference = ["aiohttp", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)"]
-quality = ["mypy (==1.5.1)", "ruff (>=0.1.3)"]
+inference = ["aiohttp", "minijinja (>=1.0)"]
+quality = ["mypy (==1.5.1)", "ruff (>=0.3.0)"]
 tensorflow = ["graphviz", "pydot", "tensorflow"]
-testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "numpy", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
+tensorflow-testing = ["keras (<3.0)", "tensorflow"]
+testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "minijinja (>=1.0)", "numpy", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
 torch = ["safetensors", "torch"]
 typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"]
 
@@ -1394,13 +1433,13 @@ pyreadline3 = {version = "*", markers = "sys_platform == \"win32\" and python_ve
 
 [[package]]
 name = "idna"
-version = "3.6"
+version = "3.7"
 description = "Internationalized Domain Names in Applications (IDNA)"
 optional = false
 python-versions = ">=3.5"
 files = [
-    {file = "idna-3.6-py3-none-any.whl", hash = "sha256:c05567e9c24a6b9faaa835c4821bad0590fbb9d5779e7caa6e1cc4978e7eb24f"},
-    {file = "idna-3.6.tar.gz", hash = "sha256:9ecdbbd083b06798ae1e86adcbfe8ab1479cf864e4ee30fe4e46a003d12491ca"},
+    {file = "idna-3.7-py3-none-any.whl", hash = "sha256:82fee1fc78add43492d3a1898bfa6d8a904cc97d8427f683ed8e798d07761aa0"},
+    {file = "idna-3.7.tar.gz", hash = "sha256:028ff3aadf0609c1fd278d8ea3089299412a7a8b9bd005dd08b9f8285bcb5cfc"},
 ]
 
 [[package]]
@@ -1416,13 +1455,13 @@ files = [
 
 [[package]]
 name = "importlib-metadata"
-version = "6.11.0"
+version = "7.0.0"
 description = "Read metadata from Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "importlib_metadata-6.11.0-py3-none-any.whl", hash = "sha256:f0afba6205ad8f8947c7d338b5342d5db2afbfd82f9cbef7879a9539cc12eb9b"},
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+    {file = "importlib_metadata-7.0.0-py3-none-any.whl", hash = "sha256:d97503976bb81f40a193d41ee6570868479c69d5068651eb039c40d850c59d67"},
+    {file = "importlib_metadata-7.0.0.tar.gz", hash = "sha256:7fc841f8b8332803464e5dc1c63a2e59121f46ca186c0e2e182e80bf8c1319f7"},
 ]
 
 [package.dependencies]
@@ -1435,28 +1474,28 @@ testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs
 
 [[package]]
 name = "importlib-resources"
-version = "6.3.0"
+version = "6.4.0"
 description = "Read resources from Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "importlib_resources-6.3.0-py3-none-any.whl", hash = "sha256:783407aa1cd05550e3aa123e8f7cfaebee35ffa9cb0242919e2d1e4172222705"},
-    {file = "importlib_resources-6.3.0.tar.gz", hash = "sha256:166072a97e86917a9025876f34286f549b9caf1d10b35a1b372bffa1600c6569"},
+    {file = "importlib_resources-6.4.0-py3-none-any.whl", hash = "sha256:50d10f043df931902d4194ea07ec57960f66a80449ff867bfe782b4c486ba78c"},
+    {file = "importlib_resources-6.4.0.tar.gz", hash = "sha256:cdb2b453b8046ca4e3798eb1d84f3cce1446a0e8e7b5ef4efb600f19fc398145"},
 ]
 
 [package.extras]
 docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["jaraco.collections", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-ruff (>=0.2.1)", "zipp (>=3.17)"]
+testing = ["jaraco.test (>=5.4)", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-ruff (>=0.2.1)", "zipp (>=3.17)"]
 
 [[package]]
 name = "ipython"
-version = "8.22.2"
+version = "8.23.0"
 description = "IPython: Productive Interactive Computing"
 optional = false
 python-versions = ">=3.10"
 files = [
-    {file = "ipython-8.22.2-py3-none-any.whl", hash = "sha256:3c86f284c8f3d8f2b6c662f885c4889a91df7cd52056fd02b7d8d6195d7f56e9"},
-    {file = "ipython-8.22.2.tar.gz", hash = "sha256:2dcaad9049f9056f1fef63514f176c7d41f930daa78d05b82a176202818f2c14"},
+    {file = "ipython-8.23.0-py3-none-any.whl", hash = "sha256:07232af52a5ba146dc3372c7bf52a0f890a23edf38d77caef8d53f9cdc2584c1"},
+    {file = "ipython-8.23.0.tar.gz", hash = "sha256:7468edaf4f6de3e1b912e57f66c241e6fd3c7099f2ec2136e239e142e800274d"},
 ]
 
 [package.dependencies]
@@ -1470,12 +1509,14 @@ prompt-toolkit = ">=3.0.41,<3.1.0"
 pygments = ">=2.4.0"
 stack-data = "*"
 traitlets = ">=5.13.0"
+typing-extensions = {version = "*", markers = "python_version < \"3.12\""}
 
 [package.extras]
-all = ["ipython[black,doc,kernel,nbconvert,nbformat,notebook,parallel,qtconsole,terminal]", "ipython[test,test-extra]"]
+all = ["ipython[black,doc,kernel,matplotlib,nbconvert,nbformat,notebook,parallel,qtconsole]", "ipython[test,test-extra]"]
 black = ["black"]
 doc = ["docrepr", "exceptiongroup", "ipykernel", "ipython[test]", "matplotlib", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "sphinxcontrib-jquery", "stack-data", "typing-extensions"]
 kernel = ["ipykernel"]
+matplotlib = ["matplotlib"]
 nbconvert = ["nbconvert"]
 nbformat = ["nbformat"]
 notebook = ["ipywidgets", "notebook"]
@@ -1511,13 +1552,13 @@ files = [
 
 [[package]]
 name = "jax"
-version = "0.4.25"
+version = "0.4.26"
 description = "Differentiate, compile, and transform Numpy code."
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "jax-0.4.25-py3-none-any.whl", hash = "sha256:8158c837e5ecc195074b421609e85329a962785b36f9fe5ff53e844e8ad87dbc"},
-    {file = "jax-0.4.25.tar.gz", hash = "sha256:a8ee189c782de2b7b2ffb64a8916da380b882a617e2769aa429b71d79747b982"},
+    {file = "jax-0.4.26-py3-none-any.whl", hash = "sha256:50dc795148ee6b0735b48b477e5abc556aa3a4c7af5d6940dad08024a908b02f"},
+    {file = "jax-0.4.26.tar.gz", hash = "sha256:2cce025d0a279ec630d550524749bc8efe25d2ff47240d2a7d4cfbc5090c5383"},
 ]
 
 [package.dependencies]
@@ -1531,45 +1572,43 @@ scipy = ">=1.9"
 
 [package.extras]
 australis = ["protobuf (>=3.13,<4)"]
-ci = ["jaxlib (==0.4.24)"]
-cpu = ["jaxlib (==0.4.25)"]
-cuda = ["jaxlib (==0.4.25+cuda11.cudnn86)"]
-cuda11-cudnn86 = ["jaxlib (==0.4.25+cuda11.cudnn86)"]
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+tpu = ["jaxlib (==0.4.26)", "libtpu-nightly (==0.1.dev20240403)", "requests"]
 
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 [package.dependencies]
@@ -1578,8 +1617,7 @@ numpy = ">=1.22"
 scipy = ">=1.9"
 
 [package.extras]
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 [[package]]
 name = "jedi"
@@ -1619,33 +1657,23 @@ i18n = ["Babel (>=2.7)"]
 
 [[package]]
 name = "keras"
-version = "2.9.0"
-description = "Deep learning for humans."
+version = "3.2.1"
+description = "Multi-backend Keras."
 optional = false
-python-versions = "*"
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 files = [
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 ]
 
 [package.dependencies]
-numpy = ">=1.9.1"
-six = ">=1.9.0"
-
-[package.extras]
-image = ["Pillow (>=5.2.0)", "scipy (>=0.14)"]
-pep8 = ["flake8"]
-tests = ["Pillow", "keras", "pandas", "pytest", "pytest-cov", "pytest-xdist", "tensorflow"]
+absl-py = "*"
+h5py = "*"
+ml-dtypes = "*"
+namex = "*"
+numpy = "*"
+optree = "*"
+rich = "*"
 
 [[package]]
 name = "kubernetes"
@@ -1721,33 +1749,31 @@ files = [
 
 [[package]]
 name = "libclang"
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+version = "18.1.1"
 description = "Clang Python Bindings, mirrored from the official LLVM repo: https://github.com/llvm/llvm-project/tree/main/clang/bindings/python, to make the installation process easier."
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 python-versions = "*"
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 ]
 
 [[package]]
 name = "markdown"
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 description = "Python implementation of John Gruber's Markdown."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -2130,6 +2156,17 @@ files = [
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 ]
 
+[[package]]
+name = "namex"
+version = "0.0.7"
+description = "A simple utility to separate the implementation of your Python package and its public API surface."
+optional = false
+python-versions = "*"
+files = [
+    {file = "namex-0.0.7-py3-none-any.whl", hash = "sha256:8a4f062945f405d77cb66b907f16aa2fd83681945e998be840eb6c4154d40108"},
+    {file = "namex-0.0.7.tar.gz", hash = "sha256:84ba65bc4d22bd909e3d26bf2ffb4b9529b608cb3f9a4336f776b04204ced69b"},
+]
+
 [[package]]
 name = "nest-asyncio"
 version = "1.6.0"
@@ -2246,42 +2283,42 @@ sympy = "*"
 
 [[package]]
 name = "opentelemetry-api"
-version = "1.23.0"
+version = "1.24.0"
 description = "OpenTelemetry Python API"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "opentelemetry_api-1.23.0-py3-none-any.whl", hash = "sha256:cc03ea4025353048aadb9c64919099663664672ea1c6be6ddd8fee8e4cd5e774"},
-    {file = "opentelemetry_api-1.23.0.tar.gz", hash = "sha256:14a766548c8dd2eb4dfc349739eb4c3893712a0daa996e5dbf945f9da665da9d"},
+    {file = "opentelemetry_api-1.24.0-py3-none-any.whl", hash = "sha256:0f2c363d98d10d1ce93330015ca7fd3a65f60be64e05e30f557c61de52c80ca2"},
+    {file = "opentelemetry_api-1.24.0.tar.gz", hash = "sha256:42719f10ce7b5a9a73b10a4baf620574fb8ad495a9cbe5c18d76b75d8689c67e"},
 ]
 
 [package.dependencies]
 deprecated = ">=1.2.6"
-importlib-metadata = ">=6.0,<7.0"
+importlib-metadata = ">=6.0,<=7.0"
 
 [[package]]
 name = "opentelemetry-exporter-otlp-proto-common"
-version = "1.23.0"
+version = "1.24.0"
 description = "OpenTelemetry Protobuf encoding"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "opentelemetry_exporter_otlp_proto_common-1.23.0-py3-none-any.whl", hash = "sha256:2a9e7e9d5a8b026b572684b6b24dcdefcaa58613d5ce3d644130b0c373c056c1"},
-    {file = "opentelemetry_exporter_otlp_proto_common-1.23.0.tar.gz", hash = "sha256:35e4ea909e7a0b24235bd0aaf17fba49676527feb1823b46565ff246d5a1ab18"},
+    {file = "opentelemetry_exporter_otlp_proto_common-1.24.0-py3-none-any.whl", hash = "sha256:e51f2c9735054d598ad2df5d3eca830fecfb5b0bda0a2fa742c9c7718e12f641"},
+    {file = "opentelemetry_exporter_otlp_proto_common-1.24.0.tar.gz", hash = "sha256:5d31fa1ff976cacc38be1ec4e3279a3f88435c75b38b1f7a099a1faffc302461"},
 ]
 
 [package.dependencies]
-opentelemetry-proto = "1.23.0"
+opentelemetry-proto = "1.24.0"
 
 [[package]]
 name = "opentelemetry-exporter-otlp-proto-grpc"
-version = "1.23.0"
+version = "1.24.0"
 description = "OpenTelemetry Collector Protobuf over gRPC Exporter"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "opentelemetry_exporter_otlp_proto_grpc-1.23.0-py3-none-any.whl", hash = "sha256:40f9e3e7761eb34f2a1001f4543028783ac26e2db27e420d5374f2cca0182dad"},
-    {file = "opentelemetry_exporter_otlp_proto_grpc-1.23.0.tar.gz", hash = "sha256:aa1a012eea5342bfef51fcf3f7f22601dcb0f0984a07ffe6025b2fbb6d91a2a9"},
+    {file = "opentelemetry_exporter_otlp_proto_grpc-1.24.0-py3-none-any.whl", hash = "sha256:f40d62aa30a0a43cc1657428e59fcf82ad5f7ea8fff75de0f9d9cb6f739e0a3b"},
+    {file = "opentelemetry_exporter_otlp_proto_grpc-1.24.0.tar.gz", hash = "sha256:217c6e30634f2c9797999ea9da29f7300479a94a610139b9df17433f915e7baa"},
 ]
 
 [package.dependencies]
@@ -2289,22 +2326,22 @@ deprecated = ">=1.2.6"
 googleapis-common-protos = ">=1.52,<2.0"
 grpcio = ">=1.0.0,<2.0.0"
 opentelemetry-api = ">=1.15,<2.0"
-opentelemetry-exporter-otlp-proto-common = "1.23.0"
-opentelemetry-proto = "1.23.0"
-opentelemetry-sdk = ">=1.23.0,<1.24.0"
+opentelemetry-exporter-otlp-proto-common = "1.24.0"
+opentelemetry-proto = "1.24.0"
+opentelemetry-sdk = ">=1.24.0,<1.25.0"
 
 [package.extras]
 test = ["pytest-grpc"]
 
 [[package]]
 name = "opentelemetry-instrumentation"
-version = "0.44b0"
+version = "0.45b0"
 description = "Instrumentation Tools & Auto Instrumentation for OpenTelemetry Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "opentelemetry_instrumentation-0.44b0-py3-none-any.whl", hash = "sha256:79560f386425176bcc60c59190064597096114c4a8e5154f1cb281bb4e47d2fc"},
-    {file = "opentelemetry_instrumentation-0.44b0.tar.gz", hash = "sha256:8213d02d8c0987b9b26386ae3e091e0477d6331673123df736479322e1a50b48"},
+    {file = "opentelemetry_instrumentation-0.45b0-py3-none-any.whl", hash = "sha256:06c02e2c952c1b076e8eaedf1b82f715e2937ba7eeacab55913dd434fbcec258"},
+    {file = "opentelemetry_instrumentation-0.45b0.tar.gz", hash = "sha256:6c47120a7970bbeb458e6a73686ee9ba84b106329a79e4a4a66761f933709c7e"},
 ]
 
 [package.dependencies]
@@ -2314,57 +2351,55 @@ wrapt = ">=1.0.0,<2.0.0"
 
 [[package]]
 name = "opentelemetry-instrumentation-asgi"
-version = "0.44b0"
+version = "0.45b0"
 description = "ASGI instrumentation for OpenTelemetry"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "opentelemetry_instrumentation_asgi-0.44b0-py3-none-any.whl", hash = "sha256:0d95c84a8991008c8a8ac35e15d43cc7768a5bb46f95f129e802ad2990d7c366"},
-    {file = "opentelemetry_instrumentation_asgi-0.44b0.tar.gz", hash = "sha256:72d4d28ec7ccd551eac11edc5ae8cac3586c0a228467d6a95fad7b6d4edd597a"},
+    {file = "opentelemetry_instrumentation_asgi-0.45b0-py3-none-any.whl", hash = "sha256:8be1157ed62f0db24e45fdf7933c530c4338bd025c5d4af7830e903c0756021b"},
+    {file = "opentelemetry_instrumentation_asgi-0.45b0.tar.gz", hash = "sha256:97f55620f163fd3d20323e9fd8dc3aacc826c03397213ff36b877e0f4b6b08a6"},
 ]
 
 [package.dependencies]
 asgiref = ">=3.0,<4.0"
 opentelemetry-api = ">=1.12,<2.0"
-opentelemetry-instrumentation = "0.44b0"
-opentelemetry-semantic-conventions = "0.44b0"
-opentelemetry-util-http = "0.44b0"
+opentelemetry-instrumentation = "0.45b0"
+opentelemetry-semantic-conventions = "0.45b0"
+opentelemetry-util-http = "0.45b0"
 
 [package.extras]
 instruments = ["asgiref (>=3.0,<4.0)"]
-test = ["opentelemetry-instrumentation-asgi[instruments]", "opentelemetry-test-utils (==0.44b0)"]
 
 [[package]]
 name = "opentelemetry-instrumentation-fastapi"
-version = "0.44b0"
+version = "0.45b0"
 description = "OpenTelemetry FastAPI Instrumentation"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "opentelemetry_instrumentation_fastapi-0.44b0-py3-none-any.whl", hash = "sha256:4441482944bea6676816668d56deb94af990e8c6e9582c581047e5d84c91d3c9"},
-    {file = "opentelemetry_instrumentation_fastapi-0.44b0.tar.gz", hash = "sha256:67ed10b93ad9d35238ae0be73cf8acbbb65a4a61fb7444d0aee5b0c492e294db"},
+    {file = "opentelemetry_instrumentation_fastapi-0.45b0-py3-none-any.whl", hash = "sha256:77d9c123a363129148f5f66d44094f3d67aaaa2b201396d94782b4a7f9ce4314"},
+    {file = "opentelemetry_instrumentation_fastapi-0.45b0.tar.gz", hash = "sha256:5a6b91e1c08a01601845fcfcfdefd0a2aecdb3c356d4a436a3210cb58c21487e"},
 ]
 
 [package.dependencies]
 opentelemetry-api = ">=1.12,<2.0"
-opentelemetry-instrumentation = "0.44b0"
-opentelemetry-instrumentation-asgi = "0.44b0"
-opentelemetry-semantic-conventions = "0.44b0"
-opentelemetry-util-http = "0.44b0"
+opentelemetry-instrumentation = "0.45b0"
+opentelemetry-instrumentation-asgi = "0.45b0"
+opentelemetry-semantic-conventions = "0.45b0"
+opentelemetry-util-http = "0.45b0"
 
 [package.extras]
 instruments = ["fastapi (>=0.58,<1.0)"]
-test = ["httpx (>=0.22,<1.0)", "opentelemetry-instrumentation-fastapi[instruments]", "opentelemetry-test-utils (==0.44b0)", "requests (>=2.23,<3.0)"]
 
 [[package]]
 name = "opentelemetry-proto"
-version = "1.23.0"
+version = "1.24.0"
 description = "OpenTelemetry Python Proto"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "opentelemetry_proto-1.23.0-py3-none-any.whl", hash = "sha256:4c017deca052cb287a6003b7c989ed8b47af65baeb5d57ebf93dde0793f78509"},
-    {file = "opentelemetry_proto-1.23.0.tar.gz", hash = "sha256:e6aaf8b7ace8d021942d546161401b83eed90f9f2cc6f13275008cea730e4651"},
+    {file = "opentelemetry_proto-1.24.0-py3-none-any.whl", hash = "sha256:bcb80e1e78a003040db71ccf83f2ad2019273d1e0828089d183b18a1476527ce"},
+    {file = "opentelemetry_proto-1.24.0.tar.gz", hash = "sha256:ff551b8ad63c6cabb1845ce217a6709358dfaba0f75ea1fa21a61ceddc78cab8"},
 ]
 
 [package.dependencies]
@@ -2372,40 +2407,40 @@ protobuf = ">=3.19,<5.0"
 
 [[package]]
 name = "opentelemetry-sdk"
-version = "1.23.0"
+version = "1.24.0"
 description = "OpenTelemetry Python SDK"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "opentelemetry_sdk-1.23.0-py3-none-any.whl", hash = "sha256:a93c96990ac0f07c6d679e2f1015864ff7a4f5587122dd5af968034436efb1fd"},
-    {file = "opentelemetry_sdk-1.23.0.tar.gz", hash = "sha256:9ddf60195837b59e72fd2033d6a47e2b59a0f74f0ec37d89387d89e3da8cab7f"},
+    {file = "opentelemetry_sdk-1.24.0-py3-none-any.whl", hash = "sha256:fa731e24efe832e98bcd90902085b359dcfef7d9c9c00eb5b9a18587dae3eb59"},
+    {file = "opentelemetry_sdk-1.24.0.tar.gz", hash = "sha256:75bc0563affffa827700e0f4f4a68e1e257db0df13372344aebc6f8a64cde2e5"},
 ]
 
 [package.dependencies]
-opentelemetry-api = "1.23.0"
-opentelemetry-semantic-conventions = "0.44b0"
+opentelemetry-api = "1.24.0"
+opentelemetry-semantic-conventions = "0.45b0"
 typing-extensions = ">=3.7.4"
 
 [[package]]
 name = "opentelemetry-semantic-conventions"
-version = "0.44b0"
+version = "0.45b0"
 description = "OpenTelemetry Semantic Conventions"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "opentelemetry_semantic_conventions-0.44b0-py3-none-any.whl", hash = "sha256:7c434546c9cbd797ab980cc88bf9ff3f4a5a28f941117cad21694e43d5d92019"},
-    {file = "opentelemetry_semantic_conventions-0.44b0.tar.gz", hash = "sha256:2e997cb28cd4ca81a25a9a43365f593d0c2b76be0685015349a89abdf1aa4ffa"},
+    {file = "opentelemetry_semantic_conventions-0.45b0-py3-none-any.whl", hash = "sha256:a4a6fb9a7bacd9167c082aa4681009e9acdbfa28ffb2387af50c2fef3d30c864"},
+    {file = "opentelemetry_semantic_conventions-0.45b0.tar.gz", hash = "sha256:7c84215a44ac846bc4b8e32d5e78935c5c43482e491812a0bb8aaf87e4d92118"},
 ]
 
 [[package]]
 name = "opentelemetry-util-http"
-version = "0.44b0"
+version = "0.45b0"
 description = "Web util for OpenTelemetry"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "opentelemetry_util_http-0.44b0-py3-none-any.whl", hash = "sha256:ff018ab6a2fa349537ff21adcef99a294248b599be53843c44f367aef6bccea5"},
-    {file = "opentelemetry_util_http-0.44b0.tar.gz", hash = "sha256:75896dffcbbeb5df5429ad4526e22307fc041a27114e0c5bfd90bb219381e68f"},
+    {file = "opentelemetry_util_http-0.45b0-py3-none-any.whl", hash = "sha256:6628868b501b3004e1860f976f410eeb3d3499e009719d818000f24ce17b6e33"},
+    {file = "opentelemetry_util_http-0.45b0.tar.gz", hash = "sha256:4ce08b6a7d52dd7c96b7705b5b4f06fdb6aa3eac1233b3b0bfef8a0cab9a92cd"},
 ]
 
 [[package]]
@@ -2428,37 +2463,97 @@ tests = ["pytest", "pytest-cov", "pytest-pep8"]
 
 [[package]]
 name = "optax"
-version = "0.2.1"
+version = "0.2.2"
 description = "A gradient processing and optimisation library in JAX."
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "optax-0.2.1-py3-none-any.whl", hash = "sha256:fe1adcf2157ecfd83af05110b4ac0de142aa52baecc4e51c42490ac6d2867cd2"},
-    {file = "optax-0.2.1.tar.gz", hash = "sha256:fc9f430fa057377140d00aa50611dabbd7e8f4999e3c7543f641f9db6997cb1a"},
+    {file = "optax-0.2.2-py3-none-any.whl", hash = "sha256:411c414a76aae259f4191a60b712663968741a5163ca92fc250b5d5c7d36fb57"},
+    {file = "optax-0.2.2.tar.gz", hash = "sha256:f09bf790ef4b09fb9c35f79a07594c6196a719919985f542dc84b0bf97812e0e"},
 ]
 
 [package.dependencies]
 absl-py = ">=0.7.1"
-chex = ">=0.1.7"
+chex = ">=0.1.86"
 jax = ">=0.1.55"
 jaxlib = ">=0.1.37"
 numpy = ">=1.18.0"
 
 [package.extras]
-docs = ["flax", "ipython (>=8.8.0)", "matplotlib (>=3.5.0)", "myst-nb (>=1.0.0)", "sphinx (>=6.0.0)", "sphinx-autodoc-typehints", "sphinx-book-theme (>=1.0.1)", "sphinx-collections (>=0.0.1)", "sphinx-gallery (>=0.14.0)", "sphinxcontrib-katex", "tensorflow (>=2.4.0)", "tensorflow-datasets (>=4.2.0)"]
+docs = ["flax", "ipython (>=8.8.0)", "matplotlib (>=3.5.0)", "myst-nb (>=1.0.0)", "sphinx (>=6.0.0)", "sphinx-autodoc-typehints", "sphinx-book-theme (>=1.0.1)", "sphinx-collections (>=0.0.1)", "sphinx-gallery (>=0.14.0)", "sphinx_contributors", "sphinxcontrib-katex", "tensorflow (>=2.4.0)", "tensorflow-datasets (>=4.2.0)"]
 dp-accounting = ["absl-py (>=1.0.0)", "attrs (>=21.4.0)", "mpmath (>=1.2.1)", "numpy (>=1.21.4)", "scipy (>=1.7.1)"]
 examples = ["dp_accounting (>=0.4)", "flax", "tensorflow (>=2.4.0)", "tensorflow-datasets (>=4.2.0)"]
 test = ["dm-tree (>=0.1.7)", "flax (>=0.5.3)"]
 
+[[package]]
+name = "optree"
+version = "0.11.0"
+description = "Optimized PyTree Utilities."
+optional = false
+python-versions = ">=3.7"
+files = [
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 [[package]]
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+qa = ["flake8 (==5.0.4)", "mypy (==0.971)", "types-setuptools (==67.2.0.1)"]
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 [[package]]
 name = "pathspec"
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 [package.dependencies]
@@ -3054,6 +3150,20 @@ tomlkit = ">=0.10.1"
 spelling = ["pyenchant (>=3.2,<4.0)"]
 testutils = ["gitpython (>3)"]
 
+[[package]]
+name = "pyparsing"
+version = "3.1.2"
+description = "pyparsing module - Classes and methods to define and execute parsing grammars"
+optional = false
+python-versions = ">=3.6.8"
+files = [
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+]
+
+[package.extras]
+diagrams = ["jinja2", "railroad-diagrams"]
+
 [[package]]
 name = "pypika"
 version = "0.48.9"
@@ -3238,13 +3348,13 @@ use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
 [[package]]
 name = "requests-oauthlib"
-version = "1.4.0"
+version = "2.0.0"
 description = "OAuthlib authentication support for Requests."
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+python-versions = ">=3.4"
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 ]
 
 [package.dependencies]
@@ -3288,45 +3398,45 @@ pyasn1 = ">=0.1.3"
 
 [[package]]
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 description = "Fundamental algorithms for scientific computing in Python"
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 [package.dependencies]
-numpy = ">=1.22.4,<1.29.0"
+numpy = ">=1.22.4,<2.3"
 
 [package.extras]
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+dev = ["cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy", "pycodestyle", "pydevtool", "rich-click", "ruff", "types-psutil", "typing_extensions"]
+doc = ["jupyterlite-pyodide-kernel", "jupyterlite-sphinx (>=0.12.0)", "jupytext", "matplotlib (>=3.5)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (>=0.15.2)", "sphinx (>=5.0.0)", "sphinx-design (>=0.4.0)"]
+test = ["array-api-strict", "asv", "gmpy2", "hypothesis (>=6.30)", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
 
 [[package]]
 name = "sentencepiece"
@@ -3435,6 +3545,17 @@ docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments
 testing = ["build[virtualenv]", "filelock (>=3.4.0)", "importlib-metadata", "ini2toml[lite] (>=0.9)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mypy (==1.9)", "packaging (>=23.2)", "pip (>=19.1)", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-ruff (>=0.2.1)", "pytest-timeout", "pytest-xdist (>=3)", "tomli", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
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+[[package]]
+name = "shellingham"
+version = "1.5.4"
+description = "Tool to Detect Surrounding Shell"
+optional = false
+python-versions = ">=3.7"
+files = [
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+]
+
 [[package]]
 name = "six"
 version = "1.16.0"
@@ -3489,13 +3610,13 @@ tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
 
 [[package]]
 name = "starlette"
-version = "0.36.3"
+version = "0.37.2"
 description = "The little ASGI library that shines."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -3534,120 +3655,92 @@ doc = ["reno", "sphinx", "tornado (>=4.5)"]
 
 [[package]]
 name = "tensorboard"
-version = "2.9.0"
+version = "2.16.2"
 description = "TensorBoard lets you watch Tensors Flow"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.9"
 files = [
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-google-auth-oauthlib = ">=0.4.1,<0.5"
-grpcio = ">=1.24.3"
+grpcio = ">=1.48.2"
 markdown = ">=2.6.8"
 numpy = ">=1.12.0"
-protobuf = ">=3.9.2"
-requests = ">=2.21.0,<3"
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-tensorboard-data-server = ">=0.6.0,<0.7.0"
-tensorboard-plugin-wit = ">=1.6.0"
+six = ">1.9"
+tensorboard-data-server = ">=0.7.0,<0.8.0"
 werkzeug = ">=1.0.1"
-wheel = ">=0.26"
 
 [[package]]
 name = "tensorboard-data-server"
-version = "0.6.1"
+version = "0.7.2"
 description = "Fast data loading for TensorBoard"
 optional = false
-python-versions = ">=3.6"
-files = [
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-[[package]]
-name = "tensorboard-plugin-wit"
-version = "1.8.1"
-description = "What-If Tool TensorBoard plugin."
-optional = false
-python-versions = "*"
+python-versions = ">=3.7"
 files = [
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 ]
 
 [[package]]
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-version = "2.9.0"
+version = "2.16.1"
 description = "TensorFlow is an open source machine learning framework for everyone."
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.9"
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 ]
 
 [package.dependencies]
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 astunparse = ">=1.6.0"
-flatbuffers = ">=1.12,<2"
-gast = ">=0.2.1,<=0.4.0"
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 google-pasta = ">=0.1.1"
 grpcio = ">=1.24.3,<2.0"
-h5py = ">=2.9.0"
-keras = ">=2.9.0rc0,<2.10.0"
-keras-preprocessing = ">=1.1.1"
+h5py = ">=3.10.0"
+keras = ">=3.0.0"
 libclang = ">=13.0.0"
-numpy = ">=1.20"
+ml-dtypes = ">=0.3.1,<0.4.0"
+numpy = {version = ">=1.23.5,<2.0.0", markers = "python_version <= \"3.11\""}
 opt-einsum = ">=2.3.2"
 packaging = "*"
-protobuf = ">=3.9.2"
+protobuf = ">=3.20.3,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
+requests = ">=2.21.0,<3"
 setuptools = "*"
 six = ">=1.12.0"
-tensorboard = ">=2.9,<2.10"
-tensorflow-estimator = ">=2.9.0rc0,<2.10.0"
-tensorflow-io-gcs-filesystem = ">=0.23.1"
+tensorboard = ">=2.16,<2.17"
+tensorflow-io-gcs-filesystem = {version = ">=0.23.1", markers = "python_version < \"3.12\""}
 termcolor = ">=1.1.0"
 typing-extensions = ">=3.6.6"
 wrapt = ">=1.11.0"
 
-[[package]]
-name = "tensorflow-estimator"
-version = "2.9.0"
-description = "TensorFlow Estimator."
-optional = false
-python-versions = ">=3.7"
-files = [
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-
-[[package]]
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-description = "TensorFlow Hub is a library to foster the publication, discovery, and consumption of reusable parts of machine learning models."
-optional = false
-python-versions = "*"
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-
-[package.dependencies]
-numpy = ">=1.12.0"
-protobuf = ">=3.19.6"
-tf-keras = ">=2.14.1"
+[package.extras]
+and-cuda = ["nvidia-cublas-cu12 (==12.3.4.1)", "nvidia-cuda-cupti-cu12 (==12.3.101)", "nvidia-cuda-nvcc-cu12 (==12.3.107)", "nvidia-cuda-nvrtc-cu12 (==12.3.107)", "nvidia-cuda-runtime-cu12 (==12.3.101)", "nvidia-cudnn-cu12 (==8.9.7.29)", "nvidia-cufft-cu12 (==11.0.12.1)", "nvidia-curand-cu12 (==10.3.4.107)", "nvidia-cusolver-cu12 (==11.5.4.101)", "nvidia-cusparse-cu12 (==12.2.0.103)", "nvidia-nccl-cu12 (==2.19.3)", "nvidia-nvjitlink-cu12 (==12.3.101)"]
 
 [[package]]
 name = "tensorflow-io-gcs-filesystem"
@@ -3680,41 +3773,22 @@ tensorflow-rocm = ["tensorflow-rocm (>=2.15.0,<2.16.0)"]
 
 [[package]]
 name = "tensorflow-macos"
-version = "2.9.0"
+version = "2.16.1"
 description = "TensorFlow is an open source machine learning framework for everyone."
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.9"
 files = [
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 [package.dependencies]
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-flatbuffers = ">=1.12,<2"
-gast = ">=0.2.1,<=0.4.0"
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-termcolor = ">=1.1.0"
-typing-extensions = ">=3.6.6"
-wrapt = ">=1.11.0"
+tensorflow = {version = "2.16.1", markers = "platform_system == \"Darwin\" and platform_machine == \"arm64\""}
+
+[package.extras]
+and-cuda = ["nvidia-cublas-cu12 (==12.3.4.1)", "nvidia-cuda-cupti-cu12 (==12.3.101)", "nvidia-cuda-nvcc-cu12 (==12.3.107)", "nvidia-cuda-nvrtc-cu12 (==12.3.107)", "nvidia-cuda-runtime-cu12 (==12.3.101)", "nvidia-cudnn-cu12 (==8.9.7.29)", "nvidia-cufft-cu12 (==11.0.12.1)", "nvidia-curand-cu12 (==10.3.4.107)", "nvidia-cusolver-cu12 (==11.5.4.101)", "nvidia-cusparse-cu12 (==12.2.0.103)", "nvidia-nccl-cu12 (==2.19.3)", "nvidia-nvjitlink-cu12 (==12.3.101)"]
 
 [[package]]
 name = "tensorflow-metadata"
@@ -3733,58 +3807,51 @@ protobuf = ">=3.20.3,<4.21"
 
 [[package]]
 name = "tensorflow-text"
-version = "2.9.0"
+version = "2.16.1"
 description = "TF.Text is a TensorFlow library of text related ops, modules, and subgraphs."
 optional = false
 python-versions = "*"
 files = [
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 [package.dependencies]
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 [package.extras]
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+tensorflow-cpu = ["tensorflow-cpu (>=2.15.0,<2.16)"]
 tests = ["absl-py", "pytest", "tensorflow-datasets (>=3.2.0)"]
 
 [[package]]
 name = "tensorstore"
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 description = "Read and write large, multi-dimensional arrays"
 optional = false
 python-versions = ">=3.9"
 files = [
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 ]
 
 [package.dependencies]
@@ -3805,26 +3872,15 @@ files = [
 [package.extras]
 tests = ["pytest", "pytest-cov"]
 
-[[package]]
-name = "tf-keras"
-version = "2.15.0"
-description = "Deep learning for humans."
-optional = false
-python-versions = ">=3.8"
-files = [
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-]
-
 [[package]]
 name = "tfds-nightly"
-version = "4.9.4.dev202403130044"
+version = "4.9.4.dev202404110044"
 description = "tensorflow/datasets is a library of datasets ready to use with TensorFlow."
 optional = false
 python-versions = ">=3.10"
 files = [
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 ]
 
 [package.dependencies]
@@ -4093,34 +4149,41 @@ test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,
 
 [[package]]
 name = "typer"
-version = "0.9.0"
+version = "0.12.3"
 description = "Typer, build great CLIs. Easy to code. Based on Python type hints."
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 files = [
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+    {file = "typer-0.12.3.tar.gz", hash = "sha256:49e73131481d804288ef62598d97a1ceef3058905aa536a1134f90891ba35482"},
 ]
 
 [package.dependencies]
-click = ">=7.1.1,<9.0.0"
+click = ">=8.0.0"
+rich = ">=10.11.0"
+shellingham = ">=1.3.0"
 typing-extensions = ">=3.7.4.3"
 
-[package.extras]
-all = ["colorama (>=0.4.3,<0.5.0)", "rich (>=10.11.0,<14.0.0)", "shellingham (>=1.3.0,<2.0.0)"]
-dev = ["autoflake (>=1.3.1,<2.0.0)", "flake8 (>=3.8.3,<4.0.0)", "pre-commit (>=2.17.0,<3.0.0)"]
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-test = ["black (>=22.3.0,<23.0.0)", "coverage (>=6.2,<7.0)", "isort (>=5.0.6,<6.0.0)", "mypy (==0.910)", "pytest (>=4.4.0,<8.0.0)", "pytest-cov (>=2.10.0,<5.0.0)", "pytest-sugar (>=0.9.4,<0.10.0)", "pytest-xdist (>=1.32.0,<4.0.0)", "rich (>=10.11.0,<14.0.0)", "shellingham (>=1.3.0,<2.0.0)"]
-
 [[package]]
 name = "typing-extensions"
-version = "4.10.0"
+version = "4.11.0"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
 python-versions = ">=3.8"
 files = [
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+]
+
+[[package]]
+name = "uritemplate"
+version = "4.1.1"
+description = "Implementation of RFC 6570 URI Templates"
+optional = false
+python-versions = ">=3.6"
+files = [
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 ]
 
 [[package]]
@@ -4152,13 +4215,13 @@ files = [
 
 [[package]]
 name = "uvicorn"
-version = "0.28.0"
+version = "0.29.0"
 description = "The lightning-fast ASGI server."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -4417,13 +4480,13 @@ files = [
 
 [[package]]
 name = "werkzeug"
-version = "3.0.1"
+version = "3.0.2"
 description = "The comprehensive WSGI web application library."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -4527,13 +4590,13 @@ files = [
 
 [[package]]
 name = "zipp"
-version = "3.18.0"
+version = "3.18.1"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 optional = false
 python-versions = ">=3.8"
 files = [
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-    {file = "zipp-3.18.0.tar.gz", hash = "sha256:df8d042b02765029a09b157efd8e820451045890acc30f8e37dd2f94a060221f"},
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 ]
 
 [package.extras]
@@ -4543,4 +4606,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<3.12"
-content-hash = "cd114d91e515033c6ca7bd0cf5febd22b3d7e2689e25a4ef5c4a6f223930980f"
+content-hash = "630829af61db0eb47edf2ebf68494279ae5ee16da85f212814646abd264f645e"
diff --git a/examples/gemini/python/docs-agent/pyproject.toml b/examples/gemini/python/docs-agent/pyproject.toml
index f241d23f6..3a1aff109 100644
--- a/examples/gemini/python/docs-agent/pyproject.toml
+++ b/examples/gemini/python/docs-agent/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "docs-agent"
-version = "0.3.1"
+version = "0.3.2"
 description = ""
 authors = ["Docs Agent contributors"]
 readme = "README.md"
@@ -16,7 +16,7 @@ ratelimit = "^2.2.1"
 absl-py = "^1.4.0"
 python-frontmatter = "^1.0.0"
 flatdict = "^4.0.1"
-google-generativeai = "^0.3.1"
+google-generativeai = "^0.5.0"
 uuid = "^1.30"
 pytz = ">=2020.1"
 chromadb = "^0.4.22"
@@ -49,3 +49,5 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry.scripts]
 agent = "docs_agent.interfaces.cli:cli"
+agent_admin = "docs_agent.interfaces.cli:cli_admin"
+agent_client = "docs_agent.interfaces.cli:cli_client"

From b6446bff8148fc326881b0152a34082c56a0f5c4 Mon Sep 17 00:00:00 2001
From: Kyo Lee <kyol@google.com>
Date: Fri, 26 Apr 2024 19:39:19 +0000
Subject: [PATCH 06/12] [docs-agent] Release of Docs Agent v.0.3.3

What's changed:

- Added a new post-processing feature to delete existing chunks
  in databases if they are no longer found in source dataset.
- Updated the pre-processing module to make sure text chunks
  are not bigger than 5 KB for generating embeddings.
- Added a feature to display the distribution of chunk sizes
  after running `agent chunk`.
- Added more `agent` commandlines and options.
- Updated the "Rewrite" button to the "Feedback" button by default.
- Bug fixes.
---
 examples/gemini/python/docs-agent/README.md   |  13 +-
 .../python/docs-agent/apps_script/README.md   |   1 +
 .../apps_script/drive_to_markdown.gs          |  78 ++++--
 .../apps_script/helper_functions.gs           |  12 +-
 .../python/docs-agent/docs/cli-reference.md   |  52 ++++
 .../docs_agent/agents/docs_agent.py           |  89 ++++---
 .../benchmarks/run_benchmark_tests.py         |   4 +-
 .../docs_agent/interfaces/README.md           |  65 ++---
 .../docs_agent/interfaces/chatbot/chatui.py   | 250 ++++++++++++++----
 .../chatbot/static/css/style-chatui-1.5.css   |  54 ++++
 .../chatbot/static/css/style-chatui.css       |  55 ++++
 .../chatbot/static/css/style-widget.css       |  55 ++++
 .../chatbot/static/javascript/app.js          |  72 +++++
 .../chatbot/templates/chat-widget/base.html   |   1 +
 .../chatbot/templates/chat-widget/result.html |  36 ++-
 .../chatbot/templates/chatui-1.5/base.html    |   1 +
 .../chatbot/templates/chatui-1.5/result.html  |  36 ++-
 .../chatbot/templates/chatui/base.html        |   1 +
 .../chatbot/templates/chatui/result.html      |  36 ++-
 .../docs-agent/docs_agent/interfaces/cli.py   | 150 +++++++++--
 .../docs-agent/docs_agent/memory/logging.py   |  70 +++--
 .../docs_agent/models/google_genai.py         |   6 +-
 .../preprocess/files_to_plain_text.py         | 135 ++++++++--
 .../preprocess/populate_vector_database.py    | 238 +++++++++++++++--
 .../preprocess/splitters/markdown_splitter.py | 169 ++++++++----
 .../storage/google_semantic_retriever.py      |  59 ++++-
 .../docs-agent/docs_agent/utilities/config.py |  72 ++++-
 examples/gemini/python/docs-agent/helpme.sh   |  34 ++-
 .../gemini/python/docs-agent/pyproject.toml   |   2 +-
 examples/gemini/python/docs-agent/tellme.sh   |   5 +-
 30 files changed, 1534 insertions(+), 317 deletions(-)

diff --git a/examples/gemini/python/docs-agent/README.md b/examples/gemini/python/docs-agent/README.md
index 85d4ca610..e7f11f294 100644
--- a/examples/gemini/python/docs-agent/README.md
+++ b/examples/gemini/python/docs-agent/README.md
@@ -172,7 +172,13 @@ Authorize Google Cloud credentials on your host machine:
 
 2. Copy the `client_secret.json` file to your host machine.
 
-3. To authenticate credentials, run the following command in the directory of
+3. Install the Google Cloud SDK on your host machine:
+
+   ```
+   sudo apt install google-cloud-sdk
+   ```
+
+4. To authenticate credentials, run the following command in the directory of
    the host machine where the `client_secret.json` file is located:
 
    ```
@@ -181,10 +187,7 @@ Authorize Google Cloud credentials on your host machine:
 
    This command opens a browser and asks to log in using your Google account.
 
-   **Note**: If the `gcloud` command doesn’t exist, install the Google Cloud SDK
-   on your host machine: `sudo apt install google-cloud-sdk`
-
-4. Follow the instructions on the browser and click **Allow** to authenticate.
+5. Follow the instructions on the browser and click **Allow** to authenticate.
 
    This saves the authenticated credentials for Docs Agent
    (`application_default_credentials.json`) in the `$HOME/.config/gcloud/`
diff --git a/examples/gemini/python/docs-agent/apps_script/README.md b/examples/gemini/python/docs-agent/apps_script/README.md
index e792ace01..d333f36a8 100644
--- a/examples/gemini/python/docs-agent/apps_script/README.md
+++ b/examples/gemini/python/docs-agent/apps_script/README.md
@@ -54,6 +54,7 @@ Do the following:
 1. Repeat the steps until all the `.gs` files are copied to your Apps Script project.
 1. Click the **+** icon next to **Services**.
 1. Scroll down and click **Drive API**.
+1. Select **v2**.
 1. Click **Add**.
 
 You are now ready to edit the parameters on the `main.gs` file to select a folder
diff --git a/examples/gemini/python/docs-agent/apps_script/drive_to_markdown.gs b/examples/gemini/python/docs-agent/apps_script/drive_to_markdown.gs
index b6204fc5e..b19d33d84 100644
--- a/examples/gemini/python/docs-agent/apps_script/drive_to_markdown.gs
+++ b/examples/gemini/python/docs-agent/apps_script/drive_to_markdown.gs
@@ -13,25 +13,55 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
+function convertDriveFolderToMDForDocsAgent(folderName, outputFolderName=""){
+  gdoc_count = 0;
+  pdf_count = 0;
+  new_file_count = 0;
+  updated_file_count = 0;
+  unchanged_file_count = 0;
+  gdoc_count, pdf_count, new_file_count, updated_file_count, unchanged_file_count = convertDriveFolder(folderName, outputFolderName=outputFolderName)
+  let conversion_count = pdf_count + gdoc_count
+  let file_count = new_file_count + updated_file_count + unchanged_file_count
+  Logger.log("Converted a total of: " + gdoc_count + " Google Doc files.");
+  Logger.log("Converted a total of: " + pdf_count + " PDF files.");
+  Logger.log("Converted a grand total of: " + conversion_count + " files.");
+  Logger.log("New files: " + new_file_count)
+  Logger.log("Updated a total of: " + updated_file_count + " files.")
+  Logger.log("Files that haven't changed: " + unchanged_file_count);
+  Logger.log("Input directory had a total of: " + file_count + " files.")
+}
+
+function convertDriveFolder(folderName, outputFolderName="", indexFile="") {
 
-function convertDriveFolderToMDForDocsAgent(folderName) {
   //Checks if input folder exists or exits
   if(folderExistsInput(folderName)){
     var file_count = 0;
     var folders = DriveApp.getFoldersByName(folderName);
-    Logger.log("Output directory: "+ folderName + "-output");
-    var folderOutput = folderName + "-output";
-    var output_file_name = folderName + "-index";
+    if (outputFolderName=="") {
+      var folderOutput = folderName + "-output";
+      var output_file_name = folderName + "-index";
+    }
+    else {
+      var folderOutput = outputFolderName + "-output";
+      var output_file_name = outputFolderName + "-index";
+    }
+    Logger.log("Output directory: "+ folderOutput);
     folderExistsOrCreate(folderOutput);
     var folderOutputObj = DriveApp.getFoldersByName(folderOutput);
     if (folderOutputObj.hasNext()){
       var folderOutputName = folderOutputObj.next();
     }
-    var sheet = checkIndexOutputOrCreate(output_file_name, folderOutputName);
-    var timeZone = Session.getScriptTimeZone();
-    var date = Utilities.formatDate(new Date(), timeZone, "MM-dd-yyyy HH:mm:ss z");
-    sheet.appendRow(["Created: ", date])
-    sheet.appendRow(["Name","ID", "URL", "Markdown ID", "Markdown Output", "Date Created", "Last Updated", "Type", "Folder", "MD5 hash", "Status"]);
+    if (indexFile=="") {
+      var sheet = checkIndexOutputOrCreate(output_file_name, folderOutputName);
+      var timeZone = Session.getScriptTimeZone();
+      var date = Utilities.formatDate(new Date(), timeZone, "MM-dd-yyyy HH:mm:ss z");
+      sheet.appendRow(["Created: ", date])
+      sheet.appendRow(["Name","ID", "URL", "Markdown ID", "Markdown Output", "Date Created", "Last Updated", "Type", "Folder", "MD5 hash", "Status"]);
+    }
+    else {
+      var sheet = indexFile
+    }
+    // var sheet_id = sheet.getId();
     var foldersnext = folders.next();
     var myfiles = foldersnext.getFiles();
     var new_file_count = 0;
@@ -54,6 +84,22 @@ function convertDriveFolderToMDForDocsAgent(folderName) {
       else{
         var fid = myfile.getId();
       }
+      if (ftype == "application/vnd.google-apps.folder") {
+        var folder = DriveApp.getFolderById(fid);
+        Logger.log("Sub-directory: " + folder);
+        sub_gdoc_count = 0;
+        sub_pdf_count = 0;
+        sub_new_file_count = 0;
+        sub_updated_file_count = 0;
+        sub_unchanged_file_count = 0;
+        sub_gdoc_count, sub_pdf_count, sub_new_file_count, sub_updated_file_count, sub_unchanged_file_count = convertDriveFolder(folder, outputFolderName=foldersnext, indexFile=sheet);
+        gdoc_count += sub_gdoc_count;
+        pdf_count += sub_pdf_count;
+        new_file_count += sub_new_file_count;
+        updated_file_count += sub_updated_file_count;
+        unchanged_file_count += sub_unchanged_file_count;
+        continue;
+      }
       var fname = sanitizeFileName(myfile.getName());
       var fdate = myfile.getLastUpdated();
       var furl = myfile.getUrl();
@@ -158,7 +204,7 @@ function convertDriveFolderToMDForDocsAgent(folderName) {
           var saved_file_id = saved_file.getId();
           Logger.log("Finished converting file: "+ fname + " to markdown.");
           Logger.log("Markdown file: " + saved_file);
-          Logger.log("Clearing temporary gdoc: " );
+          Logger.log("Clearing temporary gdoc" );
           let output_file = DriveApp.getFileById(output_id);
           output_file.setTrashed(true);
           status = "New content";
@@ -182,19 +228,13 @@ function convertDriveFolderToMDForDocsAgent(folderName) {
         hash_str,
         status,
       ];
-      row_number = file_count + start_data_row;
       sheet.appendRow(metadata);
+      // Return final row to inserRichText into correct rows
+      row_number = sheet.getLastRow();
       insertRichText(sheet, original_chip, "C", row_number);
       insertRichText(sheet, md_chip, "E", row_number);
       insertRichText(sheet, folder_chip, "I", row_number);
     }
   }
-  let conversion_count = pdf_count + gdoc_count
-  Logger.log("Converted a total of: " + gdoc_count + " Google Doc files.");
-  Logger.log("Converted a total of: " + pdf_count + " PDF files.");
-  Logger.log("Converted a grand total of: " + conversion_count + " files.");
-  Logger.log("New files: " + new_file_count)
-  Logger.log("Updated a total of: " + updated_file_count + " files.")
-  Logger.log("Files that haven't changed: " + unchanged_file_count);
-  Logger.log("Input directory had a total of: " + file_count + " files.")
+  return gdoc_count, pdf_count, new_file_count, updated_file_count, unchanged_file_count
 }
diff --git a/examples/gemini/python/docs-agent/apps_script/helper_functions.gs b/examples/gemini/python/docs-agent/apps_script/helper_functions.gs
index 129e74329..020897be4 100644
--- a/examples/gemini/python/docs-agent/apps_script/helper_functions.gs
+++ b/examples/gemini/python/docs-agent/apps_script/helper_functions.gs
@@ -67,18 +67,24 @@ function checkFileExists(fileName,folderName){
 
 // Function to check if an index output sheet exists or creates it. Returns the file object
 // Specify the file output name and outputdirectory
-function checkIndexOutputOrCreate(fileName, folderOutput) {
+function checkIndexOutputOrCreate(fileName, folderOutput, indexFileID="") {
   var timeZone = Session.getScriptTimeZone();
   var date = Utilities.formatDate(new Date(), timeZone, "MM-dd-yyyy hh:mm:ss");
   let file = {title: fileName, mimeType: MimeType.GOOGLE_SHEETS, parents: [{id: folderOutput.getId()}]}
   let params = "title='" + fileName + "' and parents in '" + folderOutput.getId() + "'";
   let file_search = DriveApp.searchFiles(params);
   if (file_search.hasNext()) {
-    let fileId = file_search.next().getId();
+    if (indexFileID=="") {
+      var fileId = file_search.next().getId();
+    }
+    else {
+      var fileId = indexFileID;
+    }
     var sheet = SpreadsheetApp.openById(fileId);
     Logger.log("File index: " + fileName + " exists.");
     var sheet_index = sheet.getSheetByName("Index");
-    if (sheet.getSheetByName("Backup")){
+    // Checks to see if this is a sub directory
+    if (sheet.getSheetByName("Backup")) {
       var sheet_backup = sheet.getSheetByName("Backup");
       sheet.deleteSheet(sheet_backup);
     }
diff --git a/examples/gemini/python/docs-agent/docs/cli-reference.md b/examples/gemini/python/docs-agent/docs/cli-reference.md
index 8e32da7a0..c3d3100d9 100644
--- a/examples/gemini/python/docs-agent/docs/cli-reference.md
+++ b/examples/gemini/python/docs-agent/docs/cli-reference.md
@@ -26,6 +26,15 @@ by running the `agent chunk` command):
 agent populate
 ```
 
+### Populate a vector database and delete stale text chunks
+
+The command below deletes stale entries in the existing vector database
+before populating it with the new text chunks:
+
+```sh
+agent populate --enable_delete_chunks
+```
+
 ### Show the Docs Agent configuration
 
 The command below prints all the fields and values in the current
@@ -35,6 +44,15 @@ The command below prints all the fields and values in the current
 agent show-config
 ```
 
+### Clean up the Docs Agent development environment
+
+The command below deletes development databases specified in the
+`config.yaml` file:
+
+```sh
+agent cleanup-dev
+```
+
 ## Docs Agent chatbot web app
 
 ### Launch the Docs Agent web app
@@ -53,6 +71,24 @@ The command below launches the Docs Agent web app to run on port 5005:
 agent chatbot --port 5005
 ```
 
+### Launch the Docs Agent web app as a widget
+
+The command below launches the Docs Agent web app to use
+a widget-friendly template:
+
+```sh
+agent chatbot --app_mode widget
+```
+
+### Launch the Docs Agent web app with a log view enabled
+
+The command below launches the Docs Agent web app while enabling
+a log view page (which is accessible at `<APP_URL>/logs`):
+
+```sh
+agent chatbot --enable_show_logs
+```
+
 ## Docs Agent benchmark test
 
 ### Run the Docs Agent benchmark test
@@ -106,6 +142,22 @@ You may also specify multiple products, for example:
 agent tellme which modules are available? --product=Flutter --product=Angular --product=Android
 ```
 
+### Ask for advice
+
+The command below reads a request and a filename from the arguments,
+asks the Gemini model, and prints its response:
+
+```sh
+agent helpme <REQUEST> --file <PATH_TO_FILE>
+```
+
+Replace `REQUEST` with a prompt and `PATH_TO_FILE` with a file's
+absolure or relative path, for example:
+
+```sh
+agent helpme write comments for this C++ file? --file ../my-project/test.cc
+```
+
 ## Online corpus management
 
 ### List all existing online corpora
diff --git a/examples/gemini/python/docs-agent/docs_agent/agents/docs_agent.py b/examples/gemini/python/docs-agent/docs_agent/agents/docs_agent.py
index 27d59af1a..2bb8fd0d6 100644
--- a/examples/gemini/python/docs-agent/docs_agent/agents/docs_agent.py
+++ b/examples/gemini/python/docs-agent/docs_agent/agents/docs_agent.py
@@ -41,7 +41,7 @@ class DocsAgent:
     """DocsAgent class"""
 
     # Temporary parameter of init_chroma
-    def __init__(self, config: ProductConfig, init_chroma: bool = True):
+    def __init__(self, config: ProductConfig, init_chroma: bool = True, init_semantic: bool = True):
         # Models settings
         self.config = config
         self.embedding_model = str(self.config.models.embedding_model)
@@ -65,33 +65,34 @@ def __init__(self, config: ProductConfig, init_chroma: bool = True):
                 ),
             )
         # AQA model settings
-        if self.config.models.language_model == "models/aqa":
-            # AQA model setup
-            self.generative_service_client = glm.GenerativeServiceClient()
-            self.retriever_service_client = glm.RetrieverServiceClient()
-            self.permission_service_client = glm.PermissionServiceClient()
-            # Start a Gemini model for other tasks
-            self.context_model = "models/gemini-pro"
-            gemini_model_config = Models(
-                language_model=self.context_model,
-                embedding_model=self.embedding_model,
-                api_endpoint=self.api_endpoint,
-            )
-            self.gemini = Gemini(
-                models_config=gemini_model_config, conditions=config.conditions
-            )
-        # Semantic retriever
-        if self.config.db_type == "google_semantic_retriever":
-            for item in self.config.db_configs:
-                if "google_semantic_retriever" in item.db_type:
-                    self.corpus_name = item.corpus_name
-                    if item.corpus_display:
-                        self.corpus_display = item.corpus_display
-                    else:
-                        self.corpus_display = (
-                            self.config.product_name + " documentation"
-                        )
-            self.aqa_response_buffer = ""
+        if init_semantic:
+            if self.config.models.language_model == "models/aqa":
+                # AQA model setup
+                self.generative_service_client = glm.GenerativeServiceClient()
+                self.retriever_service_client = glm.RetrieverServiceClient()
+                self.permission_service_client = glm.PermissionServiceClient()
+                # Start a Gemini model for other tasks
+                self.context_model = "models/gemini-pro"
+                gemini_model_config = Models(
+                    language_model=self.context_model,
+                    embedding_model=self.embedding_model,
+                    api_endpoint=self.api_endpoint,
+                )
+                self.gemini = Gemini(
+                    models_config=gemini_model_config, conditions=config.conditions
+                )
+            # Semantic retriever
+            if self.config.db_type == "google_semantic_retriever":
+                for item in self.config.db_configs:
+                    if "google_semantic_retriever" in item.db_type:
+                        self.corpus_name = item.corpus_name
+                        if item.corpus_display:
+                            self.corpus_display = item.corpus_display
+                        else:
+                            self.corpus_display = (
+                                self.config.product_name + " documentation"
+                            )
+                self.aqa_response_buffer = ""
 
         if self.config.models.language_model.startswith("models/gemini"):
             self.gemini = Gemini(
@@ -132,10 +133,10 @@ def ask_content_model_with_context(self, context, question):
             response = self.gemini.generate_content(new_prompt)
         except google.api_core.exceptions.InvalidArgument:
             return self.config.conditions.model_error_message
-        for chunk in response:
-            if str(chunk.candidates[0].content) == "":
-                return self.config.conditions.model_error_message
-        return response.text
+        # for chunk in response:
+        #     if str(chunk.candidates[0].content) == "":
+        #         return self.config.conditions.model_error_message
+        return response
 
     # Use this method for talking to Gemini's AQA model using inline passages
     # answer_style can be VERBOSE, ABSTRACTIVE, or EXTRACTIVE
@@ -223,15 +224,19 @@ def get_aqa_response_metadata(self, aqa_response_item):
 
     # Use this method for talking to Gemini's AQA model using a corpus
     # Answer style can be "VERBOSE" or ABSTRACTIVE, EXTRACTIVE
-    def ask_aqa_model_using_corpora(self, question, answer_style: str = "VERBOSE"):
+    def ask_aqa_model_using_corpora(
+        self, question, corpus_name: str = "None", answer_style: str = "VERBOSE"
+    ):
         search_result = []
+        if corpus_name == "None":
+            corpus_name = self.corpus_name
         # Prepare parameters for the AQA model
         user_question_content = glm.Content(
             parts=[glm.Part(text=question)], role="user"
         )
         # Settings to retrieve grounding content from semantic retriever
         retriever_config = glm.SemanticRetrieverConfig(
-            source=self.corpus_name, query=user_question_content
+            source=corpus_name, query=user_question_content
         )
 
         if self.config.models.language_model == "models/aqa":
@@ -485,10 +490,10 @@ def ask_content_model_with_context_prompt(
                 response = self.gemini.generate_content(contents=new_prompt)
         except:
             return self.config.conditions.model_error_message, new_prompt
-        for chunk in response:
-            if str(chunk.candidates[0].content) == "":
-                return self.config.conditions.model_error_message, new_prompt
-        return response.text, new_prompt
+        # for chunk in response:
+        #     if str(chunk.candidates[0].content) == "":
+        #         return self.config.conditions.model_error_message, new_prompt
+        return response, new_prompt
 
     # Use this method for talking to a Gemini content model
     # Provide a prompt, followed by the content of the file
@@ -502,10 +507,10 @@ def ask_content_model_to_use_file(self, prompt: str, file: str):
             response = self.gemini.generate_content(contents=new_prompt)
         except google.api_core.exceptions.InvalidArgument:
             return self.config.conditions.model_error_message
-        for chunk in response:
-            if str(chunk.candidates[0].content) == "":
-                return self.config.conditions.model_error_message
-        return response.text
+        # for chunk in response:
+        #     if str(chunk.candidates[0].content) == "":
+        #         return self.config.conditions.model_error_message
+        return response
 
     # Use this method for asking a Gemini content model for fact-checking.
     # This uses ask_content_model_with_context_prompt w
diff --git a/examples/gemini/python/docs-agent/docs_agent/benchmarks/run_benchmark_tests.py b/examples/gemini/python/docs-agent/docs_agent/benchmarks/run_benchmark_tests.py
index 925329880..fe15f9714 100644
--- a/examples/gemini/python/docs-agent/docs_agent/benchmarks/run_benchmark_tests.py
+++ b/examples/gemini/python/docs-agent/docs_agent/benchmarks/run_benchmark_tests.py
@@ -150,7 +150,7 @@ def run_benchmarks():
         embedding_01 = docs_agent.generate_embedding(target_answer)
         vprint("")
         vprint("Embedding:")
-        vprint(embedding_01)
+        vprint(str(embedding_01))
 
         # Step 3. Ask `question` to the AI model.
         response = ask_model(question, docs_agent)
@@ -168,7 +168,7 @@ def run_benchmarks():
         embedding_02 = docs_agent.generate_embedding(response)
         vprint("")
         vprint("Embedding:")
-        vprint(embedding_02)
+        vprint(str(embedding_02))
 
         # Step 5. Compute the similarity between Embedding 1 and Embedding 2.
         vprint("################")
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/README.md b/examples/gemini/python/docs-agent/docs_agent/interfaces/README.md
index 54c31e86e..adcb880b1 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/README.md
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/README.md
@@ -49,26 +49,26 @@ Setting up Docs Agent requires the following prerequisite items:
 
 1. Update the Linux package repositories on the host machine:
 
-   ```posix-terminal
+   ```
    sudo apt update
    ```
 
 2. Install the following dependencies:
 
-   ```posix-terminal
+   ```
    sudo apt install git pipx python3-venv
    ```
 
 3. Install `poetry`:
 
-   ```posix-terminal
+   ```
    pipx install poetry
    ```
 
 4. To add `$HOME/.local/bin` to your `PATH` variable, run the following
    command:
 
-   ```posix-terminal
+   ```
    pipx ensurepath
    ```
 
@@ -84,7 +84,7 @@ Setting up Docs Agent requires the following prerequisite items:
 
 6. Update your environment:
 
-   ```posix-termainl
+   ```
    source ~/.bashrc
    ```
 
@@ -98,7 +98,13 @@ credentials (via `gcloud`) stored on your host machine.
 
 2. Copy the `client_secret.json` file to your host machine.
 
-3. To authenticate credentials, run the following command in the directory of
+3. Install the Google Cloud SDK on your host machine:
+
+   ```
+   sudo apt install google-cloud-sdk
+   ```
+
+4. To authenticate credentials, run the following command in the directory of
    the host machine where the `client_secret.json` file is located:
 
    ```
@@ -107,10 +113,7 @@ credentials (via `gcloud`) stored on your host machine.
 
    This command opens a browser and asks to log in using your Google account.
 
-   **Note**: If the `gcloud` command doesn’t exist, install the Google Cloud SDK
-   on your host machine: `sudo apt install google-cloud-sdk`
-
-4. Follow the instructions on the browser and click **Allow** to authenticate.
+5. Follow the instructions on the browser and click **Allow** to authenticate.
 
    This saves the authenticated credentials for Docs Agent
    (`application_default_credentials.json`) in the `$HOME/.config/gcloud/`
@@ -123,19 +126,19 @@ from your `$HOME` directory.
 
 1. Clone the following repo:
 
-   ```posix-terminal
+   ```
    git clone https://github.com/google/generative-ai-docs.git
    ```
 
 2. Go to the Docs Agent project directory:
 
-   ```posix-terminal
+   ```
    cd generative-ai-docs/examples/gemini/python/docs-agent
    ```
 
 3. Install dependencies using `poetry`:
 
-   ```posix-terminal
+   ```
    poetry install
    ```
 
@@ -149,7 +152,7 @@ from anywhere in the terminal:
 
 1. (**Optional**) Open the `tellme.sh` file using a text editor, for example:
 
-   ```sh
+   ```
    nano tellme.sh
    ```
 
@@ -157,14 +160,8 @@ from anywhere in the terminal:
    `docs-agent` project directory on the host machine:
 
    ```
-   #!/bin/bash
-
-   # Check if the POETRY_ACTIVE environment variable is set
-   if [ -z "$POETRY_ACTIVE" ]; then
-       cd $HOME/docs-agent && poetry run agent tellme $@
-   else
-       agent tellme $@
-   fi
+   # IF NECESSARY, ADJUST THIS PATH TO YOUR `docs-agent` DIRECTORY.
+   docs_agent_dir="$HOME/docs-agent"
    ```
 
    Save the file and close text editor.
@@ -181,13 +178,13 @@ from anywhere in the terminal:
 
 3. Update your environment:
 
-   ```sh
+   ```
    source ~/.bashrc
    ```
 
 4. Now you can run the `gemini` command from anywhere in your terminal:
 
-   ```sh
+   ```
    gemini <QUESTION>
    ```
 
@@ -229,7 +226,7 @@ To set up this `helpme` command in your terminal, do the following:
 
 1. (**Optional**) Open the `helpme.sh` file using a text editor, for example:
 
-   ```sh
+   ```
    nano helpme.sh
    ```
 
@@ -237,17 +234,11 @@ To set up this `helpme` command in your terminal, do the following:
    `docs-agent` project directory on the host machine:
 
    ```
-   #!/bin/bash
-
-   # Check if the POETRY_ACTIVE environment variable is set
-   if [ -z "$POETRY_ACTIVE" ]; then
-       cd $HOME/docs-agent && poetry run agent helpme $@
-   else
-       agent helpme $@
-   fi
+   # IF NECESSARY, ADJUST THIS PATH TO YOUR `docs-agent` DIRECTORY.
+   docs_agent_dir="$HOME/docs-agent"
    ```
 
-   Save the file and close text editor.
+   Save the file and close the text editor.
 
 2. Add the following `alias` lines to your `$HOME/.bashrc` file:
 
@@ -263,14 +254,14 @@ To set up this `helpme` command in your terminal, do the following:
 
 3. Update your environment:
 
-   ```sh
+   ```
    source ~/.bashrc
    ```
 
 4. When you are ready to let Docs Agent to read output from your terminal,
    run the following command:
 
-   ```sh
+   ```
    start_agent
    ```
 
@@ -291,7 +282,7 @@ To set up this `helpme` command in your terminal, do the following:
 6. To use the latest output from your terminal, run the `gemini-pro` command
    immediately after the output:
 
-   ```sh
+   ```
    gemini-pro <REQUEST>
    ```
 
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/chatui.py b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/chatui.py
index 1f0955d18..4d41a7abf 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/chatui.py
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/chatui.py
@@ -26,6 +26,7 @@
 from absl import logging
 import pytz
 import uuid
+import re
 
 from docs_agent.utilities.helpers import (
     parse_related_questions_response_to_html_list,
@@ -49,9 +50,15 @@ def construct_blueprint(
 ):
     bp = Blueprint("chatui", __name__)
     if product_config.db_type == "google_semantic_retriever":
-        docs_agent = DocsAgent(config=product_config, init_chroma=False)
+        if product_config.secondary_db_type == "chroma":
+            docs_agent = DocsAgent(config=product_config, init_chroma=True)
+        else:
+            # A local Chroma DB is not needed for the Semantic Retreiver only mode.
+            docs_agent = DocsAgent(config=product_config, init_chroma=False)
+    elif product_config.db_type == "none":
+        docs_agent = DocsAgent(config=product_config, init_chroma=False, init_semantic=False)
     else:
-        docs_agent = DocsAgent(config=product_config)
+        docs_agent = DocsAgent(config=product_config, init_chroma=True)
     logging.info(
         f"Launching the Flask app for product: {product_config.product_name} with app_mode: {app_mode}"
     )
@@ -168,6 +175,55 @@ def rewrite():
         else:
             return redirect(url_for(redirect_index))
 
+    @bp.route("/feedback", methods=["GET", "POST"])
+    def feedback():
+        # Create the 'feedback' directory if it does not exist.
+        feedback_dir = "feedback"
+        is_exist = os.path.exists(feedback_dir)
+        if not is_exist:
+            os.makedirs(feedback_dir)
+        if request.method == "POST":
+            json_data = json.loads(request.data)
+            user_id = json_data.get("user_id")
+            question = json_data.get("question")
+            response = json_data.get("response")
+            feedback = json_data.get("feedback")
+            date_format = "%m%d%Y-%H%M%S"
+            date = datetime.now(tz=pytz.utc)
+            date = date.astimezone(pytz.timezone("US/Pacific"))
+            print(
+                "[" + date.strftime(date_format) + "] A user has submitted feedback."
+            )
+            print("Submitted by: " + user_id + "\n")
+            print("# " + question.strip() + "\n")
+            print("## Response\n")
+            print(response.strip() + "\n")
+            print("## Feedback\n")
+            print(feedback + "\n")
+            filename = (
+                feedback_dir
+                + "/"
+                + question.strip()
+                .replace(" ", "-")
+                .replace("?", "")
+                .replace("'", "")
+                .lower()
+                + "-"
+                + date.strftime(date_format)
+                + ".md"
+            )
+            with open(filename, "w", encoding="utf-8") as file:
+                file.write("Submitted by: " + user_id + "\n\n")
+                file.write("# " + question.strip() + "\n\n")
+                file.write("## Response\n\n")
+                file.write(response.strip() + "\n\n")
+                file.write("## Feedback\n\n")
+                file.write(feedback + "\n")
+                file.close()
+            return "OK"
+        else:
+            return redirect(url_for(redirect_index))
+
     # Render a response page when the user asks a question
     # using input text box.
     @bp.route("/result", methods=["GET", "POST"])
@@ -196,6 +252,19 @@ def logs():
     return bp
 
 
+# Go through the `seatch_result` object returned from the AQA model
+# and extract context.
+def extract_context_from_search_result(search_result):
+    context = ""
+    context_count = 0
+    for item in search_result:
+        context_count += 1
+        # Add a "Reference[#]" line at the end of each context.
+        context += item.section.content + "\nReference [" + str(context_count) + "]\n\n"
+    context = context.strip()
+    return context
+
+
 # Construct a set of prompts using the user question, send the prompts to
 # the lanaguage model, receive responses, and present them into a page.
 # Use template to specify a custom template for the classic web UI
@@ -208,30 +277,47 @@ def ask_model(question, agent, template: str = "chatui/index.html"):
     new_question_count = 5
     results_num = 5
     aqa_response_in_html = ""
+
+    # Debugging feature: Do not log this question if it ends with `?do_not_log`.
+    can_be_logged = "True"
+    question_match = re.search(r"^(.*)\?do_not_log$", question)
+    if question_match:
+        # Update the question to remove `do_not_log`.
+        question = question_match[1] + "?"
+        can_be_logged = "False"
+
+    # Retrieve context and ask the question.
     if "gemini" in docs_agent.config.models.language_model:
+        # For the `gemini-*` model
         if docs_agent.config.docs_agent_config == "experimental":
             results_num = 10
             new_question_count = 5
         else:
             results_num = 5
             new_question_count = 5
-        # Issue if max_sources > results_num, so leave the same for now
-        search_result, final_context = docs_agent.query_vector_store_to_build(
-            question=question,
-            token_limit=30000,
-            results_num=results_num,
-            max_sources=results_num,
-        )
-        response, full_prompt = docs_agent.ask_content_model_with_context_prompt(
-            context=final_context, question=question
-        )
-        aqa_response_in_html = ""
-    elif "aqa" in docs_agent.config.models.language_model:
-        if docs_agent.config.db_type == "google_semantic_retriever":
-            (response, search_result) = docs_agent.ask_aqa_model_using_corpora(
-                question=question
+        # Note: Error if max_sources > results_num, so leave the same for now.
+        if docs_agent.config.db_type == "none":
+            search_result = []
+            final_context = ""
+            # response = ask_content_model_with_context(context="", question=question)
+            # Issue if max_sources > results_num, so leave the same for now
+        else:
+            search_result, final_context = docs_agent.query_vector_store_to_build(
+                question=question,
+                token_limit=30000,
+                results_num=results_num,
+                max_sources=results_num,
             )
-        elif docs_agent.config.db_type == "chroma":
+        try:
+            response, full_prompt = docs_agent.ask_content_model_with_context_prompt(
+                context=final_context, question=question
+            )
+            aqa_response_in_html = ""
+        except:
+            logging.error("Failed to ask content model with context prompt.")
+    elif "aqa" in docs_agent.config.models.language_model:
+        # For the AQA model
+        if docs_agent.config.db_type == "chroma":
             (
                 response,
                 search_result,
@@ -242,20 +328,55 @@ def ask_model(question, agent, template: str = "chatui/index.html"):
             (response, search_result) = docs_agent.ask_aqa_model_using_corpora(
                 question=question
             )
-        # Buid a final_context item
-        context_count = 0
-        for item in search_result:
-            context_count += 1
-            final_context += (
-                item.section.content + "\nReference [" + str(context_count) + "]\n\n"
-            )
-        final_context = final_context.strip()
+        # Extract context from this AQA model's response.
+        final_context = extract_context_from_search_result(search_result)
+        # Save this AQA model's response.
         aqa_response_json = docs_agent.get_saved_aqa_response_json()
+        # Convert this AQA model's response to HTML for better rendering.
         if aqa_response_json:
             aqa_response_in_html = json.dumps(
                 type(aqa_response_json).to_dict(aqa_response_json), indent=2
             )
 
+    ### Check the AQA model's answerable_probability field
+    probability = "None"
+    if docs_agent.check_if_aqa_is_used():
+        aqa_response = docs_agent.get_saved_aqa_response_json()
+        try:
+            probability = aqa_response.answerable_probability
+        except:
+            probability = 0.0
+
+    # For the 1.5 mode, retrieve additional context from the secondary knowledge database.
+    additional_context = ""
+    if docs_agent.config.app_mode == "1.5":
+        if docs_agent.config.secondary_db_type == "chroma":
+            (
+                additional_search_result,
+                additional_context,
+            ) = docs_agent.query_vector_store_to_build(
+                question=question,
+                token_limit=30000,
+                results_num=5,
+                max_sources=5,
+            )
+            # Extract context from this search result.
+            additional_context = extract_context_from_search_result(
+                additional_search_result
+            )
+        elif docs_agent.config.secondary_db_type == "google_semantic_retriever":
+            (
+                additional_response,
+                additional_search_result,
+            ) = docs_agent.ask_aqa_model_using_corpora(
+                question=question,
+                corpus_name=str(docs_agent.config.secondary_corpus_name),
+            )
+            # Extract context from this search result.
+            additional_context = extract_context_from_search_result(
+                additional_search_result
+            )
+
     ### PROMPT: GET RELATED QUESTIONS.
     # 1. Use the response from Prompt 1 as context and add a custom condition.
     # 2. Prepare a new question asking the model to come up with 5 related questions.
@@ -263,54 +384,72 @@ def ask_model(question, agent, template: str = "chatui/index.html"):
     # 4. Parse the model's response into a list in HTML format.
     new_condition = f"Read the context below and answer the question at the end:"
     new_question = f"Can you think of {new_question_count} questions whose answers can be found in the context above?"
-    (
-        related_questions_response,
-        new_prompt_questions,
-    ) = docs_agent.ask_content_model_with_context_prompt(
-        context=final_context,
-        question=new_question,
-        prompt=new_condition,
-        model="gemini-pro",
-    )
-    # Clean up the response to a proper html list
-    related_questions = parse_related_questions_response_to_html_list(
-        markdown.markdown(related_questions_response)
-    )
+    try:
+        (
+            related_questions_response,
+            new_prompt_questions,
+        ) = docs_agent.ask_content_model_with_context_prompt(
+            context=final_context,
+            question=new_question,
+            prompt=new_condition,
+            model="gemini-pro",
+        )
+        # Clean up the response to a proper html list
+        related_questions = parse_related_questions_response_to_html_list(
+            markdown.markdown(related_questions_response)
+        )
+    except:
+        related_questions = ""
+        logging.error("Failed to ask content model with context prompt.")
 
     ### PREPARE OTHER ELEMENTS NEEDED BY UI.
     # - Create a uuid for this request.
     # - A workaround to get the server's URL to work with the rewrite and like features.
     new_uuid = uuid.uuid1()
     server_url = request.url_root.replace("http", "https")
-    ### Check the AQA model's answerable_probability field
-    probability = "None"
-    if docs_agent.check_if_aqa_is_used():
-        aqa_response = docs_agent.get_saved_aqa_response_json()
-        try:
-            probability = aqa_response.answerable_probability
-        except:
-            probability = 0.0
 
     ### The code below is added for the new Gemini 1.5 model.
     # Ask the Gemini 1.5 model to generate a full summary.
-    if docs_agent.config.app_mode == "1.5":
-        new_condition = f"Read the context below and provide a detailed overview to address the question at the end:"
+    if docs_agent.config.app_mode == "1.5" and docs_agent.config.db_type != "none":
+        if additional_context != "":
+            extended_context = f"RELEVANT CONTEXT FOUND IN DOCUMENTATION:\n\n{additional_context}\n\nRELEVANT CONVERSATIONS:\n\n{final_context}\n"
+        else:
+            extended_context = f"{final_context}\n"
+        additional_condition = (
+            "DO NOT INCLUDE THE NAMES OF PEOPLE FOUND IN CONVERSATIONS"
+        )
+        new_condition = f"Read the context below and provide a detailed overview to address the question at the end ({additional_condition}):"
         (
             summary_response,
             summary_prompt,
         ) = docs_agent.ask_content_model_with_context_prompt(
-            context=final_context,
+            context=extended_context,
             question=question,
             prompt=new_condition,
             model="gemini-1.5-pro",
         )
-        log_lines = f"{response}\n\nGemini 1.5 response:\n\n{summary_response}"
+        log_lines = f"{response}\n\n{summary_response}"
     else:
         summary_response = ""
         log_lines = f"{response}"
-
+ 
     ### LOG THIS REQUEST.
-    log_question(new_uuid, question, log_lines, probability)
+    if docs_agent.config.enable_logs_to_markdown == "True":
+        log_question(
+            new_uuid,
+            question,
+            log_lines,
+            probability,
+            save=can_be_logged,
+            logs_to_markdown="True",
+        )
+    else:
+        log_question(new_uuid, question, log_lines, probability, save=can_be_logged)
+
+    ### Check the feedback mode in the `config.yaml` file.
+    feedback_mode = "feedback"
+    if hasattr(docs_agent.config, "feedback_mode") and  docs_agent.config.feedback_mode == "rewrite":
+        feedback_mode = "rewrite"
 
     return render_template(
         template,
@@ -327,6 +466,7 @@ def ask_model(question, agent, template: str = "chatui/index.html"):
         final_context=final_context,
         search_result=search_result,
         summary_response=summary_response,
+        feedback_mode=feedback_mode,
     )
 
 
@@ -356,8 +496,8 @@ def ask_model_with_sources(question, agent):
 def show_logs(agent, template: str = "admin/logs.html"):
     docs_agent = agent
     product = docs_agent.config.product_name
-    log_filename = "chatui_logs.txt"
-    answerable_log_filename = "answerable_logs.txt"
+    log_filename = "logs/chatui_logs.txt"
+    answerable_log_filename = "logs/answerable_logs.txt"
     log_contents = ""
     answerable_contents = ""
     if docs_agent.config.enable_show_logs == "True":
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui-1.5.css b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui-1.5.css
index 4fd25c627..a076ba1d5 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui-1.5.css
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui-1.5.css
@@ -203,6 +203,10 @@ body {
     margin-top: 12px;
   }
 
+  #feedback-buttons-box {
+    margin-top: 12px;
+  }
+
   #answerable-span {
     font-size: small;
     font-family: system-ui;
@@ -261,6 +265,13 @@ body {
     border-radius: 15px;
   }
 
+  .feedback {
+    padding: 15px;
+    border: 2px solid #000;
+    margin-top: 6px;
+    border-radius: 15px;
+  }
+
   .question, .response, .response-text, .fact-checked-text {
     max-width: 700px;
     margin-left: 3px;
@@ -294,6 +305,21 @@ body {
     cursor:pointer;
   }
 
+  #feedback-button {
+    border: 0;
+    background-color: #cf633ff2;
+    color: #fff;
+    padding: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+    margin-top: 0.3em;
+  }
+
+  #feedback-button:hover {
+    background: #ce3705f2;
+    cursor:pointer;
+  }
+
   #like-button {
     border: 0;
     color: #fff;
@@ -322,6 +348,25 @@ body {
     color: #027f02d6;
     font-family: fantasy;
   }
+  #feedback-submit-button {
+    border: 0;
+    background: none;
+    background-color: #CF5C3F;
+    color: #fff;
+    padding: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+  }
+
+  #feedback-submit-button:hover {
+    background: #ce3705f2;
+    cursor:pointer;
+  }
+
+  #feedback-submit-result {
+    color: #027f02d6;
+    font-family: fantasy;
+  }
 
   #edit-text-area {
     font: 13px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
@@ -332,6 +377,15 @@ body {
     padding: 8px;
   }
 
+  #feedback-text-area {
+    font: 13px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
+    max-height: 500px;
+    max-width: -webkit-fill-available;
+    height: 300px;
+    width: 580px;
+    padding: 8px;
+  }
+
   #rewrite-question-header {
     margin: 0;
     margin-bottom: 5px;
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui.css b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui.css
index 7155e750d..8ee1fa684 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui.css
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui.css
@@ -178,6 +178,10 @@ body {
     margin-top: 12px;
   }
 
+  #feedback-buttons-box {
+    margin-top: 12px;
+  }
+
   #answerable-span {
     font-size: small;
     font-family: system-ui;
@@ -236,6 +240,13 @@ body {
     border-radius: 15px;
   }
 
+  .feedback {
+    padding: 15px;
+    border: 2px solid #000;
+    margin-top: 6px;
+    border-radius: 15px;
+  }
+
   .question, .response, .response-text, .fact-checked-text {
     max-width: 700px;
     margin-left: 3px;
@@ -269,6 +280,21 @@ body {
     cursor:pointer;
   }
 
+  #feedback-button {
+    border: 0;
+    background-color: #cf633ff2;
+    color: #fff;
+    padding: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+    margin-top: 0.3em;
+  }
+
+  #feedback-button:hover {
+    background: #ce3705f2;
+    cursor:pointer;
+  }
+
   #like-button {
     border: 0;
     color: #fff;
@@ -298,6 +324,26 @@ body {
     font-family: fantasy;
   }
 
+  #feedback-submit-button {
+    border: 0;
+    background: none;
+    background-color: #CF5C3F;
+    color: #fff;
+    padding: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+  }
+
+  #feedback-submit-button:hover {
+    background: #ce3705f2;
+    cursor:pointer;
+  }
+
+  #feedback-submit-result {
+    color: #027f02d6;
+    font-family: fantasy;
+  }
+
   #edit-text-area {
     font: 13px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
     max-height: 500px;
@@ -307,6 +353,15 @@ body {
     padding: 8px;
   }
 
+  #feedback-text-area {
+    font: 13px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
+    max-height: 500px;
+    max-width: -webkit-fill-available;
+    height: 300px;
+    width: 580px;
+    padding: 8px;
+  }
+
   #rewrite-question-header {
     margin: 0;
     margin-bottom: 5px;
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-widget.css b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-widget.css
index a0732ba9f..37e7e5947 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-widget.css
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-widget.css
@@ -194,6 +194,10 @@ body {
     margin-top: 12px;
   }
 
+  #feedback-buttons-box {
+    margin-top: 12px;
+  }
+
   #answerable-span {
     font-size: small;
     font-family: system-ui;
@@ -252,6 +256,13 @@ body {
     border-radius: 15px;
   }
 
+  .feedback {
+    padding: 15px;
+    border: 2px solid #000;
+    margin-top: 6px;
+    border-radius: 15px;
+  }
+
   .question, .response, .response-text, .fact-checked-text {
     max-width: 700px;
     margin-left: 3px;
@@ -291,6 +302,21 @@ body {
     cursor:pointer;
   }
 
+  #feedback-button {
+    border: 0;
+    background-color: #cf633ff2;
+    color: #fff;
+    padding: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+    margin-top: 0.3em;
+  }
+
+  #feedback-button:hover {
+    background: #ce3705f2;
+    cursor:pointer;
+  }
+
   #like-button {
     border: 0;
     color: #fff;
@@ -320,6 +346,26 @@ body {
     font-family: fantasy;
   }
 
+  #feedback-submit-button {
+    border: 0;
+    background: none;
+    background-color: #CF5C3F;
+    color: #fff;
+    padding: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+  }
+
+  #feedback-submit-button:hover {
+    background: #ce3705f2;
+    cursor:pointer;
+  }
+
+  #feedback-submit-result {
+    color: #027f02d6;
+    font-family: fantasy;
+  }
+
   #edit-text-area {
     font: 13px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
     max-height: 500px;
@@ -329,6 +375,15 @@ body {
     padding: 8px;
   }
 
+  #feedback-text-area {
+    font: 13px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
+    max-height: 500px;
+    max-width: -webkit-fill-available;
+    height: 300px;
+    width: 580px;
+    padding: 8px;
+  }
+
   #rewrite-question-header {
     margin: 0;
     margin-bottom: 5px;
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/javascript/app.js b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/javascript/app.js
index 32908b566..6007e7f80 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/javascript/app.js
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/javascript/app.js
@@ -69,6 +69,23 @@ if (rewriteButton != null){
   });
 }
 
+// Toggle the hidden class on the `feedback-box` div.
+let feedbackButton = document.getElementById('feedback-button');
+
+if (feedbackButton != null){
+  feedbackButton.addEventListener('click',function (){
+    let feedbackBox = document.getElementById('feedback-box');
+    if (feedbackBox.classList.contains("hidden")){
+      feedbackBox.classList.remove("hidden");
+      // Trigger a focus event on the textarea
+      let element = document.getElementById('feedback-text-area');
+      element.dispatchEvent(new Event("focus"));
+    }else{
+      feedbackBox.classList.add("hidden");
+    }
+  });
+}
+
 // Toggle the selected class on the `like this response` button.
 let likeButton = document.getElementById('like-button');
 
@@ -136,6 +153,28 @@ function resize_textarea(){
   }
 }
 
+// Adjust the size of the `feedback-text-area` textarea.
+let feedbackTextArea = document.getElementById('feedback-text-area');
+
+if (feedbackTextArea != null){
+  feedbackTextArea.addEventListener('focus', resize_feedback_textarea);
+  feedbackTextArea.addEventListener('input', resize_feedback_textarea);
+}
+
+function resize_feedback_textarea(){
+  this.style.height = "5px";
+  this.style.width = "650px";
+  this.style.height = (this.scrollHeight)+"px";
+  let feedbackSubmitButton = document.getElementById('feedback-submit-button');
+  if (feedbackSubmitButton != null){
+    if (feedbackSubmitButton.classList.contains("disable")){
+      feedbackSubmitButton.classList.remove("disable");
+      let feedbackResult = document.getElementById('feedback-submit-result');
+      feedbackResult.textContent = "Click to re-submit updated feedback.";
+    }
+  }
+}
+
 // Make a rewrite POST call.
 let rewriteSubmitButton = document.getElementById('submit-button');
 
@@ -169,6 +208,39 @@ if (rewriteSubmitButton != null){
   }, false);
 }
 
+// Make a feedback POST call.
+let feedbackSubmitButton = document.getElementById('feedback-submit-button');
+
+if (feedbackSubmitButton != null){
+  feedbackSubmitButton.addEventListener('click',function (){
+    let xhr = new XMLHttpRequest();
+    // The value of `urlRewrite` below is specified in the html template,
+    // which is set by the Flask server.
+    // See chatbot/templates/chatui/base.html
+    xhr.open("POST", urlFeedback, true);
+    xhr.setRequestHeader("Accept", "application/json");
+    xhr.setRequestHeader("Content-Type", "application/json");
+    let feedbackQuestion = document.getElementById('feedback-question-span');
+    let feedbackResponse = document.getElementById('feedback-response-span');
+    let feedbackTextArea = document.getElementById('feedback-text-area');
+    let userIDInput = document.getElementById('user-id');
+    let userID = userIDInput.value;
+    if (userID == "") {
+      userID = "anonymous";
+    }
+    let data = JSON.stringify({
+      "user_id": userID,
+      "question": feedbackQuestion.textContent,
+      "response": feedbackResponse.textContent,
+      "feedback": feedbackTextArea.value});
+    xhr.send(data);
+
+    let feedbackResult = document.getElementById('feedback-submit-result');
+    feedbackResult.textContent = "Feedback has been submitted. Thank you!";
+    feedbackSubmitButton.classList.add("disable");
+  }, false);
+}
+
 // Monitor the width of the Docs Agent widget.
 let answerableSpan = document.getElementById('answerable-span');
 //let rewriteButton = document.getElementById('rewrite-button');
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chat-widget/base.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chat-widget/base.html
index 42987dee4..22f2adb33 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chat-widget/base.html
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chat-widget/base.html
@@ -18,6 +18,7 @@
   </div>
   <script>
     let urlRewrite = "{{ server_url }}rewrite";
+    let urlFeedback = "{{ server_url }}feedback";
     let urlLike = "{{ server_url }}like";
   </script>
   <script src="{{ url_for('static', filename='javascript/app.js') }}"></script>
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chat-widget/result.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chat-widget/result.html
index e3c65c759..aa249e3eb 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chat-widget/result.html
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chat-widget/result.html
@@ -132,7 +132,8 @@ <h2 class="handle">
   </section>
 {% endif %}
 
-<!--Common like/rewrite-->
+<!--Feedback buttons at the bottom of the UI display.-->
+{% if feedback_mode == "rewrite" %}
 <div id="rewrite-buttons-box">
   <input type="button" value="Rewrite this response" id="rewrite-button">
   <input type="button" class="notselected" value="Like this response &#128077;" id="like-button">
@@ -166,3 +167,36 @@ <h4>Rewrite:</h4>
 <div class="hidden" id="uuid-box">
   {{ uuid | safe }}
 </div>
+{% else %}
+<div id="feedback-buttons-box">
+  <input type="button" value="Provide feedback" id="feedback-button">
+  <input type="button" class="notselected" value="Like this response &#128077;" id="like-button">
+  {% if aqa_response_in_html != "" %}
+  <span id="answerable-span">
+    (<b>Answerable probability</b>: {{"%.3f"|format(probability|float)}})
+  </span>
+  {% endif %}
+</div>
+<div class="feedback hidden" id="feedback-box">
+  <h4>Feedback:</h4>
+  <textarea id="feedback-text-area"></textarea>
+  <br>
+  <label for="user-id">LDAP:</label>
+  <input type="text" id="user-id" name="user-id" placeholder="Optional">
+  <br>
+  <span id="feedback-submit-box">
+    <input type="button" value="Submit" id="feedback-submit-button">
+  </span>
+  <span id="feedback-submit-result">
+  </span>
+</div>
+<div class="hidden" id="uuid-box">
+  {{ uuid | safe }}
+</div>
+<div class="hidden" id="feedback-question-span">
+  {{ question | replace("+", " ") | replace("%3F", "?")}}
+</div>
+<div class="hidden" id="feedback-response-span">
+  {{ md_to_html(response) | safe }}
+</div>
+{% endif %}
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-1.5/base.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-1.5/base.html
index 51ae74ed9..4ddd096b2 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-1.5/base.html
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-1.5/base.html
@@ -20,6 +20,7 @@
   <br>
   <script>
     let urlRewrite = "{{ server_url }}rewrite";
+    let urlFeedback = "{{ server_url }}feedback";
     let urlLike = "{{ server_url }}like";
   </script>
   <script src="{{ url_for('static', filename='javascript/app.js') }}"></script>
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-1.5/result.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-1.5/result.html
index c2108e930..3b17fafe9 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-1.5/result.html
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-1.5/result.html
@@ -125,7 +125,8 @@ <h2 class="handle">
   </section>
 {% endif %}
 
-<!--Common like/rewrite-->
+<!--Feedback buttons at the bottom of the UI display.-->
+{% if feedback_mode == "rewrite" %}
 <div id="rewrite-buttons-box">
   <input type="button" value="Rewrite this response" id="rewrite-button">
   <input type="button" class="notselected" value="Like this response &#128077;" id="like-button">
@@ -159,3 +160,36 @@ <h4>Rewrite:</h4>
 <div class="hidden" id="uuid-box">
   {{ uuid | safe }}
 </div>
+{% else %}
+<div id="feedback-buttons-box">
+  <input type="button" value="Provide feedback" id="feedback-button">
+  <input type="button" class="notselected" value="Like this response &#128077;" id="like-button">
+  {% if aqa_response_in_html != "" %}
+  <span id="answerable-span">
+    (<b>Answerable probability</b>: {{"%.3f"|format(probability|float)}})
+  </span>
+  {% endif %}
+</div>
+<div class="feedback hidden" id="feedback-box">
+  <h4>Feedback:</h4>
+  <textarea id="feedback-text-area"></textarea>
+  <br>
+  <label for="user-id">LDAP:</label>
+  <input type="text" id="user-id" name="user-id" placeholder="Optional">
+  <br>
+  <span id="feedback-submit-box">
+    <input type="button" value="Submit" id="feedback-submit-button">
+  </span>
+  <span id="feedback-submit-result">
+  </span>
+</div>
+<div class="hidden" id="uuid-box">
+  {{ uuid | safe }}
+</div>
+<div class="hidden" id="feedback-question-span">
+  {{ question | replace("+", " ") | replace("%3F", "?")}}
+</div>
+<div class="hidden" id="feedback-response-span">
+  {{ md_to_html(response) | safe }}
+</div>
+{% endif %}
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui/base.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui/base.html
index 944984182..87cece3e4 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui/base.html
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui/base.html
@@ -20,6 +20,7 @@
   <br>
   <script>
     let urlRewrite = "{{ server_url }}rewrite";
+    let urlFeedback = "{{ server_url }}feedback";
     let urlLike = "{{ server_url }}like";
   </script>
   <script src="{{ url_for('static', filename='javascript/app.js') }}"></script>
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui/result.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui/result.html
index ea47264e2..e6df8b9b7 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui/result.html
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui/result.html
@@ -102,7 +102,8 @@ <h2 class="handle">
   </section>
 {% endif %}
 
-<!--Common like/rewrite-->
+<!--Feedback buttons at the bottom of the UI display.-->
+{% if feedback_mode == "rewrite" %}
 <div id="rewrite-buttons-box">
   <input type="button" value="Rewrite this response" id="rewrite-button">
   <input type="button" class="notselected" value="Like this response &#128077;" id="like-button">
@@ -136,3 +137,36 @@ <h4>Rewrite:</h4>
 <div class="hidden" id="uuid-box">
   {{ uuid | safe }}
 </div>
+{% else %}
+<div id="feedback-buttons-box">
+  <input type="button" value="Provide feedback" id="feedback-button">
+  <input type="button" class="notselected" value="Like this response &#128077;" id="like-button">
+  {% if aqa_response_in_html != "" %}
+  <span id="answerable-span">
+    (<b>Answerable probability</b>: {{"%.3f"|format(probability|float)}})
+  </span>
+  {% endif %}
+</div>
+<div class="feedback hidden" id="feedback-box">
+  <h4>Feedback:</h4>
+  <textarea id="feedback-text-area"></textarea>
+  <br>
+  <label for="user-id">LDAP:</label>
+  <input type="text" id="user-id" name="user-id" placeholder="Optional">
+  <br>
+  <span id="feedback-submit-box">
+    <input type="button" value="Submit" id="feedback-submit-button">
+  </span>
+  <span id="feedback-submit-result">
+  </span>
+</div>
+<div class="hidden" id="uuid-box">
+  {{ uuid | safe }}
+</div>
+<div class="hidden" id="feedback-question-span">
+  {{ question | replace("+", " ") | replace("%3F", "?")}}
+</div>
+<div class="hidden" id="feedback-response-span">
+  {{ md_to_html(response) | safe }}
+</div>
+{% endif %}
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/cli.py b/examples/gemini/python/docs-agent/docs_agent/interfaces/cli.py
index fcb1ee0db..2d76fe3a4 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/cli.py
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/cli.py
@@ -96,27 +96,40 @@ def cli_client(ctx, config_file, product):
 
 @cli_admin.command()
 @common_options
-def chunk(config_file: typing.Optional [str], product: list[str] = [""]):
+def chunk(config_file: typing.Optional[str], product: list[str] = [""]):
     """Convert files to plain text chunks."""
     loaded_config, product_config = return_config_and_product(
         config_file=config_file, product=product
     )
     chunker.process_all_products(config_file=product_config)
-    click.echo("The files are successfully converted into text chunks.")
+    click.echo("\nFiles are successfully converted into text chunks.")
 
 
 @cli_admin.command()
+@click.option(
+    "--enable_delete_chunks",
+    is_flag=True,
+    help="Delete stale chunks in the existing databases.",
+)
 @common_options
-def populate(config_file: typing.Optional [str], product: list[str] = [""]):
+def populate(
+    config_file: typing.Optional[str],
+    enable_delete_chunks: bool = False,
+    product: list[str] = [""],
+):
     """Populate a vector database using text chunks."""
     # Loads configurations from common options
     loaded_config, product_config = return_config_and_product(
         config_file=config_file, product=product
     )
+    # If `--enable_delete_chunks` flag is set, update the config object.
+    if enable_delete_chunks:
+        for product in product_config.products:
+            product.enable_delete_chunks = "True"
 
     populate_script.process_all_products(config_file=product_config)
     for item in product_config.products:
-        click.echo(f"The text chunks are successfully added to {item.db_type}.")
+        click.echo(f"\nText chunks are successfully added to {item.db_type}.")
 
 
 @cli_admin.command()
@@ -127,16 +140,28 @@ def populate(config_file: typing.Optional [str], product: list[str] = [""]):
     "--app_mode",
     default=None,
     show_default=True,
-    type=click.Choice(["web", "widget", "experimental"], case_sensitive=False),
+    type=click.Choice(["web", "widget", "1.5", "experimental"], case_sensitive=False),
     help="Specify a mode for the chatbot.",
 )
+@click.option(
+    "--enable_show_logs",
+    is_flag=True,
+    help="Enable a page view that shows log files.",
+)
+@click.option(
+    "--enable_logs_to_markdown",
+    is_flag=True,
+    help="Save each question-and-response pair to a Markdown file.",
+)
 @common_options
 def chatbot(
     hostname: str,
     port: str,
     debug: str,
     app_mode: str,
-    config_file: typing.Optional [str],
+    enable_show_logs: str,
+    enable_logs_to_markdown: str,
+    config_file: typing.Optional[str],
     product: list[str] = [""],
 ):
     """Launch the Flask-based chatbot app."""
@@ -144,20 +169,34 @@ def chatbot(
     loaded_config, product_config = return_config_and_product(
         config_file=config_file, product=product
     )
-
-    product_file = product_config.products[0]
-    if app_mode == None:
-        app_mode = product_file.app_mode
-    app = chatbot_flask.create_app(product=product_file, app_mode=app_mode)
+    # Launch the web app using the first product in the `config.yaml` file.
+    product = product_config.products[0]
+    # Check the `--app_mode` flag
+    if app_mode == None and hasattr(product, "app_mode"):
+        app_mode = product.app_mode
+    elif app_mode != None:
+        # Update the app_mode field in the product config object.
+        product.app_mode = app_mode
+    # Check the `--app_port` flag
+    app_port = int(port)
+    if app_port == 5000 and hasattr(product, "app_port"):
+        app_port = int(product.app_port)
+    # If `--enable_show_logs` flag is set, update the product config object.
+    if enable_show_logs:
+        product.enable_show_logs = "True"
+    # If `--enable_logs_to_markdown` flag is set, update the product config object.
+    if enable_logs_to_markdown:
+        product.enable_logs_to_markdown = "True"
+    app = chatbot_flask.create_app(product=product, app_mode=app_mode)
     click.echo(
-        f"Launching the chatbot UI for product {product_file.product_name} in {app_mode} mode."
+        f"Launching the chatbot UI for product {product.product_name} in {app_mode} mode."
     )
-    app.run(host=hostname, port=port, debug=debug)
+    app.run(host=hostname, port=app_port, debug=debug)
 
 
 @cli_admin.command()
 @common_options
-def show_config(config_file: typing.Optional [str], product: list[str] = [""]):
+def show_config(config_file: typing.Optional[str], product: list[str] = [""]):
     """Print the Docs Agent configuration."""
     # Loads configurations from common options
     loaded_config, product_config = return_config_and_product(
@@ -171,7 +210,7 @@ def show_config(config_file: typing.Optional [str], product: list[str] = [""]):
 @cli_client.command(name="tellme")
 @click.argument("words", nargs=-1)
 @common_options
-def tellme(words, config_file: typing.Optional [str], product: list[str] = [""]):
+def tellme(words, config_file: typing.Optional[str], product: list[str] = [""]):
     """Answer a question related to the product."""
     # Loads configurations from common options
     loaded_config, product_configs = return_config_and_product(
@@ -203,8 +242,8 @@ def tellme(words, config_file: typing.Optional [str], product: list[str] = [""])
 @common_options
 def helpme(
     words,
-    config_file: typing.Optional [str],
-    file: typing.Optional [str] = None,
+    config_file: typing.Optional[str],
+    file: typing.Optional[str] = None,
     rag: bool = False,
     product: list[str] = [""],
 ):
@@ -222,17 +261,19 @@ def helpme(
     question = ""
     for word in words:
         question += word + " "
-    if file:
+    question = question.strip()
+    if file and file != "None":
         # This feature is only available in gemini 1.5 pro (large context)
         if product_config.products[0].models.language_model.startswith(
             "models/gemini-1.5-pro"
         ):
             try:
-                with open(file, "r", encoding="utf-8") as auto:
+                this_file = os.path.realpath(os.path.join(os.getcwd(), file))
+                with open(this_file, "r", encoding="utf-8") as auto:
                     # Read the input content
                     content = auto.read()
                     auto.close()
-                final_file = f"The content of the {file} file:\n" + content
+                final_file = f"THE CONTENT OF THE FILE {file} BELOW:\n\n" + content
                 console.ask_model_with_file(
                     question.strip(), product_config, file=final_file, rag=rag
                 )
@@ -272,7 +313,7 @@ def helpme(
 
 @cli_admin.command()
 @common_options
-def benchmark(config_file: typing.Optional [str], product: list[str] = [""]):
+def benchmark(config_file: typing.Optional[str], product: list[str] = [""]):
     """Run the Docs Agent benchmark test."""
     # Loads configurations from common options
     loaded_config, product_config = return_config_and_product(
@@ -283,7 +324,7 @@ def benchmark(config_file: typing.Optional [str], product: list[str] = [""]):
 
 @cli_admin.command()
 @common_options
-def list_corpora(config_file: typing.Optional [str], product: list[str] = [""]):
+def list_corpora(config_file: typing.Optional[str], product: list[str] = [""]):
     """List all existing online corpora."""
     # Loads configurations from common options
     loaded_config, product_config = return_config_and_product(
@@ -301,7 +342,9 @@ def list_corpora(config_file: typing.Optional [str], product: list[str] = [""]):
 @cli_admin.command()
 @click.option("--name", default=None)
 @common_options
-def delete_corpus(name: str, config_file: typing.Optional [str], product: list[str] = [""]):
+def delete_corpus(
+    name: str, config_file: typing.Optional[str], product: list[str] = [""]
+):
     """Delete an online corpus."""
     # Loads configurations from common options
     loaded_config, product_config = return_config_and_product(
@@ -323,7 +366,9 @@ def delete_corpus(name: str, config_file: typing.Optional [str], product: list[s
 @cli_admin.command()
 @click.option("--name", default=None)
 @common_options
-def open_corpus(name: str, config_file: typing.Optional [str], product: list[str] = [""]):
+def open_corpus(
+    name: str, config_file: typing.Optional[str], product: list[str] = [""]
+):
     """Share an online corpus with everyone."""
     # Loads configurations from common options
     loaded_config, product_config = return_config_and_product(
@@ -346,7 +391,11 @@ def open_corpus(name: str, config_file: typing.Optional [str], product: list[str
 @click.option("--role", default="READER")
 @common_options
 def share_corpus(
-    name: str, email: str, role: str, config_file: typing.Optional [str], product: list[str] = [""]
+    name: str,
+    email: str,
+    role: str,
+    config_file: typing.Optional[str],
+    product: list[str] = [""],
 ):
     """Share an online corpus with a user."""
     # Loads configurations from common options
@@ -370,7 +419,7 @@ def share_corpus(
 @click.option("--name", default=None)
 @common_options
 def remove_corpus_permission(
-    name: str, config_file: typing.Optional [str], product: list[str] = [""]
+    name: str, config_file: typing.Optional[str], product: list[str] = [""]
 ):
     """Remove a user permission from an online corpus."""
     # Loads configurations from common options
@@ -391,7 +440,9 @@ def remove_corpus_permission(
 @cli_admin.command()
 @click.option("--name", default=None)
 @common_options
-def get_all_docs(name: str, config_file: typing.Optional [str], product: list[str] = [""]):
+def get_all_docs(
+    name: str, config_file: typing.Optional[str], product: list[str] = [""]
+):
     """Get the list of all docs in an online corpus."""
     # Loads configurations from common options
     loaded_config, product_config = return_config_and_product(
@@ -405,14 +456,55 @@ def get_all_docs(name: str, config_file: typing.Optional [str], product: list[st
         semantic.get_all_docs(corpus_name=name, print_output=True)
 
 
+@cli_admin.command()
+@common_options
+def cleanup_dev(
+    config_file: typing.Optional[str],
+    product: list[str] = [""],
+):
+    """Delete all databases in this Docs Agent development environment."""
+    print("Cleaning up the Docs Agent development environment.")
+    print("Found the following database configuration:")
+    # Loads configurations from common options
+    loaded_config, product_config = return_config_and_product(
+        config_file=config_file, product=product
+    )
+    chroma_dir = ""
+    corpus_name = ""
+    product = product_config.products[0]
+    for db in product.db_configs:
+        if hasattr(db, "vector_db_dir") and db.vector_db_dir != None:
+            print(f"Chroma database: {db.vector_db_dir}")
+            chroma_dir = db.vector_db_dir
+        if hasattr(db, "corpus_name") and db.corpus_name != None:
+            print(f"Corpus name: {db.corpus_name}")
+            corpus_name = db.corpus_name
+    if chroma_dir != "":
+        command = "rm -fr " + chroma_dir
+        if click.confirm(
+            f"\nDeleting the Chroma database {chroma_dir} ({command}).\nDo you want to continue?",
+            abort=True,
+        ):
+            os.system(command)
+            print("Done.")
+    if corpus_name != "":
+        if click.confirm(
+            f"\nDeleting the corpus named {corpus_name}.\nDo you want to continue?",
+            abort=True,
+        ):
+            semantic = SemanticRetriever()
+            semantic.delete_a_corpus(corpus_name=corpus_name)
+            print("Done.")
+
+
 @cli_admin.command()
 @click.option("--input_chroma", default=None)
 @click.option("--output_dir", default=None)
 @common_options
 def backup_chroma(
     input_chroma: str,
-    output_dir: typing.Optional [str],
-    config_file: typing.Optional [str],
+    output_dir: typing.Optional[str],
+    config_file: typing.Optional[str],
     product: list[str] = [""],
 ):
     """Backup a chroma database to an output directory."""
diff --git a/examples/gemini/python/docs-agent/docs_agent/memory/logging.py b/examples/gemini/python/docs-agent/docs_agent/memory/logging.py
index a6b7ef9f5..beb1280bc 100644
--- a/examples/gemini/python/docs-agent/docs_agent/memory/logging.py
+++ b/examples/gemini/python/docs-agent/docs_agent/memory/logging.py
@@ -16,12 +16,48 @@
 
 from datetime import datetime
 import pytz
+import os
 
 """Module to log interactions with the chatbot"""
 
 
-# Log the question and response to the server's log file.
-def log_question(uid, user_question: str, response: str, probability: str = "None", save: str = "txt"):
+# Save this question and response pair as a file.
+def log_question_to_file(user_question: str, response: str, probability: str = "None"):
+    filename = str(user_question).lower().replace(" ", "-").replace("?", "")
+    log_dir = "./logs/responses"
+    log_filename = f"{log_dir}/{filename}.md"
+    if not os.path.exists(log_dir):
+        os.makedirs(log_dir)
+    with open(log_filename, "a", encoding="utf-8") as log_file:
+        log_file.write("# " + user_question.strip() + "\n\n")
+        log_file.write(response.strip() + "\n\n")
+        if probability != "None":
+            log_file.write("(Answerable probability: " + str(probability) + ")\n")
+        log_file.close()
+
+
+# Log the answerable_probability score and question.
+def log_answerable_probability(user_question: str, probability: str):
+    log_dir = "./logs"
+    answerable_list_filename = log_dir + "/answerable_logs.txt"
+    if not os.path.exists(log_dir):
+        os.makedirs(log_dir)
+    with open(answerable_list_filename, "a", encoding="utf-8") as log_file:
+        log_file.write(
+            str("{:.16f}".format(probability)) + "    " + user_question.strip() + "\n"
+        )
+        log_file.close()
+
+
+# Print and log the question and response.
+def log_question(
+    uid,
+    user_question: str,
+    response: str,
+    probability: str = "None",
+    save: str = "True",
+    logs_to_markdown: str = "False",
+):
     date_format = "%m/%d/%Y %H:%M:%S %Z"
     date = datetime.now(tz=pytz.utc)
     date = date.astimezone(pytz.timezone("US/Pacific"))
@@ -32,8 +68,12 @@ def log_question(uid, user_question: str, response: str, probability: str = "Non
     # For the AQA model, also print the response's answerable_probability
     if probability != "None":
         print("Answerable probability: " + str(probability) + "\n")
-    if save == "txt":
-        with open("chatui_logs.txt", "a", encoding="utf-8") as log_file:
+    if save == "True":
+        log_dir = "./logs"
+        log_filename = log_dir + "/chatui_logs.txt"
+        if not os.path.exists(log_dir):
+            os.makedirs(log_dir)
+        with open(log_filename, "a", encoding="utf-8") as log_file:
             log_file.write(
                 "[" + date.strftime(date_format) + "][UID " + str(uid) + "]\n"
             )
@@ -42,25 +82,23 @@ def log_question(uid, user_question: str, response: str, probability: str = "Non
             if probability != "None":
                 log_file.write("Answerable probability: " + str(probability) + "\n\n")
             log_file.close()
-        # Added to track the answerable_probability scores.
         if probability != "None":
-            with open("answerable_logs.txt", "a", encoding="utf-8") as log2_file:
-                log2_file.write(
-                    str("{:.16f}".format(probability))
-                    + "    "
-                    + user_question.strip()
-                    + "\n"
-                )
-                log2_file.close()
+            # Track the answerable_probability scores.
+            log_answerable_probability(user_question, probability)
+        if logs_to_markdown == "True":
+            log_question_to_file(user_question, response, probability)
+
 
-def log_like(is_like, uid, save: str = "txt"):
+def log_like(is_like, uid, save: str = "True"):
     date_format = "%m/%d/%Y %H:%M:%S %Z"
     date = datetime.now(tz=pytz.utc)
     date = date.astimezone(pytz.timezone("US/Pacific"))
     print("UID: " + str(uid))
     print("Like: " + str(is_like))
-    if save == "txt":
-        with open("chatui_logs.txt", "a", encoding="utf-8") as log_file:
+    if save == "True":
+        log_dir = "./logs"
+        log_filename = log_dir + "/chatui_logs.txt"
+        with open(log_filename, "a", encoding="utf-8") as log_file:
             log_file.write(
                 "[" + date.strftime(date_format) + "][UID " + str(uid) + "]\n"
             )
diff --git a/examples/gemini/python/docs-agent/docs_agent/models/google_genai.py b/examples/gemini/python/docs-agent/docs_agent/models/google_genai.py
index f8ff4b634..da9e82058 100644
--- a/examples/gemini/python/docs-agent/docs_agent/models/google_genai.py
+++ b/examples/gemini/python/docs-agent/docs_agent/models/google_genai.py
@@ -129,7 +129,11 @@ def generate_content(self, contents):
         if self.language_model is None:
             raise GoogleUnsupportedModelError(self.language_model, self.api_endpoint)
         model = google.generativeai.GenerativeModel(model_name=self.language_model)
-        return model.generate_content(contents)
+        response = model.generate_content(contents)
+        for chunk in response:
+            if str(chunk.candidates[0].content) == "":
+                return self.model_error_message
+        return response.text
 
     # Use this method for talking to a Gemini content model
     # Optionally provide a prompt, if not use the one from config.yaml
diff --git a/examples/gemini/python/docs-agent/docs_agent/preprocess/files_to_plain_text.py b/examples/gemini/python/docs-agent/docs_agent/preprocess/files_to_plain_text.py
index 1b06c6006..141156053 100644
--- a/examples/gemini/python/docs-agent/docs_agent/preprocess/files_to_plain_text.py
+++ b/examples/gemini/python/docs-agent/docs_agent/preprocess/files_to_plain_text.py
@@ -40,6 +40,7 @@
     fidl_splitter,
 )
 
+
 # Construct a URL from a URL prefix and a relative path.
 def construct_a_url(url_prefix: str, relative_path: str):
     temp_url = end_path_backslash(add_scheme_url(url=url_prefix, scheme="https"))
@@ -48,6 +49,7 @@ def construct_a_url(url_prefix: str, relative_path: str):
     built_url = strip_ext_url[1]
     return built_url
 
+
 # This function pre-processes files before they are actually chunked.
 # This allows it to resolve includes of includes, Jinja templates, etc...
 # TODO support Jinja, for this need to support data filters as well
@@ -179,6 +181,8 @@ def process_markdown_file(
                 index=chunk_number,
                 extension="md",
             )
+            # Get the text chunk filename string (after `docs-agent/data`).
+            text_chunk_filename = get_relative_path_and_filename(filename_to_save)
             # Generate UUID for each plain text chunk and collect its metadata,
             # which will be written to the top-level `file_index.json` file.
             md_hash = uuid.uuid3(namespace_uuid, section.content)
@@ -213,6 +217,7 @@ def process_markdown_file(
                 "previous_id": int(section.previous_id),
                 "URL": str(built_url),
                 "md_hash": str(md_hash),
+                "text_chunk_filename": str(text_chunk_filename),
                 "token_estimate": float(section.token_count),
                 "full_token_estimate": float(page_token_estimate),
                 "parent_tree": list(section.parent_tree),
@@ -242,6 +247,8 @@ def process_markdown_file(
                 index=chunk_number,
                 extension="md",
             )
+            # Get the text chunk filename string (after `docs-agent/data`).
+            text_chunk_filename = get_relative_path_and_filename(filename_to_save)
             # Generate UUID for each plain text chunk and collect its metadata,
             # which will be written to the top-level `file_index.json` file.
             md_hash = uuid.uuid3(namespace_uuid, file_with_include)
@@ -252,7 +259,7 @@ def process_markdown_file(
             built_url = construct_a_url(url_prefix, relative_path)
             # Get the page title
             page_title = "None"
-            if "title" in  metadata:
+            if "title" in metadata:
                 page_title = metadata["title"]
             # Construct metadata.
             file_metadata[filename_to_save] = {
@@ -268,6 +275,7 @@ def process_markdown_file(
                 "previous_id": int(1),
                 "URL": str(built_url),
                 "md_hash": str(md_hash),
+                "text_chunk_filename": str(text_chunk_filename),
                 "token_estimate": float(1.0),
                 "full_token_estimate": float(page_token_estimate),
                 "metadata": dict(metadata),
@@ -390,6 +398,7 @@ def process_files_from_input(
     inputpathitem: Input,
     splitter: str,
     inputpath: typing.Optional[str] = None,
+    input_path_count: int = 0,
 ):
     # If inputpath isn't specified assign path from item
     if inputpath is None:
@@ -401,7 +410,8 @@ def process_files_from_input(
     file_index = []
     full_file_metadata = {}
     resolved_output_path = resolve_path(product_config.output_path)
-    # Pre-calculates file count
+    chunk_group_name = "text_chunks_" + '{:03d}'.format(input_path_count)
+    # Get the total file count.
     file_count = sum(len(files) for _, _, files in os.walk(resolve_path(inputpath)))
     # Set up a status bar for the terminal display.
     progress_bar = tqdm.tqdm(
@@ -425,7 +435,7 @@ def process_files_from_input(
             # Get the full path to this input file.
             filename_to_open = os.path.join(root, file)
             # Construct a new sub-directory for storing output plain text files.
-            new_path = resolved_output_path + re.sub(
+            new_path = resolved_output_path + "/" + chunk_group_name + re.sub(
                 resolve_path(inputpath), "", os.path.join(root, "")
             )
             is_exist = os.path.exists(new_path)
@@ -497,6 +507,7 @@ def process_files_from_input(
                 full_file_metadata.update(this_file_metadata)
 
     # The processing of input files is finished.
+    progress_bar.set_description_str(f"Finished processing files.", refresh=False)
     # Count all processed files.
     file_count = md_count + html_count + fidl_count
     # Print the summary of the processed files.
@@ -506,6 +517,7 @@ def process_files_from_input(
     print(str(html_count) + " HTML files.")
     if fidl_count > 0:
         print(str(fidl_count) + " FIDL files.")
+    print()
     return file_count, md_count, html_count, file_index, full_file_metadata
 
 
@@ -514,13 +526,13 @@ def save_file_index_json(output_path, output_content):
     json_out_file = resolve_path(output_path) + "/file_index.json"
     with open(json_out_file, "w", encoding="utf-8") as outfile:
         json.dump(output_content, outfile)
-    print(
-        "Created " + json_out_file + " to store the complete list of processed files."
-    )
+    # print("Created " + json_out_file + " to store the complete list of processed files.")
 
 
 # Given a file, root, and inputpath, make a relative path
-def make_relative_path(file: str, inputpath: str, root: typing.Optional[str] = None) -> str:
+def make_relative_path(
+    file: str, inputpath: str, root: typing.Optional[str] = None
+) -> str:
     file_slash = "/" + file
     if root is None:
         relative_path = os.path.relpath(file_slash, inputpath)
@@ -542,15 +554,26 @@ def make_file_chunk_name(
 # Given a path, file, chunk index, and an optional path extension (to save chunk)
 # Create a chunk name
 def make_chunk_name(new_path: str, file: str, index: int, extension: str = "md") -> str:
-    # Grab the filename without the .md extension
     new_filename = os.path.join(new_path, file)
+    filename_to_save = new_filename
+    # Grab the filename without the .md extension
     match = re.search(r"(.*)\.md$", new_filename)
-    new_filename_no_ext = match[1]
-    # Save clean plain text to a new filename appended with an index
-    filename_to_save = new_filename_no_ext + "_" + str(index) + "." + extension
+    if match:
+        new_filename_no_ext = match[1]
+        # Save the filename appended with an index.
+        filename_to_save = new_filename_no_ext + "_" + str(index) + "." + extension
     return filename_to_save
 
 
+# Return the relative path after the `docs-agent/data` path
+def get_relative_path_and_filename(full_path: str):
+    path_and_filename = full_path
+    match = re.search(r".*\/docs-agent\/data\/(.*)$", full_path)
+    if match:
+        path_and_filename = match[1]
+    return path_and_filename
+
+
 # Given a path, it resolves the path to an absolute path, and if it exists,
 # deletes it, before re-creating it (essentially making a fresh directory)
 # It then returns the absolute path name
@@ -570,8 +593,9 @@ def process_inputs_from_product(input_product: ProductConfig, temp_process_path:
     total_md_count = 0
     total_html_count = 0
     final_file_metadata = {}
+    input_path_count = 0
     for input_path_item in input_product.inputs:
-        print(f"Input path: {input_path_item.path}")
+        print(f"\nInput path {input_path_count}: {input_path_item.path}")
         temp_output = pre_process_doc_files(
             product_config=input_product,
             inputpathitem=input_path_item,
@@ -590,6 +614,7 @@ def process_inputs_from_product(input_product: ProductConfig, temp_process_path:
             inputpathitem=input_path_item,
             inputpath=temp_output,
             splitter=input_product.markdown_splitter,
+            input_path_count=input_path_count,
         )
         # Clear the temp_output
         shutil.rmtree(temp_output)
@@ -608,26 +633,93 @@ def process_inputs_from_product(input_product: ProductConfig, temp_process_path:
         total_file_count += file_count
         total_md_count += md_count
         total_html_count += html_count
+        input_path_count += 1
     source_file_index[input_product.product_name] = final_file_metadata
     # Write the recorded input variables into `file_index.json`.
     save_file_index_json(
         output_path=input_product.output_path, output_content=source_file_index
     )
     print(
-        "==========================================="
-        + f"\nFor product {input_product.product_name}:\n"
-        + "Processed a total of "
-        + str(total_file_count)
-        + " files from "
+        "\n[Summary]"
+        + f"\nProduct: {input_product.product_name}"
+        + "\nSources: "
         + str(len(input_product.inputs))
-        + " sources.\n"
+        + "\nTotal number of processed source files: "
+        + str(total_file_count)
+        + "\nMarkdown files: "
         + str(total_md_count)
-        + " Markdown files.\n"
+        + "\nHTML files: "
         + str(total_html_count)
-        + " HTML files.\n"
     )
 
 
+# Print the size distribution map of created text chunks.
+def get_chunk_size_distribution_from_product(input_product: ProductConfig):
+    chunk_size_map = {
+        "50": 0,
+        "500": 0,
+        "1000": 0,
+        "1500": 0,
+        "2000": 0,
+        "2500": 0,
+        "3000": 0,
+        "4000": 0,
+        "5000": 0,
+        "6000": 0,
+    }
+    total_file_count = 0
+    chunk_dir = input_product.output_path
+    for root, dirs, files in os.walk(resolve_path(chunk_dir)):
+        for file in files:
+            this_filename = os.path.join(root, file)
+            if this_filename.endswith(".md"):
+                file_stats = os.stat(this_filename)
+                chunk_size = int(file_stats.st_size)
+                if chunk_size <= 50:
+                    count = chunk_size_map["50"]
+                    chunk_size_map["50"] = count + 1
+                elif chunk_size > 50 and chunk_size <= 500:
+                    count = chunk_size_map["500"]
+                    chunk_size_map["500"] = count + 1
+                elif chunk_size > 500 and chunk_size <= 1000:
+                    count = chunk_size_map["1000"]
+                    chunk_size_map["1000"] = count + 1
+                elif chunk_size > 1000 and chunk_size <= 1500:
+                    count = chunk_size_map["1500"]
+                    chunk_size_map["1500"] = count + 1
+                elif chunk_size > 1500 and chunk_size <= 2000:
+                    count = chunk_size_map["2000"]
+                    chunk_size_map["2000"] = count + 1
+                elif chunk_size > 2000 and chunk_size <= 2500:
+                    count = chunk_size_map["2500"]
+                    chunk_size_map["2500"] = count + 1
+                elif chunk_size > 2000 and chunk_size <= 3000:
+                    count = chunk_size_map["3000"]
+                    chunk_size_map["3000"] = count + 1
+                elif chunk_size > 3000 and chunk_size <= 4000:
+                    count = chunk_size_map["4000"]
+                    chunk_size_map["4000"] = count + 1
+                elif chunk_size > 4000 and chunk_size <= 5000:
+                    count = chunk_size_map["5000"]
+                    chunk_size_map["5000"] = count + 1
+                else:
+                    count = chunk_size_map["6000"]
+                    chunk_size_map["6000"] = count + 1
+
+    # Print the distribution result.
+    print("\nDistribution of text chunk sizes and counts:")
+    prev_size = 0
+    for key in list(chunk_size_map):
+        count = chunk_size_map[key]
+        if int(key) == 50:
+            print(f"Chunks smaller than {key} bytes: {count}")
+        elif int(key) == 6000:
+            print(f"Chunks larger than {key} bytes: {count}")
+        else:
+            print(f"Chunks between {prev_size} and {key} bytes: {count}")
+        prev_size = int(key)
+
+
 # Given a ReadConfig object, process all products
 # Default Read config defaults to source of project with config.yaml
 # temp_process_path is where temporary files will be processed and then deleted
@@ -652,6 +744,9 @@ def process_all_products(
             input_product=product, temp_process_path=temp_process_path
         )
 
+        # Print the distribution map of text chunk sizes.
+        get_chunk_size_distribution_from_product(input_product=product)
+
 
 def main():
     #### Main ####
diff --git a/examples/gemini/python/docs-agent/docs_agent/preprocess/populate_vector_database.py b/examples/gemini/python/docs-agent/docs_agent/preprocess/populate_vector_database.py
index c8829138b..65a80fd97 100644
--- a/examples/gemini/python/docs-agent/docs_agent/preprocess/populate_vector_database.py
+++ b/examples/gemini/python/docs-agent/docs_agent/preprocess/populate_vector_database.py
@@ -52,8 +52,18 @@ def get_file_count_in_a_dir(path):
     return file_count
 
 
+# Return the relative path after the `docs-agent/data` path
+def get_relative_path_and_filename(full_path: str):
+    path_and_filename = full_path
+    match = re.search(r".*\/docs-agent\/data\/(.*)$", full_path)
+    if match:
+        path_and_filename = match[1]
+    return path_and_filename
+
+
 # Prepare progres bars for showing files being processed and uploaded.
 def init_progress_bars(file_count):
+    print()
     main = tqdm.tqdm(
         total=file_count,
         position=0,
@@ -129,6 +139,172 @@ def upload_an_entry_to_a_corpus(
     return document_name
 
 
+# Delete entries in the Chroma database if we cannot find matches in the current dataset.
+def delete_unmatched_entries_in_chroma(
+    product_config: ProductConfig, chroma_client, collection
+):
+    print()
+    print(f"Scanning the Chroma database to identify entries to be deleted.")
+    # Arrays to store IDs, text chunk filename, and md hashes of
+    # the existing entries in the local Chroma vector database.
+    existing_online_entry_ids = []
+    existing_online_entry_text_chunk_filenames = []
+    existing_online_entry_md_hashes = []
+    # Get all entries in the vector database.
+    all_entries = collection.get()
+    for entry in all_entries["ids"]:
+        # logging.error(f"ID: {entry}")
+        existing_online_entry_ids.append(str(entry))
+    for entry in all_entries["metadatas"]:
+        # logging.error(f"Metadata: {entry}")
+        text_chunk_filename = ""
+        md_hash = ""
+        if "text_chunk_filename" in entry:
+            text_chunk_filename = entry["text_chunk_filename"]
+        if "md_hash" in entry:
+            md_hash = entry["md_hash"]
+        # logging.error(f"Text chunk filename: {text_chunk_filename}")
+        # logging.error(f"MD HASH: {md_hash}")
+        existing_online_entry_text_chunk_filenames.append(str(text_chunk_filename))
+        existing_online_entry_md_hashes.append(str(md_hash))
+
+    # Examine the new candidate entries in the current `data` directory.
+    candidate_entries = {}
+    (index_object, full_index_path) = load_index(input_path=product_config.output_path)
+    for product in index_object:
+        dictionary_input = index_object[product]
+    # Extract the text chunk name and hash from each chunk data.
+    for item in dictionary_input:
+        chunk_data = dictionary_input[item]
+        text_chunk_filename = ""
+        text_chunk_md_hash = ""
+        # print(f"Candidate text chunk data: {chunk_data}")
+        if "text_chunk_filename" in chunk_data:
+            text_chunk_filename = chunk_data["text_chunk_filename"]
+        if "md_hash" in chunk_data:
+            text_chunk_md_hash = chunk_data["md_hash"]
+        # print(f"Candidate text chunk filename: {text_chunk_filename}")
+        if text_chunk_filename != "":
+            candidate_entries[text_chunk_filename] = text_chunk_md_hash
+
+    # Compare the existing online entries to the candidate entries.
+    to_be_deleted_online_entry_ids = []
+    index = 0
+    for index, item in enumerate(existing_online_entry_text_chunk_filenames):
+        existing_text_chunk = item
+        existing_md_hash = existing_online_entry_md_hashes[index]
+        existing_id = existing_online_entry_ids[index]
+        if existing_text_chunk in candidate_entries:
+            candidate_md_hash = candidate_entries[existing_text_chunk]
+            if existing_md_hash != candidate_md_hash:
+                logging.info(
+                    f"The entry {existing_text_chunk} in the Chroma database "
+                    + "will be deleted because its content has changed."
+                )
+                to_be_deleted_online_entry_ids.append(existing_id)
+        else:
+            logging.info(
+                f"The entry {existing_text_chunk} in the Chroma database "
+                + "will be deleted because it is no longer found in the current dataset."
+            )
+            to_be_deleted_online_entry_ids.append(existing_id)
+
+    # Delete identified entries in the Chroma database.
+    if to_be_deleted_online_entry_ids:
+        collection.delete(ids=to_be_deleted_online_entry_ids)
+        deleted_entries_count = len(to_be_deleted_online_entry_ids)
+        print(f"Deleted entries count: {deleted_entries_count}")
+    else:
+        print(f"Keeping all existing entries in the Chroma database.")
+    return to_be_deleted_online_entry_ids
+
+
+# Delete entries in the online corpus if we cannot find matches in the current dataset.
+def delete_unmatched_entries_in_online_corpus(
+    product_config: ProductConfig, semantic_object, corpus_name
+):
+    print()
+    print(f"Scanning the online corpus to identify chunks to be deleted.")
+    print(f"(This may take some time.)")
+    # Get all chunks in the online corpus.
+    all_chunks = []
+    all_docs = semantic_object.get_all_docs(corpus_name=corpus_name, print_output=False)
+    for doc in all_docs:
+        doc_name = str(doc.name)
+        chunks = semantic_object.get_all_chunks(doc_name=doc_name, print_output=False)
+        for chunk in chunks:
+            all_chunks.append(chunk)
+
+    # Examine the new candidate entries in the current `data` directory.
+    candidate_entries = {}
+    (index_object, full_index_path) = load_index(input_path=product_config.output_path)
+    for product in index_object:
+        dictionary_input = index_object[product]
+    # Extract the text chunk name and hash from each chunk data.
+    for item in dictionary_input:
+        chunk_data = dictionary_input[item]
+        text_chunk_filename = ""
+        text_chunk_md_hash = ""
+        # print(f"Candidate text chunk data: {chunk_data}")
+        if "text_chunk_filename" in chunk_data:
+            text_chunk_filename = chunk_data["text_chunk_filename"]
+        if "md_hash" in chunk_data:
+            text_chunk_md_hash = chunk_data["md_hash"]
+        # print(f"Candidate text chunk filename: {text_chunk_filename}")
+        if text_chunk_filename != "":
+            candidate_entries[text_chunk_filename] = text_chunk_md_hash
+
+    # Compare the existing online entries to the candidate entries.
+    to_be_deleted_online_chunk_names = []
+    for chunk in all_chunks:
+        existing_chunk_name = chunk.name
+        existing_md_hash = ""
+        existing_text_chunk_filename = ""
+        metadata = chunk.custom_metadata
+        for item in metadata:
+            if item.key == "md_hash":
+                # print(f"md_hash: {item.string_value}")
+                existing_md_hash = item.string_value
+            elif item.key == "text_chunk_filename":
+                # print(f"text_chunk_filename: {item.string_value}")
+                existing_text_chunk_filename = item.string_value
+        if existing_text_chunk_filename in candidate_entries:
+            candidate_md_hash = candidate_entries[existing_text_chunk_filename]
+            if existing_md_hash != candidate_md_hash:
+                logging.info(
+                    f"{existing_text_chunk_filename} in the online corpus "
+                    + "will be deleted because its content has changed."
+                )
+                to_be_deleted_online_chunk_names.append(existing_chunk_name)
+        else:
+            logging.info(
+                f"{existing_text_chunk_filename} in the online corpus will be "
+                + "deleted because it is no longer found in the current dataset."
+            )
+            to_be_deleted_online_chunk_names.append(existing_chunk_name)
+
+    # Delete identified chunks in the online corpus.
+    if to_be_deleted_online_chunk_names:
+        # Initialize a progress bar object.
+        progress_bar = tqdm.tqdm(
+            position=0, desc="Deleting the chunk", bar_format="{desc}"
+        )
+        # Loop for deleting chunks online.
+        for chunk_name in to_be_deleted_online_chunk_names:
+            # progress_bar.update(1)
+            progress_bar.set_description_str(
+                f"Deleting the chunk {chunk_name}", refresh=True
+            )
+            semantic_object.delete_a_chunk(chunk_name)
+        delete_count = len(to_be_deleted_online_chunk_names)
+        progress_bar.set_description_str(
+            f"Deleted chunks count: {delete_count}", refresh=False
+        )
+    else:
+        print(f"Keeping all existing chunks in the online corpus.")
+    return to_be_deleted_online_chunk_names
+
+
 # Read plain text files (.md) from an input dir and
 # add their content to the vector database.
 # Embeddings are generated automatically as they are added to the database.
@@ -151,10 +327,18 @@ def populateToDbFromProduct(product_config: ProductConfig):
                 name=item.collection_name,
                 embedding_function=embedding_function_gemini,
             )
+            if (
+                hasattr(product_config, "enable_delete_chunks")
+                and product_config.enable_delete_chunks == "True"
+            ):
+                # Delete entries in the database if we cannot find matches
+                # in the current dataset.
+                delete_unmatched_entries_in_chroma(
+                    product_config, chroma_client, collection
+                )
 
     # Initialzie the Semantic Retreival API.
     corpus_name = ""
-    all_docs = []
     if product_config.db_type == "google_semantic_retriever":
         logging.info("Initializing the Semantic Retrieval API for an online storage.")
         semantic = SemanticRetriever()
@@ -164,21 +348,17 @@ def populateToDbFromProduct(product_config: ProductConfig):
                 if semantic.does_this_corpus_exist(corpus_name) == False:
                     # Create a new corpus.
                     semantic.create_a_new_corpus(item.corpus_display, corpus_name)
-                else:
-                    # Get all documents from the existing corpus.
-                    # This array is used for detecting duplicate entries.
-                    all_docs = semantic.get_all_docs(corpus_name=corpus_name)
-
-    # These variables track the resource names of documents for the Semantic Retrieval API.
-    document_name_in_corpus = ""
-    dict_document_names_in_corpus = {}
-
-    # Load the file index information from the file_index.json file.
-    (index, full_index_path) = load_index(input_path=product_config.output_path)
+                elif (
+                    hasattr(product_config, "enable_delete_chunks")
+                    and product_config.enable_delete_chunks == "True"
+                ):
+                    # Delete chunks in the corpus if we cannot find matches in the current dataset.
+                    delete_unmatched_entries_in_online_corpus(
+                        product_config, semantic, corpus_name
+                    )
 
-    # Get the total number of files for the progress bar.
+    # Initialize progress bar objects.
     file_count = get_file_count_in_a_dir(product_config.output_path)
-    # Prepare progress bars.
     (
         progress_bar,
         progress_new_file,
@@ -186,13 +366,20 @@ def populateToDbFromProduct(product_config: ProductConfig):
         progress_update_file,
     ) = init_progress_bars(file_count)
 
+    # Get the preprocess information from the `file_index.json` file.
+    (index, full_index_path) = load_index(input_path=product_config.output_path)
+
+    # Local variables track the resource names of documents for the Semantic Retrieval API.
+    document_name_in_corpus = ""
+    dict_document_names_in_corpus = {}
+
     # Local variables for counting files.
     total_files = 0
     updated_count = 0
     new_count = 0
     unchanged_count = 0
 
-    # Main loop that goes through each `path` in config.yaml.
+    # Loop through each `path` in the `config.yaml` file.
     for root, dirs, files in os.walk(product_config.output_path):
         # Convert `output_path` to be a fully resolved path.
         fully_resolved_output = end_path_backslash(
@@ -312,9 +499,12 @@ def populateToDbFromProduct(product_config: ProductConfig):
                     f"Skipped {file} because there is no configured parser for extension {extension}"
                 )
 
-    print("")
-    print(f"===========================================")
-    print("Total number of entries: " + str(total_files))
+    progress_bar.set_description_str(
+        f"Finished processing text chunk files (and file_index.json).", refresh=True
+    )
+    progress_unchanged_file.set_description_str(
+        f"Total number of entries: {total_files}", refresh=True
+    )
 
 
 def findFileinDict(input_file_name: str, index_object, content_file):
@@ -322,6 +512,12 @@ def findFileinDict(input_file_name: str, index_object, content_file):
     for product in index_object:
         dictionary_input = index_object[product]
     if input_file_name in dictionary_input:
+        chunk_data = dictionary_input[input_file_name]
+        # Extract the text chunk name from the index object.
+        text_chunk_filename = ""
+        if "text_chunk_filename" in chunk_data:
+            text_chunk_filename = chunk_data["text_chunk_filename"]
+            # logging.error(f"Chunk name: {text_chunk_filename}")
         # If metadata exists, add these to a dictionary that is then
         # merged with other metadata values
         if "metadata" in dictionary_input[input_file_name]:
@@ -340,6 +536,9 @@ def findFileinDict(input_file_name: str, index_object, content_file):
         section.content = content_file
         # Combines Section db in dictionary with extra
         metadata_dict_final = section.encodeToChromaDBNoContent() | metadata_dict_extra
+        # Add the text chunk filename to the metadata.
+        if text_chunk_filename != "":
+            metadata_dict_final["text_chunk_filename"] = text_chunk_filename
         # Overide title if it exists from frontmatter
         if "title" in metadata_dict_final:
             doc_title = str(metadata_dict_final["title"])
@@ -370,7 +569,7 @@ def load_index(
     full_index_path = resolve_path(end_path_backslash(input_path) + input_index_name)
     try:
         with open(full_index_path, "r", encoding="utf-8") as index_file:
-            print("Using file index: " + full_index_path + "\n")
+            logging.info("Using file index: " + full_index_path + "\n")
             index = json.load(index_file)
             return index, full_index_path
     except FileNotFoundError:
@@ -395,6 +594,7 @@ def process_all_products(
         print(f"Processing product: {product.product_name}")
         print(f"Input directory: {resolve_path(product.output_path)}")
         print(f"Database operation db type: {product.db_type}")
+        print()
         for item in product.db_configs:
             print(f"{item}")
         print(f"===========================================")
diff --git a/examples/gemini/python/docs-agent/docs_agent/preprocess/splitters/markdown_splitter.py b/examples/gemini/python/docs-agent/docs_agent/preprocess/splitters/markdown_splitter.py
index fc8f84292..43eba1ce7 100644
--- a/examples/gemini/python/docs-agent/docs_agent/preprocess/splitters/markdown_splitter.py
+++ b/examples/gemini/python/docs-agent/docs_agent/preprocess/splitters/markdown_splitter.py
@@ -192,7 +192,13 @@ def DictionarytoSection(metadata: dict) -> Section:
 
 
 class Page:
-    def __init__(self, title: str, URL: str, section_count: int, metadata: typing.Optional[dict] = None):
+    def __init__(
+        self,
+        title: str,
+        URL: str,
+        section_count: int,
+        metadata: typing.Optional[dict] = None,
+    ):
         self.title = title
         self.URL = URL
         self.section_count = section_count
@@ -302,13 +308,18 @@ def make_markdown_chunk(markdown_text, header_id_spaces):
                         section_title = "RFC (request for comment)"
                 # Removing this line as this can be added (if needed) once retrieved from the db
                 # section_markdown += section_intro.format(section_title=section_title)
-        elif line.startswith("#") and first_header == False:
-            section_done = True
-            remaining_markdown += line + "\n"
-        elif not (line.startswith("#")) and section_done:
-            remaining_markdown += line + "\n"
+        elif len(section_markdown) > 100:
+            # Temp solution: Do not create a chunk if the size is less than 100 chars.
+            if line.startswith("#") and first_header == False:
+                section_done = True
+                remaining_markdown += line + "\n"
+            elif not (line.startswith("#")) and section_done:
+                remaining_markdown += line + "\n"
+            else:
+                section_markdown += line + "\n"
         else:
             section_markdown += line + "\n"
+    section_level = level_to_int(section_level)
     return (
         section_id,
         section_level,
@@ -318,6 +329,35 @@ def make_markdown_chunk(markdown_text, header_id_spaces):
     )
 
 
+# Returns an int of a level to avoid blank string
+def level_to_int(level) -> int:
+    if level == "":
+        level = 0
+    else:
+        level = int(level)
+    return level
+
+
+# In a regular child level the length of the parent_tree should be greater
+# than the current level, so for example a header 2 (level 2) may have a
+# parent_tree of [0, 1], in this case we want to append the previous previous_id
+# Conditions to be aware of:
+# If headers are out of order, you may end up with an array level with
+# the same value, such as a header 4 that ends with a parent_tree of
+# [0, 1, 25, 25] indicates that you jumped from a header 2, right into a 4
+def build_parent_tree(
+    parent_tree: list[int], level: int, previous_id: int
+) -> list[int]:
+    if len(parent_tree) != level:
+        first = True
+        while len(parent_tree) != level:
+            if len(parent_tree) >= level:
+                parent_tree.pop()
+            elif len(parent_tree) <= level:
+                parent_tree.append(previous_id)
+    return parent_tree
+
+
 # This function cleans section ids from special characters
 def clean_section_id(section_id: str) -> str:
     section_id = re.sub("'", "", section_id)
@@ -363,6 +403,42 @@ def verify_file(file):
         logging.error(f"[FileNotFound] Missing the include file: {file}")
 
 
+# Takes in current section, then returns an array of
+# sections that it split by lines
+def split_sections_by_lines(section: Section):
+    buffer = []
+    for line in section.content.split("\n"):
+        # Special case if line is too long - tends to be comma seperated lists
+        if get_byte_size(line) > 5000:
+            for item in line.split(","):
+                item = item + ","
+                buffer.append(item)
+        else:
+            buffer.append(line)
+    chunks = construct_chunks(buffer)
+    page_sections = []
+    chunk_count = 0
+    for chunk in chunks:
+        # Calculate tokens for new chunk
+        token_count = tokenCount.returnHighestTokens(chunk)
+        # Section id needs to get bumped up
+        new_section = Section(
+            (section.id + chunk_count),
+            section.name_id,
+            section.page_title,
+            section.section_title,
+            section.level,
+            section.previous_id,
+            section.parent_tree.copy(),
+            token_count,
+            markdown_to_text(chunk),
+        )
+        chunk_count += 1
+        page_sections.append(new_section)
+    return page_sections
+    # return page_sections, remaining_content
+
+
 # This function converts Markdown page (#), section (##), and subsection (###)
 # headings into plain English.
 def process_markdown_page(markdown_text, header_id_spaces: str = "_"):
@@ -388,38 +464,19 @@ def process_markdown_page(markdown_text, header_id_spaces: str = "_"):
         name_id, level, title, content, remaining_content = make_markdown_chunk(
             markdown_text=remaining_content, header_id_spaces=header_id_spaces
         )
+        # Ensure parent_level is an int
         parent_level = int(parent_level)
-        if level == "":
-            level = 0
-        else:
-            level = int(level)
-        token_count = tokenCount.returnHighestTokens(content)
-        section_id += 1
         # Indicates the first encountered header since parent_level and parent_tree have base values
-        if parent_level == 0 and parent_tree == [0]:
+        if parent_level == 0 and parent_tree == [0] and "title" not in data:
             page_title = title
-        # In a regular child level the length of the parent_tree should be greater
-        # than the current level, so for example a header 2 (level 2) may have a
-        # parent_tree of [0, 1], in this case we want to append the previous previous_id
-        # Conditions to be aware of:
-        # If headers are out of order, you may end up with an array level with
-        # the same value, such as a header 4 that ends with a parent_tree of
-        # [0, 1, 25, 25] indicates that you jumped from a header 2, right into a 4
-        # todo: cleanup parent_tree when headers are out of order
-        if len(parent_tree) != level:
-            first = True
-            while len(parent_tree) != level:
-                if len(parent_tree) >= level:
-                    parent_tree.pop()
-                elif len(parent_tree) <= level:
-                    parent_tree.append(previous_id)
-        # The length of the parent_tree should always equal the level before this exits
-        # elif (len(parent_tree) == level):
-        #     #Do not do anything here as these are siblings with shared parents
-        #     print ("No change to parent headers")
-        # Clean the markdown content such as comments
-        content = markdown_to_text(content)
-        # Create a Section object for each section of the doc. Copy ensures parent_tree doesn't get overwritten
+        # Builds the parent tree based on current section level and the previous_id
+        parent_tree = build_parent_tree(parent_tree, level, previous_id)
+        plain_text_content = markdown_to_text(content)
+        token_count = tokenCount.returnHighestTokens(plain_text_content)
+        # This goes up as sections start at 1
+        section_id += 1
+        # Initializes a Section, content may be replaced if too large
+        # Copy ensures parent_tree doesn't get overwritten
         section = Section(
             section_id,
             name_id,
@@ -429,13 +486,22 @@ def process_markdown_page(markdown_text, header_id_spaces: str = "_"):
             previous_id,
             parent_tree.copy(),
             token_count,
-            content,
+            plain_text_content,
         )
-        # Make a list of section objects
-        page_sections.append(section)
-        # print ("Tree:")
-        # print (parent_tree)
-        # print ("Count: " + str(section_count) + "\nParent id: " + str(previous_id) + "\nParent level: " + str(parent_level) + "\nHeading level: " + str(level) + "\nTitle: " + title + "\nID: " + name_id + "\nContent:\n" + content)
+        # If content is larger than 5KB - split by lines
+        if len(plain_text_content.encode("utf-8")) > 5000:
+            # Return an array of sections that were split and the remain content
+            logging.info("Chunk is too big - splitting by lines")
+            new_sections = split_sections_by_lines(section=section)
+            # Merge the list of sections and bump up the section_id.
+            # Length has to be reduced by 1 since original chunk was already
+            # counted
+            section_id = section_id + (len(new_sections)-1)
+            page_sections += new_sections
+        # Create a Section object for sections under 6k of the doc.
+        else:
+            # If less than 5kb - append to a list of section objects
+            page_sections.append(section)
         # Prepare previous_id for next section
         previous_id = int(section_id)
         parent_level = int(level)
@@ -557,20 +623,21 @@ def process_document_into_sections(markdown_text):
                     buffer.clear()
         buffer.append(line)
     # Add the last section on the page.
-    content = convert_array_to_buffer(buffer)
-    sections.append(content)
+    contents = construct_chunks(buffer)
+    for content in contents:
+        sections.append(content)
     return sections
 
 
 # Process an array of Markdwon text into an array of string buffers
-# whose size is smaller than 6KB.
+# whose size is smaller than 5KB.
 def construct_chunks(lines):
     contents = []
     buffer_size = get_byte_size(lines)
-    if int(buffer_size) > 6000:
-        # If the protocol is larget than 6KB, divide it into two.
+    if int(buffer_size) > 5000:
+        # If the protocol is larger than 5KB, divide it into two.
         logging.info(
-            "Found a text chunk greater than 6KB (size: " + str(buffer_size) + ")."
+            "Found a text chunk greater than 5KB (size: " + str(buffer_size) + ")."
         )
         (first_half, second_half) = divide_an_array(lines)
         first_content = construct_chunks(first_half)
@@ -582,12 +649,13 @@ def construct_chunks(lines):
         contents.append(chunk)
     return contents
 
+
 # Convert an array into a string buffer.
 def convert_array_to_buffer(lines):
-   content = ""
-   for line in lines:
+    content = ""
+    for line in lines:
         content += line + "\n"
-   return content
+    return content
 
 
 # Get the byte size of lines.
@@ -604,4 +672,3 @@ def divide_an_array(lines):
     first_half = lines[:half_point]
     second_half = lines[half_point:]
     return first_half, second_half
-
diff --git a/examples/gemini/python/docs-agent/docs_agent/storage/google_semantic_retriever.py b/examples/gemini/python/docs-agent/docs_agent/storage/google_semantic_retriever.py
index d95c180cf..1a84bc2fd 100644
--- a/examples/gemini/python/docs-agent/docs_agent/storage/google_semantic_retriever.py
+++ b/examples/gemini/python/docs-agent/docs_agent/storage/google_semantic_retriever.py
@@ -211,6 +211,15 @@ def create_a_chunk(
             )
         return response
 
+    def delete_a_chunk(self, chunk_name: str):
+        response = []
+        try:
+            request = glm.DeleteChunkRequest(name=chunk_name)
+            response = self.retriever_service_client.delete_chunk(request)
+        except:
+            logging.error(f"Cannot delete a chunk: {chunk_name}")
+        return response
+
     def create_a_doc_chunk(
         self,
         corpus_name: str,
@@ -284,6 +293,54 @@ def get_all_docs(self, corpus_name: str, print_output: bool = False):
             logging.error("Error in listing all docs: " + corpus_name)
             return all_docs
 
+    def get_all_chunks(self, doc_name: str, print_output: bool = False):
+        all_chunks = []
+        try:
+            request = glm.ListChunksRequest(parent=doc_name, page_size=20)
+            response = self.retriever_service_client.list_chunks(request)
+            index = 0
+            for chunk in response.chunks:
+                if print_output:
+                    index += 1
+                    print(f"\nChunk # {index}")
+                    print(f"Name: {chunk.name}")
+                    metadata = chunk.custom_metadata
+                    for item in metadata:
+                        if item.key == "uuid":
+                            print(f"uuid: {item.string_value}")
+                        elif item.key == "md_hash":
+                            print(f"md_hash: {item.string_value}")
+                        elif item.key == "text_chunk_filename":
+                            print(f"text_chunk_filename: {item.string_value}")
+                all_chunks.append(chunk)
+            while (
+                hasattr(response, "next_page_token") and response.next_page_token != ""
+            ):
+                request = glm.ListChunksRequest(
+                    parent=doc_name,
+                    page_size=20,
+                    page_token=response.next_page_token,
+                )
+                response = self.retriever_service_client.list_chunks(request)
+                for chunk in response.chunks:
+                    if print_output:
+                        index += 1
+                        print(f"\nChunk # {index}")
+                        print(f"Name: {chunk.name}")
+                        metadata = chunk.custom_metadata
+                        for item in metadata:
+                            if item.key == "uuid":
+                                print(f"uuid: {item.string_value}")
+                            elif item.key == "md_hash":
+                                print(f"md_hash: {item.string_value}")
+                            elif item.key == "text_chunk_filename":
+                                print(f"text_chunk_filename: {item.string_value}")
+                    all_chunks.append(chunk)
+            return all_chunks
+        except:
+            logging.error("Error in listing all chunks: " + doc_name)
+            return all_chunks
+
     def share_a_corpus(self, corpus_name: str, email: str, role: str):
         shared_user_email = email
         user_type = "USER"
@@ -325,4 +382,4 @@ def delete_permission(self, permission_name: str):
         delete_permission_response = self.permission_service_client.delete_permission(
             request
         )
-        return delete_permission_response
\ No newline at end of file
+        return delete_permission_response
diff --git a/examples/gemini/python/docs-agent/docs_agent/utilities/config.py b/examples/gemini/python/docs-agent/docs_agent/utilities/config.py
index c56456da2..84c32e9f4 100644
--- a/examples/gemini/python/docs-agent/docs_agent/utilities/config.py
+++ b/examples/gemini/python/docs-agent/docs_agent/utilities/config.py
@@ -37,19 +37,25 @@ def __init__(
         corpus_name: typing.Optional[str] = None,
         # Only used when creating a corpus
         corpus_display: typing.Optional[str] = None,
+        secondary_db_type: typing.Optional[str] = None,
+        secondary_corpus_name: typing.Optional[str] = None,
     ):
         self.db_type = db_type
         self.vector_db_dir = vector_db_dir
         self.collection_name = collection_name
         self.corpus_name = corpus_name
         self.corpus_display = corpus_display
+        self.secondary_db_type = secondary_db_type
+        self.secondary_corpus_name = secondary_corpus_name
 
     def __str__(self):
         return f"Database type: {self.db_type}\n\
 Vector database dir: {self.vector_db_dir}\n\
 Collection name: {self.collection_name}\n\
 Corpus name: {self.corpus_name}\n\
-Corpus display: {self.corpus_display}\n"
+Corpus display: {self.corpus_display}\n\
+Secondary database type: {self.secondary_db_type}\n\
+Secondary corpus name: {self.secondary_corpus_name}\n"
 
     def return_vector_db_dir(self):
         return self.vector_db_dir
@@ -299,7 +305,13 @@ def __init__(
         markdown_splitter: str = "token_splitter",
         db_type: str = "chroma",
         app_mode: str = "web",
+        app_port: int = 5000,
+        feedback_mode: str = "rewrite",
         enable_show_logs: str = "False",
+        enable_logs_to_markdown: str = "False",
+        enable_delete_chunks: str = "False",
+        secondary_db_type: typing.Optional[str] = None,
+        secondary_corpus_name: typing.Optional[str] = None,
     ):
         self.product_name = product_name
         self.docs_agent_config = docs_agent_config
@@ -312,7 +324,13 @@ def __init__(
         self.log_level = log_level
         self.inputs = inputs
         self.app_mode = app_mode
+        self.app_port = app_port
+        self.feedback_mode = feedback_mode
         self.enable_show_logs = enable_show_logs
+        self.enable_logs_to_markdown = enable_logs_to_markdown
+        self.enable_delete_chunks = enable_delete_chunks
+        self.secondary_db_type = secondary_db_type
+        self.secondary_corpus_name = secondary_corpus_name
 
     def __str__(self):
         # Extracts the list of Inputs
@@ -328,9 +346,15 @@ def __str__(self):
         return f"Product: {self.product_name}\n\
 Docs Agent config: {self.docs_agent_config}\n\
 App mode: {self.app_mode}\n\
+App port: {self.app_port}\n\
+Feedback mode: {self.feedback_mode}\n\
 Enable show logs: {self.enable_show_logs}\n\
+Enable logs to Markdown: {self.enable_logs_to_markdown}\n\
+Enable delete chunks: {self.enable_delete_chunks}\n\
 Markdown splitter: {self.markdown_splitter}\n\
 Database type: {self.db_type}\n\
+Secondary database type: {self.secondary_db_type}\n\
+Secondary corpus name: {self.secondary_corpus_name}\n\
 Output path: {self.output_path}\n\
 \nDatabase configs:\n{db_config_str}\n\
 Log level: {self.log_level}\n\
@@ -399,12 +423,38 @@ def returnProducts(self, product: typing.Optional[str] = None) -> ConfigFile:
                 except KeyError:
                     app_mode = "web"
                 if app_mode not in supported_app_modes:
-                    logging.error(f"Your configuration is using an invalid mode: {app_mode}. Valid modes are {supported_app_modes}")
+                    logging.error(
+                        f"Your configuration is using an invalid mode: {app_mode}. Valid modes are {supported_app_modes}"
+                    )
                     return sys.exit(1)
+                try:
+                    app_port = int(item["app_port"])
+                except KeyError:
+                    app_port = 5000
+                try:
+                    feedback_mode = item["feedback_mode"]
+                except KeyError:
+                    feedback_mode = "feedback"
                 try:
                     enable_show_logs = item["enable_show_logs"]
                 except KeyError:
                     enable_show_logs = "False"
+                try:
+                    enable_logs_to_markdown = item["enable_logs_to_markdown"]
+                except KeyError:
+                    enable_logs_to_markdown = "False"
+                try:
+                    enable_delete_chunks = item["enable_delete_chunks"]
+                except KeyError:
+                    enable_delete_chunks = "False"
+                try:
+                    secondary_db_type = item["secondary_db_type"]
+                except KeyError:
+                    secondary_db_type = None
+                try:
+                    secondary_corpus_name = item["secondary_corpus_name"]
+                except KeyError:
+                    secondary_corpus_name = None
                 try:
                     product_config = ProductConfig(
                         product_name=item["product_name"],
@@ -418,13 +468,17 @@ def returnProducts(self, product: typing.Optional[str] = None) -> ConfigFile:
                         conditions=item["conditions"],
                         inputs=item["inputs"],
                         app_mode=app_mode,
+                        app_port=app_port,
+                        feedback_mode=feedback_mode,
                         enable_show_logs=enable_show_logs,
+                        enable_logs_to_markdown=enable_logs_to_markdown,
+                        enable_delete_chunks=enable_delete_chunks,
+                        secondary_db_type=secondary_db_type,
+                        secondary_corpus_name=secondary_corpus_name,
                     )
                     # This is done for keys with children
                     # Inputs
-                    new_inputs = ReadInputs(
-                        input_list=item["inputs"]
-                    ).returnInputs()
+                    new_inputs = ReadInputs(input_list=item["inputs"]).returnInputs()
                     product_config.inputs = new_inputs
                     product_config.inputs = new_inputs
                     # Conditions
@@ -433,9 +487,7 @@ def returnProducts(self, product: typing.Optional[str] = None) -> ConfigFile:
                     ).returnConditions()
                     product_config.conditions = new_conditions
                     # Models
-                    new_models = ReadModels(
-                        input_list=item["models"]
-                    ).returnModels()
+                    new_models = ReadModels(input_list=item["models"]).returnModels()
                     product_config.models = new_models
                     # DbConfigs
                     new_db_configs = ReadDbConfigs(
@@ -475,7 +527,9 @@ def returnProducts(self, product: typing.Optional[str] = None) -> ConfigFile:
 
 
 # Function to make using common_options simpler
-def return_config_and_product(config_file: typing.Optional[str] = None, product: list[str] = [""]):
+def return_config_and_product(
+    config_file: typing.Optional[str] = None, product: list[str] = [""]
+):
     if config_file is None:
         loaded_config = ReadConfig()
     else:
diff --git a/examples/gemini/python/docs-agent/helpme.sh b/examples/gemini/python/docs-agent/helpme.sh
index 96e0716e1..52e287b05 100755
--- a/examples/gemini/python/docs-agent/helpme.sh
+++ b/examples/gemini/python/docs-agent/helpme.sh
@@ -16,10 +16,38 @@
 # limitations under the License.
 #
 
+# IF NECESSARY, ADJUST THIS PATH TO YOUR `docs-agent` DIRECTORY.
+docs_agent_dir="$HOME/docs-agent"
+
+# Initialize request and filename variables.
+request=""
+filename=""
+
+# Loop through the arguments and look for the `--file` flag.
+while [[ $# -gt 0 ]]; do
+  case "$1" in
+    --file)
+      shift
+      filename="$1"
+      shift
+      ;;
+    *)
+      request="$request $1"
+      shift
+      ;;
+  esac
+done
+
+# Get the full path for the filename.
+if [[ -n "$filename" ]]; then
+  filename=$(readlink -m ${filename})
+else
+  filename="None"
+fi
+
 # Check if the POETRY_ACTIVE environment variable is set
 if [ -z "$POETRY_ACTIVE" ]; then
-   cd $HOME/docs-agent && poetry run agent helpme $@
+  cd "$docs_agent_dir" && poetry run agent helpme "$request" --file "$filename"
 else
-   agent helpme $@
+  agent helpme "$request" --file "$filename"
 fi
-
diff --git a/examples/gemini/python/docs-agent/pyproject.toml b/examples/gemini/python/docs-agent/pyproject.toml
index 3a1aff109..719a54e7f 100644
--- a/examples/gemini/python/docs-agent/pyproject.toml
+++ b/examples/gemini/python/docs-agent/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "docs-agent"
-version = "0.3.2"
+version = "0.3.3"
 description = ""
 authors = ["Docs Agent contributors"]
 readme = "README.md"
diff --git a/examples/gemini/python/docs-agent/tellme.sh b/examples/gemini/python/docs-agent/tellme.sh
index 531299f8f..a51d48554 100755
--- a/examples/gemini/python/docs-agent/tellme.sh
+++ b/examples/gemini/python/docs-agent/tellme.sh
@@ -16,9 +16,12 @@
 # limitations under the License.
 #
 
+# IF NECESSARY, ADJUST THIS PATH TO YOUR `docs-agent` DIRECTORY.
+docs_agent_dir="$HOME/docs-agent"
+
 # Check if the POETRY_ACTIVE environment variable is set
 if [ -z "$POETRY_ACTIVE" ]; then
-   cd $HOME/docs-agent && poetry run agent tellme $@
+   cd "$docs_agent_dir" && poetry run agent tellme $@
 else
    agent tellme $@
 fi

From 87138c4633c3378f92bdf075486d5fbb930922e2 Mon Sep 17 00:00:00 2001
From: Kyo Lee <kyol@google.com>
Date: Fri, 10 May 2024 19:03:53 +0000
Subject: [PATCH 07/12] [Docs Agent] Release of Docs Agent v.0.3.4

What's changed

-  **Enhanced file processing:** Process an entire directory with the
   `agent helpme` command and export results to a `responses.yaml` file.
-  **Improved session control:** Manage conversation context using the
   `--new` and `--cont` flags with `agent helpme`.
-  **Debugging tools:** Capture detailed logs for troubleshooting and
   export them to CSV with the new `agent write-logs-to-csv` command.
-  **Documentation expansion:** Explore updated configuration references,
   recent release notes, and a new concept doc on chunking.
-  **Bug fixes:** Added fixes for the FIDL splitter, file handling, and
   blank results, and updates to various README files.
---
 examples/gemini/python/docs-agent/README.md   | 121 +++-----
 .../docs-agent/docs/chunking-process.md       |  68 +++++
 .../python/docs-agent/docs/cli-reference.md   |  63 +++-
 .../docs-agent/docs/config-reference.md       | 156 ++++++++++
 .../python/docs-agent/docs/whats-new.md       |  26 ++
 .../docs-agent/docs_agent/agents/__init__.py  |   0
 .../docs_agent/agents/docs_agent.py           |  23 +-
 .../docs_agent/benchmarks/__init__.py         |   0
 .../docs_agent/interfaces/__init__.py         |   0
 .../docs_agent/interfaces/chatbot/chatui.py   | 112 +++++--
 .../chatbot/static/css/style-chatui-1.5.css   |  17 +-
 .../chatbot/static/css/style-chatui.css       |  17 +-
 .../chatbot/static/css/style-widget.css       |  19 +-
 .../chatbot/static/javascript/app.js          |  72 +++++
 .../chatbot/templates/admin/debugs.html       |  24 ++
 .../chatbot/templates/chat-widget/result.html |  19 ++
 .../chatbot/templates/chatui-1.5/result.html  |  19 ++
 .../chatbot/templates/chatui/result.html      |  19 ++
 .../docs-agent/docs_agent/interfaces/cli.py   | 271 ++++++++++++++--
 .../docs_agent/interfaces/run_console.py      | 289 ++++++++----------
 .../docs-agent/docs_agent/memory/__init__.py  |   0
 .../docs-agent/docs_agent/memory/logging.py   | 201 +++++++++++-
 .../docs-agent/docs_agent/models/__init__.py  |   0
 .../docs_agent/postprocess/__init__.py        |   0
 .../docs_agent/preprocess/README.md           | 112 ++++++-
 .../docs_agent/preprocess/__init__.py         |   0
 .../preprocess/files_to_plain_text.py         | 146 +++++----
 .../preprocess/splitters/__init__.py          |   0
 .../preprocess/splitters/fidl_splitter.py     |   4 +-
 .../docs-agent/docs_agent/storage/__init__.py |   0
 .../docs-agent/docs_agent/tests/__init__.py   |   0
 .../docs_agent/tests/cli_unit_tests.py        |  11 +
 .../test_vector_database.py                   |   0
 .../docs_agent/utilities/__init__.py          |   0
 .../docs-agent/docs_agent/utilities/config.py |  15 +-
 35 files changed, 1430 insertions(+), 394 deletions(-)
 create mode 100644 examples/gemini/python/docs-agent/docs/chunking-process.md
 create mode 100644 examples/gemini/python/docs-agent/docs/config-reference.md
 create mode 100644 examples/gemini/python/docs-agent/docs_agent/agents/__init__.py
 create mode 100644 examples/gemini/python/docs-agent/docs_agent/benchmarks/__init__.py
 create mode 100644 examples/gemini/python/docs-agent/docs_agent/interfaces/__init__.py
 create mode 100644 examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/admin/debugs.html
 create mode 100644 examples/gemini/python/docs-agent/docs_agent/memory/__init__.py
 create mode 100644 examples/gemini/python/docs-agent/docs_agent/models/__init__.py
 create mode 100644 examples/gemini/python/docs-agent/docs_agent/postprocess/__init__.py
 create mode 100644 examples/gemini/python/docs-agent/docs_agent/preprocess/__init__.py
 create mode 100644 examples/gemini/python/docs-agent/docs_agent/preprocess/splitters/__init__.py
 create mode 100644 examples/gemini/python/docs-agent/docs_agent/storage/__init__.py
 create mode 100644 examples/gemini/python/docs-agent/docs_agent/tests/__init__.py
 create mode 100644 examples/gemini/python/docs-agent/docs_agent/tests/cli_unit_tests.py
 rename examples/gemini/python/docs-agent/docs_agent/{testing => tests}/test_vector_database.py (100%)
 create mode 100644 examples/gemini/python/docs-agent/docs_agent/utilities/__init__.py

diff --git a/examples/gemini/python/docs-agent/README.md b/examples/gemini/python/docs-agent/README.md
index e7f11f294..357761fd2 100644
--- a/examples/gemini/python/docs-agent/README.md
+++ b/examples/gemini/python/docs-agent/README.md
@@ -9,10 +9,10 @@ and project deployment.
 
 ## Overview
 
-Docs Agent apps use a technique known as Retrieval Augmented Generation (RAG), which allows
-you to bring your own documents as knowledge sources to AI language models. This approach
-helps the AI language models to generate relevant and accurate responses that are grounded
-in the information that you provide and control.
+Docs Agent apps use a technique known as Retrieval Augmented Generation (RAG), which
+allows you to bring your own documents as knowledge sources to AI language models.
+This approach helps the AI language models to generate relevant and accurate responses
+that are grounded in the information that you provide and control.
 
 ![Docs Agent architecture](docs/images/docs-agent-architecture-01.png)
 
@@ -26,66 +26,43 @@ the [Set up Docs Agent][set-up-docs-agent] section below.
 
 The following list summarizes the tasks and features supported by Docs Agent:
 
-- **Process Markdown**: Split Markdown files into small plain text files. (See the
-  Python scripts in the [`preprocess`][preprocess-dir] directory.)
-- **Generate embeddings**: Use an embedding model to process small plain text files
-  into embeddings, and store them in a vector database. (See the
-  [`populate_vector_database.py`][populate-vector-database] script.)
-- **Perform semantic search**: Compare embeddings in the vector database to retrieve
-  most relevant content given user questions.
-- **Add context to a user question**: Add a list of text chunks returned from
-  a semantic search as context in a prompt. (See the
-  [Structure of a prompt to a language model][prompt-structure] section.)
-- **(Experimental) “Fact-check” responses**: This experimental feature composes
+- **Process Markdown**: Split Markdown files into small plain text chunks. (See
+  [Docs Agent chunking process][chunking-process].)
+- **Generate embeddings**: Use an embedding model to process text chunks into embeddings
+  and store them in a vector database.
+- **Perform semantic search**: Compare embeddings in a vector database to retrieve
+  chunks that are most relevant to user questions.
+- **Add context to a user question**: Add chunks returned from a semantic search as
+  [context][prompt-structure] to a prompt.
+- **Fact-check responses**: This [experimental feature][fact-check-section] composes
   a follow-up prompt and asks the language model to “fact-check” its own previous response.
-  (See the [Using a language model to fact-check its own response][fact-check-section]
-  section.)
-- **Generate related questions**: In addition to displaying a response to the user
-  question, the web UI displays 5 questions generated by the language model based on
-  the context of the user question. (See the
-  [Using a language model to suggest related questions][related-questions-section]
-  section.)
-- **Return URLs of documentation sources**: Docs Agent's vector database stores URLs
-  as metadata next to embeddings. Whenever the vector database is used to retrieve
-  text chunks for context, the database can also return the URLs of the sources used
-  to generate the embeddings.
-- **Collect feedback from users**: Docs Agent's chatbot web UI includes buttons that
-  allow users to [like generated responses][like-generated-responses] or
-  [submit rewrites][submit-a-rewrite].
+- **Generate related questions**: In addition to answering a question, Docs Agent can
+  [suggest related questions][related-questions-section] based on the context of the
+  question.
+- **Return URLs of source documents**: URLs are stored as chunks' metadata. This enables
+  Docs Agent to return the URLs of the source documents.
+- **Collect feedback from users**: Docs Agent's web app has buttons that allow users
+  to [like responses][like-generated-responses] or [submit rewrites][submit-a-rewrite].
 - **Convert Google Docs, PDF, and Gmail into Markdown files**: This feature uses
-  Apps Script to convert Google Docs, PDF, and Gmail into Markdown files, which then
-  can be used as input datasets for Docs Agent. (See the
-  [`apps_script`][apps-script-readme] directory.)
-- **Run benchmark test to monitor the quality of AI-generated responses**: Using
-  Docs Agent, you can run [benchmark test][benchmark-test] to measure and compare
-  the quality of text chunks, embeddings, and AI-generated responses.
-- **Use the Semantic Retrieval API and AQA model**: You can use Gemini's
-  [Semantic Retrieval API][semantic-api] to upload source documents to an online
-  corpus and use the [AQA model][aqa-model] that is specifically created for answering
-  questions using an online corpus.
-- **Manage online corpora using the Docs Agent CLI**: The Docs Agent CLI enables you
-  to create, populate, update and delete online corpora using the Semantic Retrieval AI.
-  For the list of all available Docs Agent command lines, see the
-  [Docs Agent CLI reference][cli-reference] page.
-- **Run the Docs Agent CLI from anywhere in a terminal**: You can set up the
-  Docs Agent CLI to ask questions to the Gemini model from anywhere in a terminal.
-  For more information, see the [Set up Docs Agent CLI][cli-readme] page.
-- **Support the Gemini 1.5 models**: You can use the new Gemini 1.5 models,
-  `gemini-1.5-pro-latest` and `text-embedding-004`, with Docs Agent today.
-  For the moment, the following `config.yaml` setup is recommended:
-
-  ```
-  models:
-  - language_model: "models/aqa"
-    embedding_model: "models/text-embedding-004"
-    api_endpoint: "generativelanguage.googleapis.com"
-  ...
-  app_mode: "1.5"
-  db_type: "chroma"
-  ```
-
-  The setup above uses 3 Gemini models to their strength: AQA (`aqa`),
-  Gemini 1.0 Pro (`gemini-pro`), and Gemini 1.5 Pro (`gemini-1.5-pro-latest`).
+  [Apps Script][apps-script-readme] to convert Google Docs, PDF, and Gmail into
+  Markdown files, which then can be used as input datasets for Docs Agent.
+- **Run benchmark test**: Docs Agent can [run benchmark test][benchmark-test] to measure
+  and compare the quality of text chunks, embeddings, and AI-generated responses.
+- **Use the Semantic Retrieval API and AQA model**: Docs Agent can use Gemini's
+  [Semantic Retrieval API][semantic-api] to upload source documents to online corpora
+  and use the [AQA model][aqa-model] for answering questions.
+- **Manage online corpora using the Docs Agent CLI**: The [Docs Agent CLI][cli-reference]
+  lets you create, update and delete online corpora using the Semantic Retrieval AI.
+- **Prevent duplicate chunks and delete obsolete chunks in databases**: Docs Agent
+  uses [metadata in chunks][chunking-process] to prevent uploading duplicate chunks
+  and delete obsolete chunks that are no longer present in the source.
+- **Run the Docs Agent CLI from anywhere in a terminal**:
+  [Set up the Docs Agent CLI][cli-readme] to make requests to the Gemini models
+  from anywhere in a terminal.
+- **Support the Gemini 1.5 models**: Docs Agent works with the Gemini 1.5 models,
+  `gemini-1.5-pro-latest` and `text-embedding-004`. The new ["1.5"][new-15-mode] web app
+  mode uses all three Gemini models to their strength: AQA (`aqa`), Gemini 1.0 Pro
+  (`gemini-pro`), and Gemini 1.5 Pro (`gemini-1.5-pro-latest`).
 
 For more information on Docs Agent's architecture and features,
 see the [Docs Agent concepts][docs-agent-concepts] page.
@@ -122,26 +99,26 @@ Update your host machine's environment to prepare for the Docs Agent setup:
 
 1. Update the Linux package repositories on the host machine:
 
-   ```posix-terminal
+   ```
    sudo apt update
    ```
 
 2. Install the following dependencies:
 
-   ```posix-terminal
+   ```
    sudo apt install git pipx python3-venv
    ```
 
 3. Install `poetry`:
 
-   ```posix-terminal
+   ```
    pipx install poetry
    ```
 
 4. To add `$HOME/.local/bin` to your `PATH` variable, run the following
    command:
 
-   ```posix-terminal
+   ```
    pipx ensurepath
    ```
 
@@ -157,7 +134,7 @@ Update your host machine's environment to prepare for the Docs Agent setup:
 
 6. Update your environment:
 
-   ```posix-termainl
+   ```
    source ~/.bashrc
    ```
 
@@ -202,25 +179,25 @@ Clone the Docs Agent project and install dependencies:
 
 1. Clone the following repo:
 
-   ```posix-terminal
+   ```
    git clone https://github.com/google/generative-ai-docs.git
    ```
 
 2. Go to the Docs Agent project directory:
 
-   ```posix-terminal
+   ```
    cd generative-ai-docs/examples/gemini/python/docs-agent
    ```
 
 3. Install dependencies using `poetry`:
 
-   ```posix-terminal
+   ```
    poetry install
    ```
 
 4. Enter the `poetry` shell environment:
 
-   ```posix-terminal
+   ```
    poetry shell
    ```
 
@@ -437,3 +414,5 @@ Meggin Kearney (`@Meggin`), and Kyo Lee (`@kyolee415`).
 [oauth-client]: https://ai.google.dev/docs/oauth_quickstart#set-cloud
 [cli-readme]: docs_agent/interfaces/README.md
 [cli-reference]: docs/cli-reference.md
+[chunking-process]: docs/chunking-process.md
+[new-15-mode]: docs/config-reference.md#app_mode
diff --git a/examples/gemini/python/docs-agent/docs/chunking-process.md b/examples/gemini/python/docs-agent/docs/chunking-process.md
new file mode 100644
index 000000000..e4c054b92
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs/chunking-process.md
@@ -0,0 +1,68 @@
+# Docs Agent chunking process
+
+This page describes Docs Agent's chunking process and potential optimizations.
+
+Currently, Docs Agent utilizes Markdown headings (`#`, `##`, and `###`) to
+split documents into smaller, manageable chunks. However, the Docs Agent team
+is actively developing more advanced strategies to improve the quality and
+relevance of these chunks for retrieval.
+
+## Chunking technique
+
+In Retrieval Augmented Generation ([RAG][rag]) based systems, ensuring each
+chunk contains the right information and context is crucial for accurate
+retrieval. The goal of an effective chunking process is to ensure that each
+chunk encapsulates a focused topic, which enhances the accuracy of retrieval
+and ultimately leads to better answers. At the same time, the Docs Agent team
+acknowledges the importance of a flexible approach that allows for
+customization based on specific datasets and use cases.
+
+Key characteristics in Docs Agent’s chunking process include:
+
+- **Docs Agent splits documents based on Markdown headings.** However,
+  this approach has limitations, especially when dealing with large sections.
+- **Docs Agent chunks are smaller than 5000 bytes (characters).** This size
+  limit is set by the embedding model used in generating embeddings.
+- **Docs Agent enhances chunks with additional metadata.** The metadata helps
+  Docs Agent to execute operations efficiently, such as preventing duplicate
+  chunks in databases and deleting obsolete chunks that are  no longer
+  present in the source.
+- **Docs Agent retrieves the top 5 chunks and displays the top chunk's URL.**
+  However, this is adjustable in Docs Agent’s configuration (see the `widget`
+  and `experimental` app modes).
+
+The Docs Agent team continues to explore various optimizations to enhance
+the functionality and effectiveness of the chunking process. These efforts
+include refining the chunking algorithm itself and developing advanced
+post-processing techniques, for instance, reconstructing chunks to original
+documents after retrieval.
+
+Additionally, the team has been exploring methods for co-optimizing content
+structure and chunking strategies, which aims to maximize retrieval
+effectiveness by ensuring the structure of the source document itself
+complements the chunking process.
+
+## Chunks retrieval
+
+Docs Agent employs two distinct approaches for storing and retrieving chunks:
+
+- **The local database approach uses a [Chroma][chroma] vector database.**
+  This approach grants greater control over the chunking and retrieval
+  process. This option is recommended for development and experimental
+  setups.
+- **The online corpus approach uses Gemini’s
+  [Semantic Retrieval API][semantic-retrieval].** This approach provides
+  the advantages of centrally hosted online databases, ensuring
+  accessibility for all users throughout the organization. This approach
+  has some drawbacks, as control is reduced because the API may dictate
+  how chunks are selected and where customization can be applied.
+
+Choosing between these approaches depends on the specific needs of the user’s
+deployment situation, which is to balance control and transparency against
+possible improvements in performance, broader reach and ease of use.
+
+<!-- Reference links -->
+
+[rag]: concepts.md
+[chroma]: https://docs.trychroma.com/
+[semantic-retrieval]: https://ai.google.dev/gemini-api/docs/semantic_retrieval
diff --git a/examples/gemini/python/docs-agent/docs/cli-reference.md b/examples/gemini/python/docs-agent/docs/cli-reference.md
index c3d3100d9..e7385aa92 100644
--- a/examples/gemini/python/docs-agent/docs/cli-reference.md
+++ b/examples/gemini/python/docs-agent/docs/cli-reference.md
@@ -3,10 +3,15 @@
 This page provides a list of the Docs Agent command lines and their usages
 and examples.
 
-**Important**: All `agent` commands in this page need to run in the
-`poetry shell` environment.
+The Docs Agent CLI helps developers to manage the Docs Agent project and
+interact with language models. It can handle various tasks such as
+processing documents, populating vector databases, launching the chatbot,
+running benchmark test, sending prompts to language models, and more.
 
-## Processing of Markdown files
+**Important**: All `agent` commands need to run in the `poetry shell`
+environment.
+
+## Processing documents
 
 ### Chunk Markdown files into small text chunks
 
@@ -53,7 +58,16 @@ The command below deletes development databases specified in the
 agent cleanup-dev
 ```
 
-## Docs Agent chatbot web app
+### Write logs to a CSV file
+
+The command below writes the summaries of all captured debugging information
+(in the `logs/debugs` directory) to  a `.csv` file:
+
+```sh
+agent write-logs-to-csv
+```
+
+## Launching the chatbot web app
 
 ### Launch the Docs Agent web app
 
@@ -89,7 +103,7 @@ a log view page (which is accessible at `<APP_URL>/logs`):
 agent chatbot --enable_show_logs
 ```
 
-## Docs Agent benchmark test
+## Running benchmark test
 
 ### Run the Docs Agent benchmark test
 
@@ -158,7 +172,44 @@ absolure or relative path, for example:
 agent helpme write comments for this C++ file? --file ../my-project/test.cc
 ```
 
-## Online corpus management
+### Ask for advice in a session
+
+The command below starts a new session (`--new`), which tracks responses,
+before running the `agent helpme` command:
+
+```sh
+agent helpme <REQUEST> --file <PATH_TO_FILE> --new
+```
+
+For example:
+
+```sh
+agent helpme write a draft of all features found in this README file? --file ./README.md --new
+```
+
+After starting a session, use the `--cont` flag to include the previous
+responses as context to the request:
+
+```sh
+agent helpme <REQUEST> --cont
+```
+
+For example:
+
+```sh
+agent helpme write a concept doc that delves into more details of these features? --cont
+```
+
+### Ask for advice using RAG
+
+The command below uses a local or online vector database (specified in
+the `config.yaml` file) to retrieve relevant context for the request:
+
+```sh
+agent helpme <REQUEST> --file <PATH_TO_FILE> --rag
+```
+
+## Managing online corpora
 
 ### List all existing online corpora
 
diff --git a/examples/gemini/python/docs-agent/docs/config-reference.md b/examples/gemini/python/docs-agent/docs/config-reference.md
new file mode 100644
index 000000000..cbb5eda9e
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs/config-reference.md
@@ -0,0 +1,156 @@
+# Docs Agent configuration reference
+
+This page provides a list of additional options that can be specified
+in the Docs Agent configuration file ([`config.yaml`][config-yaml]).
+
+## Web application options
+
+### app_port
+
+This field sets the port which the Docs Agent web app runs on.
+
+```
+app_port: 5001
+```
+
+By default, the web app is set to use port 5000.
+
+### app_mode
+
+This field controls the user interface mode of the web app.
+
+The options are:
+
+* `widget`: This mode launches a compact widget-style interface, suitable
+  for being embedded within a webpage.
+
+  ```
+  app_mode: "widget"
+  ```
+
+* `1.5`: This special mode is designed to be used with the Gemini 1.5 Pro
+  model.
+
+  ```
+  models:
+    - language_model: "models/aqa"
+  ...
+  app_mode: "1.5"
+  ```
+
+When this field is not specified, the web app is set to use the standard mode.
+
+## User feedback options
+
+### feedback_mode
+
+This field sets the type of feedback mechanism available to users for providing
+the quality or relevance of responses.
+
+The options are:
+
+* `feedback`: This is the default setting.
+
+  ```
+  feedback_mode: "feedback"
+  ```
+
+* `rewrite`: This option provides the "Rewrite this response" button to allows
+  users to suggest alternative responses.
+
+  ```
+  feedback_mode: "rewrite"
+  ```
+
+* `like_and_dislike`: This option provides simple "Like" and "Dislike" buttons.
+
+  ```
+  feedback_mode: "like_and_dislike"
+  ```
+
+## Logging options
+
+### log_level
+
+This field controls the level of detail captured in the logs generated by Docs
+Agent.
+
+Setting it to `VERBOSE` provides more comprehensive logging information:
+
+```
+log_level: "VERBOSE"
+```
+
+This field is set to `NORMAL` by default.
+
+### enable_show_logs
+
+Setting this field to `"True"` allows logs to be displayed on a web browser
+(which is accessible at `<APP_URL>/logs`):
+
+```
+enable_show_logs: "True"
+```
+
+### enable_logs_to_markdown
+
+Setting this field to `"True"` saves the generated answers as Markdown pages
+on the host machine:
+
+```
+enable_logs_to_markdown: "True"
+```
+
+### enable_logs_for_debugging
+
+Setting this field to `"True"` generates detailed logs for debugging purposes:
+
+```
+enable_logs_for_debugging: "True"
+```
+
+## Database management options
+
+### enable_delete_chunks
+
+Setting this field to `"True"` enables the ability to delete outdated, stale
+text chunks from the vector databases:
+
+```
+enable_delete_chunks: "True"
+```
+
+## Secondary database configuration
+
+Docs Agent allows for the use of a secondary database alongside the primary one
+for providing additional context from a different source.
+
+### secondary_db_type
+
+This field specifies the type of secondary database to be used:
+
+```
+secondary_db_type: "google_semantic_retrieval"
+```
+
+or
+
+```
+secondary_db_type: "chroma"
+```
+
+When `chroma` is specified, the collection in the `vector_stores/chroma`
+directory is used as the secondary database.
+
+### secondary_corpus_name
+
+This field defines the name of the corpus for the secondary database,
+for example:
+
+```
+secondary_corpus_name: "corpora/my-example-corpus"
+```
+
+<!-- Reference link -->
+
+[config-yaml]: ../config.yaml
diff --git a/examples/gemini/python/docs-agent/docs/whats-new.md b/examples/gemini/python/docs-agent/docs/whats-new.md
index fd2a5d09d..7794324ac 100644
--- a/examples/gemini/python/docs-agent/docs/whats-new.md
+++ b/examples/gemini/python/docs-agent/docs/whats-new.md
@@ -1,5 +1,31 @@
 # What's new in Docs Agent
 
+## April 2024
+
+* **Focus: Feature enhancements and usability improvements**
+* Expanded CLI functionality with options for managing online corpora and interacting with files.
+* Addressed bug fixes and performed code refactoring for improved stability and maintainability.
+* Added a new chat app template specifically designed for the **Gemini 1.5 model**.
+* Updated GenAI SDK version to `0.5.0`.
+* Introduced a splitter for handling of Fuchsia’s FIDL protocol files in the preprocessing stage.
+
+## March 2024
+
+* **Milestone: Introduction of the Docs Agent CLI**
+* Added the `tellme` command for direct interaction with Gemini from a Linux terminal.
+* Expanded CLI options for corpora management, including creation, deletion, and permission control.
+* Enhanced the chat app UI with a "loading" animation and probability-based response pre-screening.
+* Enabled displaying more URLs retrieved from the AQA model in the widget mode.
+* Added support for including URLs as metadata when uploading chunks to online corpora.
+
+## February 2024
+
+* **Focus: Refining AQA model integration**
+* Improved UI rendering of AQA model responses, especially for code segments.
+* Addressed bug fixes to handle unexpected AQA model responses.
+* Generated related questions by using retrieved context instead of a user question.
+* Started logging `answerable_probability` for AQA model responses.
+
 ## January 2024
 
 * **Milestone: Docs Agent uses AQA model and Semantric Retrieval API**
diff --git a/examples/gemini/python/docs-agent/docs_agent/agents/__init__.py b/examples/gemini/python/docs-agent/docs_agent/agents/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/examples/gemini/python/docs-agent/docs_agent/agents/docs_agent.py b/examples/gemini/python/docs-agent/docs_agent/agents/docs_agent.py
index 2bb8fd0d6..820c22034 100644
--- a/examples/gemini/python/docs-agent/docs_agent/agents/docs_agent.py
+++ b/examples/gemini/python/docs-agent/docs_agent/agents/docs_agent.py
@@ -41,7 +41,12 @@ class DocsAgent:
     """DocsAgent class"""
 
     # Temporary parameter of init_chroma
-    def __init__(self, config: ProductConfig, init_chroma: bool = True, init_semantic: bool = True):
+    def __init__(
+        self,
+        config: ProductConfig,
+        init_chroma: bool = True,
+        init_semantic: bool = True,
+    ):
         # Models settings
         self.config = config
         self.embedding_model = str(self.config.models.embedding_model)
@@ -297,6 +302,22 @@ def ask_aqa_model(self, question):
             response = self.ask_aqa_model_using_local_vector_store(question)
         return response
 
+    # Retrieve and return chunks that are most relevant to the input question.
+    def retrieve_chunks_from_corpus(self, question, corpus_name: str = "None"):
+        if corpus_name == "None":
+            corpus_name = self.corpus_name
+        user_query = question
+        results_count = 5
+        # Quick fix: This was needed to allow the method to be called
+        # even when the model is not set to `models/aqa`.
+        retriever_service_client = glm.RetrieverServiceClient()
+        # Make the request
+        request = glm.QueryCorpusRequest(
+            name=corpus_name, query=user_query, results_count=results_count
+        )
+        query_corpus_response = retriever_service_client.query_corpus(request)
+        return query_corpus_response
+
     # Use this method for asking a Gemini content model for fact-checking
     def ask_content_model_to_fact_check(self, context, prev_response):
         question = self.config.conditions.fact_check_question + "\n\nText: "
diff --git a/examples/gemini/python/docs-agent/docs_agent/benchmarks/__init__.py b/examples/gemini/python/docs-agent/docs_agent/benchmarks/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/__init__.py b/examples/gemini/python/docs-agent/docs_agent/interfaces/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/chatui.py b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/chatui.py
index 4d41a7abf..730353687 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/chatui.py
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/chatui.py
@@ -41,7 +41,13 @@
 from docs_agent.storage.chroma import Format
 from docs_agent.agents.docs_agent import DocsAgent
 
-from docs_agent.memory.logging import log_question, log_like
+from docs_agent.memory.logging import (
+    log_question,
+    log_debug_info_to_file,
+    log_feedback_to_file,
+    log_like,
+    log_dislike,
+)
 
 
 # This is used to define the app blueprint using a productConfig
@@ -56,7 +62,9 @@ def construct_blueprint(
             # A local Chroma DB is not needed for the Semantic Retreiver only mode.
             docs_agent = DocsAgent(config=product_config, init_chroma=False)
     elif product_config.db_type == "none":
-        docs_agent = DocsAgent(config=product_config, init_chroma=False, init_semantic=False)
+        docs_agent = DocsAgent(
+            config=product_config, init_chroma=False, init_semantic=False
+        )
     else:
         docs_agent = DocsAgent(config=product_config, init_chroma=True)
     logging.info(
@@ -119,9 +127,17 @@ def api():
     def like():
         if request.method == "POST":
             json_data = json.loads(request.data)
+            uuid_found = str(json_data.get("uuid")).strip()
             is_like = json_data.get("like")
-            uuid_found = json_data.get("uuid")
-            log_like(is_like, str(uuid_found).strip())
+            if is_like != None:
+                log_like(is_like, uuid_found)
+            is_dislike = json_data.get("dislike")
+            if is_dislike != None:
+                log_dislike(is_dislike, uuid_found)
+            # Check if the server has the `debugs` directory.
+            debug_dir = "logs/debugs"
+            if os.path.exists(debug_dir):
+                log_feedback_to_file(uuid_found, is_like, is_dislike)
             return "OK"
         else:
             return redirect(url_for(redirect_index))
@@ -191,9 +207,7 @@ def feedback():
             date_format = "%m%d%Y-%H%M%S"
             date = datetime.now(tz=pytz.utc)
             date = date.astimezone(pytz.timezone("US/Pacific"))
-            print(
-                "[" + date.strftime(date_format) + "] A user has submitted feedback."
-            )
+            print("[" + date.strftime(date_format) + "] A user has submitted feedback.")
             print("Submitted by: " + user_id + "\n")
             print("# " + question.strip() + "\n")
             print("## Response\n")
@@ -244,11 +258,20 @@ def question(ask):
         else:
             return redirect(url_for(redirect_index))
 
-    # Render the log view page
+    # Render the log view page.
     @bp.route("/logs", methods=["GET", "POST"])
     def logs():
         return show_logs(agent=docs_agent)
 
+    # Render the debug view page.
+    @bp.route("/debugs/<filename>", methods=["GET", "POST"])
+    def debugs(filename):
+        if request.method == "GET":
+            filename = urllib.parse.unquote_plus(filename)
+            return show_debug_info(agent=docs_agent, filename=filename)
+        else:
+            return redirect(url_for(redirect_index))
+
     return bp
 
 
@@ -279,12 +302,12 @@ def ask_model(question, agent, template: str = "chatui/index.html"):
     aqa_response_in_html = ""
 
     # Debugging feature: Do not log this question if it ends with `?do_not_log`.
-    can_be_logged = "True"
+    can_be_logged = True
     question_match = re.search(r"^(.*)\?do_not_log$", question)
     if question_match:
         # Update the question to remove `do_not_log`.
         question = question_match[1] + "?"
-        can_be_logged = "False"
+        can_be_logged = False
 
     # Retrieve context and ask the question.
     if "gemini" in docs_agent.config.models.language_model:
@@ -432,24 +455,44 @@ def ask_model(question, agent, template: str = "chatui/index.html"):
     else:
         summary_response = ""
         log_lines = f"{response}"
- 
+
     ### LOG THIS REQUEST.
-    if docs_agent.config.enable_logs_to_markdown == "True":
-        log_question(
-            new_uuid,
-            question,
-            log_lines,
-            probability,
-            save=can_be_logged,
-            logs_to_markdown="True",
-        )
-    else:
-        log_question(new_uuid, question, log_lines, probability, save=can_be_logged)
+    if can_be_logged:
+        if docs_agent.config.enable_logs_to_markdown == "True":
+            log_question(
+                new_uuid,
+                question,
+                log_lines,
+                probability,
+                save=True,
+                logs_to_markdown="True",
+            )
+        else:
+            log_question(new_uuid, question, log_lines, probability, save=True)
+        # Log debug information.
+
+        if docs_agent.config.enable_logs_for_debugging == "True":
+            top_source_url = search_result[0].section.url
+            source_urls = ""
+            index = 1
+            for result in search_result:
+                source_urls += "[" + str(index) + "]: " + str(result.section.url) + "\n"
+                index += 1
+            log_debug_info_to_file(
+                uid=new_uuid,
+                user_question=question,
+                response=log_lines,
+                context=final_context,
+                top_source_url=top_source_url,
+                source_urls=source_urls,
+                probability=probability,
+                server_url=server_url,
+            )
 
     ### Check the feedback mode in the `config.yaml` file.
     feedback_mode = "feedback"
-    if hasattr(docs_agent.config, "feedback_mode") and  docs_agent.config.feedback_mode == "rewrite":
-        feedback_mode = "rewrite"
+    if hasattr(docs_agent.config, "feedback_mode"):
+        feedback_mode = str(docs_agent.config.feedback_mode)
 
     return render_template(
         template,
@@ -519,3 +562,24 @@ def show_logs(agent, template: str = "admin/logs.html"):
         logs=log_contents,
         answerable_logs=answerable_contents,
     )
+
+
+# Display a page showing debug information.
+def show_debug_info(agent, filename: str, template: str = "admin/debugs.html"):
+    docs_agent = agent
+    product = docs_agent.config.product_name
+    debug_dir = "logs/debugs"
+    debug_filename = f"{debug_dir}/{filename}"
+    debug_info = ""
+    if docs_agent.config.enable_logs_for_debugging == "True":
+        try:
+            if debug_filename.endswith("txt"):
+                with open(debug_filename, "r", encoding="utf-8") as file:
+                    debug_info = file.read()
+        except:
+            debug_info = "Cannot find or open this file."
+    return render_template(
+        template,
+        product=product,
+        debug_info=debug_info,
+    )
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui-1.5.css b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui-1.5.css
index a076ba1d5..4cdb442e3 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui-1.5.css
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui-1.5.css
@@ -247,12 +247,18 @@ body {
     cursor:pointer;
   }
 
-  .selected {
+  #like-button.selected {
     background-color: #1e6a9c;
     padding-top: 7px;
     padding-bottom: 7px;
   }
 
+  #dislike-button.selected {
+    background-color: #CF5C3F;
+    padding-top: 7px;
+    padding-bottom: 7px;
+  }
+
   .selected:hover {
     background-color: #0a619a;
     cursor:pointer;
@@ -329,6 +335,15 @@ body {
     cursor:pointer;
   }
 
+  #dislike-button {
+    border: 0;
+    color: #fff;
+    padding-left: 7px;
+    padding-right: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+  }
+
   #submit-button {
     border: 0;
     background: none;
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui.css b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui.css
index 8ee1fa684..7267c9b22 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui.css
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui.css
@@ -222,12 +222,18 @@ body {
     cursor:pointer;
   }
 
-  .selected {
+  #like-button.selected {
     background-color: #1e6a9c;
     padding-top: 7px;
     padding-bottom: 7px;
   }
 
+  #dislike-button.selected {
+    background-color: #CF5C3F;
+    padding-top: 7px;
+    padding-bottom: 7px;
+  }
+
   .selected:hover {
     background-color: #0a619a;
     cursor:pointer;
@@ -304,6 +310,15 @@ body {
     cursor:pointer;
   }
 
+  #dislike-button {
+    border: 0;
+    color: #fff;
+    padding-left: 7px;
+    padding-right: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+  }
+
   #submit-button {
     border: 0;
     background: none;
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-widget.css b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-widget.css
index 37e7e5947..9658ef6a7 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-widget.css
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-widget.css
@@ -41,7 +41,7 @@ body {
     font-weight: 500;
     font-size: 1.3em;
     margin-top: 0.1em;
-    margin-left: 0.1em;
+    margin-left: 1.0em;
     margin-bottom: 0.9em;
   }
 
@@ -238,12 +238,18 @@ body {
     cursor:pointer;
   }
 
-  .selected {
+  #like-button.selected {
     background-color: #1e6a9c;
     padding-top: 7px;
     padding-bottom: 7px;
   }
 
+  #dislike-button.selected {
+    background-color: #CF5C3F;
+    padding-top: 7px;
+    padding-bottom: 7px;
+  }
+
   .selected:hover {
     background-color: #0a619a;
     cursor:pointer;
@@ -326,6 +332,15 @@ body {
     cursor:pointer;
   }
 
+  #dislike-button {
+    border: 0;
+    color: #fff;
+    padding-left: 7px;
+    padding-right: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+  }
+
   #submit-button {
     border: 0;
     background: none;
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/javascript/app.js b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/javascript/app.js
index 6007e7f80..cd3e9aba9 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/javascript/app.js
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/javascript/app.js
@@ -88,6 +88,7 @@ if (feedbackButton != null){
 
 // Toggle the selected class on the `like this response` button.
 let likeButton = document.getElementById('like-button');
+let dislikeButton = document.getElementById('dislike-button');
 
 if (likeButton != null){
   likeButton.addEventListener('click',function (){
@@ -100,6 +101,13 @@ if (likeButton != null){
       if (uuidBox != null){
         uuid =  uuidBox.textContent;
       }
+      if (dislikeButton != null){
+        dislikeButton.classList.add("hidden");
+        if (dislikeButton.classList.contains("selected")) {
+          dislikeButton.classList.remove("selected");
+          dislikeButton.classList.add("notselected");
+	}
+      }
       let xhr = new XMLHttpRequest();
       // The value of `urlLike` is specified in the html template,
       // which is set by the Flask server.
@@ -118,6 +126,13 @@ if (likeButton != null){
       if (uuidBox != null){
         uuid =  uuidBox.textContent;
       }
+      if (dislikeButton != null){
+        dislikeButton.classList.remove("hidden");
+        if (dislikeButton.classList.contains("selected")) {
+          dislikeButton.classList.remove("selected");
+          dislikeButton.classList.add("notselected");
+	}
+      }
       let xhr = new XMLHttpRequest();
       // The value of `urlLike` is specified in the html template,
       // which is set by the Flask server.
@@ -131,6 +146,63 @@ if (likeButton != null){
   });
 }
 
+// Toggle the selected class on the `dislike` button.
+if (dislikeButton != null){
+  dislikeButton.addEventListener('click',function (){
+    if (dislikeButton.classList.contains("notselected")) {
+      this.classList.remove("notselected");
+      this.classList.add("selected");
+      this.value = "Disliked"
+      let uuidBox = document.getElementById('uuid-box');
+      let uuid = "Unknown";
+      if (uuidBox != null){
+        uuid =  uuidBox.textContent;
+      }
+      if (likeButton != null){
+        likeButton.classList.add("hidden");
+        if (likeButton.classList.contains("selected")) {
+          likeButton.classList.remove("selected");
+          likeButton.classList.add("notselected");
+	}
+      }
+      let xhr = new XMLHttpRequest();
+      // The value of `urlLike` is specified in the html template,
+      // which is set by the Flask server.
+      // See chatbot/templates/chatui/base.html
+      xhr.open("POST", urlLike, true);
+      xhr.setRequestHeader("Accept", "application/json");
+      xhr.setRequestHeader("Content-Type", "application/json");
+      let data = JSON.stringify({"dislike": true, "uuid": uuid});
+      xhr.send(data);
+    }else{
+      this.classList.remove("selected");
+      this.classList.add("notselected");
+      this.value = 'Dislike \uD83D\uDC4E';
+      let uuidBox = document.getElementById('uuid-box');
+      let uuid = "Unknown";
+      if (uuidBox != null){
+        uuid =  uuidBox.textContent;
+      }
+      if (likeButton != null){
+        likeButton.classList.remove("hidden");
+        if (likeButton.classList.contains("selected")) {
+          likeButton.classList.remove("selected");
+          likeButton.classList.add("notselected");
+	}
+      }
+      let xhr = new XMLHttpRequest();
+      // The value of `urlLike` is specified in the html template,
+      // which is set by the Flask server.
+      // See chatbot/templates/chatui/base.html
+      xhr.open("POST", urlLike, true);
+      xhr.setRequestHeader("Accept", "application/json");
+      xhr.setRequestHeader("Content-Type", "application/json");
+      let data = JSON.stringify({"dislike": false, "uuid": uuid});
+      xhr.send(data);
+    }
+  });
+}
+
 // Adjust the size of the `edit-text-area` textarea.
 let rewriteTextArea = document.getElementById('edit-text-area');
 
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/admin/debugs.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/admin/debugs.html
new file mode 100644
index 000000000..4a04c62e3
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/admin/debugs.html
@@ -0,0 +1,24 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <link rel="stylesheet" href="{{ url_for('static', filename='css/style-widget.css') }}">
+    <link rel="stylesheet" href="{{ url_for('static', filename='css/chatbox.css') }}">
+    <link rel="stylesheet" href="{{ url_for('static', filename='css/style-logs.css') }}">
+    <link rel="shortcut icon" href="{{ url_for('static', filename='images/favicon.png') }}">
+    <title>{{ product }} Docs Agent admin page</title>
+</head>
+<body>
+  {% if debug_info %}
+  <div id='widget-box'>
+    <div name="title"><h1>{{ product }} Docs Agent</h1></div>
+    <div id='chat-border'>
+      <div class="body-content">
+	<h2>Debug information<h2>
+	<pre class="log-pre">{{ debug_info }}</pre>
+      </div>
+    </div>
+  </div>
+  {% endif %}
+</body>
+</html>
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chat-widget/result.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chat-widget/result.html
index aa249e3eb..c0c4e9856 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chat-widget/result.html
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chat-widget/result.html
@@ -167,6 +167,25 @@ <h4>Rewrite:</h4>
 <div class="hidden" id="uuid-box">
   {{ uuid | safe }}
 </div>
+{% elif feedback_mode == "like_and_dislike" %}
+<div id="feedback-buttons-box">
+  <input type="button" class="notselected" value="Like this response &#128077;" id="like-button">
+  <input type="button" class="notselected" value="Disike &#128078;" id="dislike-button">
+  {% if aqa_response_in_html != "" %}
+  <span id="answerable-span">
+    (<b>Answerable probability</b>: {{"%.3f"|format(probability|float)}})
+  </span>
+  {% endif %}
+</div>
+<div class="hidden" id="uuid-box">
+  {{ uuid | safe }}
+</div>
+<div class="hidden" id="feedback-question-span">
+  {{ question | replace("+", " ") | replace("%3F", "?")}}
+</div>
+<div class="hidden" id="feedback-response-span">
+  {{ md_to_html(response) | safe }}
+</div>
 {% else %}
 <div id="feedback-buttons-box">
   <input type="button" value="Provide feedback" id="feedback-button">
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-1.5/result.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-1.5/result.html
index 3b17fafe9..3fca7dea5 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-1.5/result.html
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-1.5/result.html
@@ -160,6 +160,25 @@ <h4>Rewrite:</h4>
 <div class="hidden" id="uuid-box">
   {{ uuid | safe }}
 </div>
+{% elif feedback_mode == "like_and_dislike" %}
+<div id="feedback-buttons-box">
+  <input type="button" class="notselected" value="Like this response &#128077;" id="like-button">
+  <input type="button" class="notselected" value="Disike &#128078;" id="dislike-button">
+  {% if aqa_response_in_html != "" %}
+  <span id="answerable-span">
+    (<b>Answerable probability</b>: {{"%.3f"|format(probability|float)}})
+  </span>
+  {% endif %}
+</div>
+<div class="hidden" id="uuid-box">
+  {{ uuid | safe }}
+</div>
+<div class="hidden" id="feedback-question-span">
+  {{ question | replace("+", " ") | replace("%3F", "?")}}
+</div>
+<div class="hidden" id="feedback-response-span">
+  {{ md_to_html(response) | safe }}
+</div>
 {% else %}
 <div id="feedback-buttons-box">
   <input type="button" value="Provide feedback" id="feedback-button">
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui/result.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui/result.html
index e6df8b9b7..0999c7a55 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui/result.html
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui/result.html
@@ -137,6 +137,25 @@ <h4>Rewrite:</h4>
 <div class="hidden" id="uuid-box">
   {{ uuid | safe }}
 </div>
+{% elif feedback_mode == "like_and_dislike" %}
+<div id="feedback-buttons-box">
+  <input type="button" class="notselected" value="Like this response &#128077;" id="like-button">
+  <input type="button" class="notselected" value="Disike &#128078;" id="dislike-button">
+  {% if aqa_response_in_html != "" %}
+  <span id="answerable-span">
+    (<b>Answerable probability</b>: {{"%.3f"|format(probability|float)}})
+  </span>
+  {% endif %}
+</div>
+<div class="hidden" id="uuid-box">
+  {{ uuid | safe }}
+</div>
+<div class="hidden" id="feedback-question-span">
+  {{ question | replace("+", " ") | replace("%3F", "?")}}
+</div>
+<div class="hidden" id="feedback-response-span">
+  {{ md_to_html(response) | safe }}
+</div>
 {% else %}
 <div id="feedback-buttons-box">
   <input type="button" value="Provide feedback" id="feedback-button">
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/cli.py b/examples/gemini/python/docs-agent/docs_agent/interfaces/cli.py
index 2d76fe3a4..2acf992f1 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/cli.py
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/cli.py
@@ -28,6 +28,7 @@
     return_pure_dir,
     end_path_backslash,
     start_path_no_backslash,
+    resolve_path,
 )
 from docs_agent.preprocess import files_to_plain_text as chunker
 from docs_agent.preprocess import populate_vector_database as populate_script
@@ -36,10 +37,12 @@
 from docs_agent.interfaces import run_console as console
 from docs_agent.storage.google_semantic_retriever import SemanticRetriever
 from docs_agent.storage.chroma import ChromaEnhanced
+from docs_agent.memory.logging import write_logs_to_csv_file
 import socket
 import os
 import string
 import re
+import time
 
 
 def common_options(func):
@@ -209,12 +212,18 @@ def show_config(config_file: typing.Optional[str], product: list[str] = [""]):
 
 @cli_client.command(name="tellme")
 @click.argument("words", nargs=-1)
+@click.option(
+    "--model",
+    help="Specify a model to use. Overrides the model (language_model) for all loaded product configurations.",
+    default=None,
+    multiple=False,
+)
 @common_options
-def tellme(words, config_file: typing.Optional[str], product: list[str] = [""]):
+def tellme(words, config_file: typing.Optional[str], product: list[str] = [""], model: typing.Optional[str] = None):
     """Answer a question related to the product."""
     # Loads configurations from common options
     loaded_config, product_configs = return_config_and_product(
-        config_file=config_file, product=product
+        config_file=config_file, product=product, model=model
     )
     if product == ():
         # Extracts the first product by default
@@ -229,70 +238,231 @@ def tellme(words, config_file: typing.Optional[str], product: list[str] = [""]):
 
 @cli_client.command()
 @click.argument("words", nargs=-1)
+@click.option(
+    "--model",
+    help="Specify a model to use. Overrides the model (language_model) for all loaded product configurations.",
+    default=None,
+    multiple=False,
+)
 @click.option(
     "--file",
     type=click.Path(),
-    help="Only available with Gemini 1.5 Pro. Specify a file with which a task is performed.",
+    help="Specify a file to be included as context.",
+)
+@click.option(
+    "--path",
+    type=click.Path(),
+    help="Specify a path where the request will be applied to all files found in the directory.",
+)
+@click.option(
+    "--file_ext",
+    help="Works with --path. Specify the file type to be selected. By default it is set to None.",
 )
 @click.option(
     "--rag",
     is_flag=True,
-    help="Only works with --file. Augments the context input with content from your configured RAG system.",
+    help="Add entries found in the knowledge database as context.",
+)
+@click.option(
+    "--yaml",
+    is_flag=True,
+    help="Works with --path. Store the output as a responses.yaml file.",
+)
+@click.option(
+    "--new",
+    is_flag=True,
+    help="Works with --file. Clear the previous responses buffer.",
+)
+@click.option(
+    "--cont",
+    is_flag=True,
+    help="Use the previous responses buffer as context.",
 )
 @common_options
 def helpme(
     words,
     config_file: typing.Optional[str],
     file: typing.Optional[str] = None,
+    path: typing.Optional[str] = None,
+    file_ext: typing.Optional[str] = None,
     rag: bool = False,
+    yaml: bool = False,
+    new: bool = False,
+    cont: bool = False,
     product: list[str] = [""],
+    model: typing.Optional[str] = None,
 ):
-    """(Experimental) Ask AI to perform a task using console output.
-    Use --file to perform a task on a specific file."""
+    """Ask the AI model to perform a task from the terminal."""
     # Loads configurations from common options
     loaded_config, product_configs = return_config_and_product(
-        config_file=config_file, product=product
+        config_file=config_file, product=product, model=model
     )
     if product == ():
         # Extracts the first product by default
         product_config = ConfigFile(products=[product_configs.products[0]])
     else:
         product_config = product_configs
+
+    # Get the language model.
+    this_model = product_config.products[0].models.language_model
+    # This feature is only available to the Gemini Pro models (not AQA).
+    if not this_model.startswith("models/gemini"):
+        click.echo(f"File mode is not supported with this model: {this_model}")
+        exit(1)
+
+    # Get the question string.
     question = ""
     for word in words:
         question += word + " "
     question = question.strip()
-    if file and file != "None":
-        # This feature is only available in gemini 1.5 pro (large context)
-        if product_config.products[0].models.language_model.startswith(
-            "models/gemini-1.5-pro"
-        ):
-            try:
-                this_file = os.path.realpath(os.path.join(os.getcwd(), file))
-                with open(this_file, "r", encoding="utf-8") as auto:
-                    # Read the input content
-                    content = auto.read()
-                    auto.close()
-                final_file = f"THE CONTENT OF THE FILE {file} BELOW:\n\n" + content
-                console.ask_model_with_file(
-                    question.strip(), product_config, file=final_file, rag=rag
-                )
-            except FileNotFoundError:
-                click.echo(f"File not found: {file}")
+
+    # Set the filename for recording exchanges with the Gemini models.
+    history_file = "/tmp/docs_agent_responses"
+
+    # 4 different mode: Terminal output (default), All files, Single file,
+    # and Previous exchanges
+    helpme_mode = "TERMINAL_OUTPUT"
+    if path:
+        helpme_mode = "ALL_FILES"
+    elif file and file != "None":
+        helpme_mode = "SINGLE_FILE"
+    elif cont:
+        helpme_mode = "PREVIOUS_EXCHANGES"
+
+    if helpme_mode == "PREVIOUS_EXCHANGES":
+        # Continue mode, which uses the previous exchangs as the main context.
+        this_output = console.ask_model_with_file(
+            question.strip(),
+            product_config,
+            context_file=history_file,
+            rag=rag,
+            return_output=True,
+        )
+        print()
+        print(f"{this_output}")
+        print()
+        with open(history_file, "a", encoding="utf-8") as out_file:
+            out_file.write(f"QUESTION: {question}\n\n")
+            out_file.write(f"RESPONSE:\n\n{this_output}\n")
+            out_file.close()
+    elif helpme_mode == "SINGLE_FILE":
+        # Single file mode.
+        this_file = os.path.realpath(os.path.join(os.getcwd(), file))
+        this_output = ""
+        if cont:
+            # if the `--cont` flag is set, inlcude the previous exchanges as additional context.
+            this_output = console.ask_model_with_file(
+                question.strip(),
+                product_config,
+                file=this_file,
+                context_file=history_file,
+                rag=rag,
+                return_output=True,
+            )
         else:
-            click.echo(
-                f"File mode is not supported with this model: {product_config.products[0].models.language_model}"
+            # By default, do not inlcude any additional context.
+            this_output = console.ask_model_with_file(
+                question.strip(),
+                product_config,
+                file=this_file,
+                rag=rag,
+                return_output=True,
             )
+        print()
+        print(f"{this_output}")
+        print()
+        # Read the file content to be included in the history file.
+        file_content = ""
+        with open(this_file, "r", encoding="utf-8") as target_file:
+            file_content = target_file.read()
+            target_file.close()
+        # If the `--new` flag is set, overwrite the history file.
+        write_mode = "a"
+        if new:
+            write_mode = "w"
+        # Record this exchange in the history file.
+        with open(history_file, write_mode, encoding="utf-8") as out_file:
+            out_file.write(f"QUESTION: {question}\n\n")
+            out_file.write(f"FILE NAME: {file}\n")
+            out_file.write(f"FILE CONTENT:\n\n{file_content}\n")
+            out_file.write(f"RESPONSE:\n\n{this_output}\n")
+            out_file.close()
+    elif helpme_mode == "ALL_FILES":
+        # All files mode, which makes the request to all files in the path.
+        # Set the `file_type` variable for display only.
+        file_type = "." + str(file_ext)
+        if file_ext == None:
+            file_type = "All types"
+
+        # Ask the user to confirm.
+        if click.confirm(
+            f"Making a request to all files found in the path below:\n"
+            + f"Question: {question}\nPath:  {path}\nFile type: {file_type}\n"
+            + f"Do you want to continue?",
+            abort=True,
+        ):
+            print()
+            out_buffer = ""
+            for root, dirs, files in os.walk(resolve_path(path)):
+                for file in files:
+                    file_path = os.path.realpath(os.path.join(root, file))
+                    if file_ext == None:
+                        # Apply to all files.
+                        print(f"# File: {file_path}")
+                        this_output = console.ask_model_with_file(
+                            question,
+                            product_config,
+                            file=file_path,
+                            rag=rag,
+                            return_output=True,
+                        )
+                        this_output = this_output.strip()
+                        print()
+                        print(f"{this_output}")
+                        print()
+                        if yaml is True:
+                            out_buffer += (
+                                f"  - question: {question}\n"
+                                + f"    response: {this_output}\n"
+                                + f"    file: {file_path}\n"
+                            )
+                        time.sleep(2)
+                    elif file.endswith(file_ext):
+                        print(f"# File: {file_path}")
+                        this_output = console.ask_model_with_file(
+                            question,
+                            product_config,
+                            file=file_path,
+                            rag=rag,
+                            return_output=True,
+                        )
+                        this_output = this_output.strip()
+                        print()
+                        print(f"{this_output}")
+                        print()
+                        if yaml is True:
+                            out_buffer += (
+                                f"  - question: {question}\n"
+                                + f"    response: {this_output}\n"
+                                + f"    file: {file_path}\n"
+                            )
+                        time.sleep(2)
+
+            if yaml is True:
+                output_filename = "./responses.yaml"
+                with open(output_filename, "w", encoding="utf-8") as out_file:
+                    out_file.write("benchmarks:\n")
+                    out_file.write(out_buffer)
+                    out_file.close()
     else:
+        # Terminal output mode, which reads the terminal ouput as context.
         terminal_output = ""
         # Set the default filename created from the `script` command.
         file_path = "/tmp/docs_agent_console_input"
         # Set the maximum number of lines to read from the terminal.
         lines_limit = -150
-        if product_config.products[0].models.language_model.startswith(
-            "models/gemini-1.5-pro"
-        ):
-            # Read, at the most, 5000 lines printed from the latest command ran on the terminal.
+        # For the new 1.5 pro model, increase the limit to 5000 lines.
+        if this_model.startswith("models/gemini-1.5-pro"):
             lines_limit = -5000
         try:
             with open(file_path, "r", encoding="utf-8") as file:
@@ -308,16 +478,22 @@ def helpme(
         except:
             terminal_output = "No console output is provided."
         context = "THE FOLLOWING IS MY CONSOLE OUTPUT:\n\n" + terminal_output
-        console.ask_model_for_help(question.strip(), context, product_config)
+        console.ask_model_for_help(question, context, product_config)
 
 
 @cli_admin.command()
+@click.option(
+    "--model",
+    help="Specify a model to use. Overrides the model (language_model) for all loaded product configurations.",
+    default=None,
+    multiple=False,
+)
 @common_options
-def benchmark(config_file: typing.Optional[str], product: list[str] = [""]):
+def benchmark(config_file: typing.Optional[str], product: list[str] = [""],  model: typing.Optional[str] = None):
     """Run the Docs Agent benchmark test."""
     # Loads configurations from common options
     loaded_config, product_config = return_config_and_product(
-        config_file=config_file, product=product
+        config_file=config_file, product=product, model=model
     )
     benchmarks.run_benchmarks()
 
@@ -538,5 +714,34 @@ def backup_chroma(
         click.echo(f"Can't backup chroma database specified: {input_chroma}")
 
 
+@cli_admin.command()
+@click.option("--date", default="None")
+@common_options
+def write_logs_to_csv(
+    date: typing.Optional[str],
+    config_file: typing.Optional[str],
+    product: list[str] = [""],
+):
+    """Write captured debug information to a CSV file."""
+    # Loads configurations from common options
+    loaded_config, product_config = return_config_and_product(
+        config_file=config_file, product=product
+    )
+    output_filename = "debug-info-all.csv"
+    if date != "None":
+        output_filename = "debug-info-" + str(date) + ".csv"
+        click.echo(
+            f"Writing all debug logs from {date} to the logs/{output_filename} file:\n"
+        )
+    else:
+        click.echo(f"Writing all debug logs to the logs/{output_filename} file:\n")
+    # Write the target debug logs to a CSV file.
+    write_logs_to_csv_file(log_date=date)
+    # Print the content of the CSV file.
+    with open("./logs/" + output_filename, "r") as f:
+        print(f.read())
+        f.close()
+
+
 if __name__ == "__main__":
     cli()
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/run_console.py b/examples/gemini/python/docs-agent/docs_agent/interfaces/run_console.py
index 0b11d9bc5..84b3ca775 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/run_console.py
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/run_console.py
@@ -218,187 +218,146 @@ def ask_model_with_file(
     question: str,
     product_configs: ConfigFile,
     file: typing.Optional[str] = None,
+    context_file: typing.Optional[str] = None,
     rag: bool = False,
+    return_output: bool = False,
 ):
     # Initialize Rich console
     ai_console = Console(width=160)
     full_prompt = ""
     final_context = ""
-    results_num = 5
-    # Initialize Docs Agent
+    response = ""
+
+    # Get the content of the target file.
+    file_content = ""
+    if file != None:
+        try:
+            with open(file, "r", encoding="utf-8") as auto:
+                content = auto.read()
+                auto.close()
+            file_content = f"\nTHE CONTENT BELOW IS FROM THE FILE {file}:\n\n" + content
+        except:
+            print(f"Cannot open the file {file}")
+            exit(1)
+
+    # Get the content of the context file.
+    context_file_content = ""
+    if context_file != None:
+        try:
+            with open(context_file, "r", encoding="utf-8") as auto:
+                content = auto.read()
+                auto.close()
+            context_file_content = (
+                f"\nTHE CONTENT BELOW IS FROM THE PREVIOUS EXCHANGES WITH GEMINI:\n\n"
+                + content
+            )
+            file_content = context_file_content + "\n\n" + file_content
+        except:
+            print(f"Cannot open the context file {file}")
+            exit(1)
+
+    # Use the first product by default.
+    product = product_configs.products[0]
+    language_model = product.models.language_model
     with Progress(transient=True) as progress:
-        search_results = []
-        responses = []
-        links = []
         task_docs_agent = progress.add_task(
             "[turquoise4 bold]Starting Docs Agent ", total=None, refresh=True
         )
-        for product in product_configs.products:
-            if "gemini" in product.models.language_model:
-                if rag and product.db_type == "chroma":
-                    docs_agent = DocsAgent(config=product, init_chroma=True)
-                else:
-                    docs_agent = DocsAgent(config=product, init_chroma=False)
+        if rag:
+            # RAG specified. Retrieve additional context from a database.
+            if product.db_type == "chroma":
+                # Use a local Chroma database.
+                # Initialize Docs Agent.
+                docs_agent = DocsAgent(
+                    config=product, init_chroma=True, init_semantic=False
+                )
+                # Get the Chroma collection name.
+                collection = docs_agent.return_chroma_collection()
+                # Set the progress bar.
+                label = f"[turquoise4 bold]Asking Gemini (model: {language_model}, source: {collection}) "
                 progress.update(
-                    task_docs_agent,
-                    description=f"[turquoise4 bold]Asking Gemini (model: {product.models.language_model}, source: {docs_agent.return_chroma_collection()}) ",
-                    total=None,
+                    task_docs_agent, description=label, total=None, refresh=True
                 )
-                if docs_agent.config.docs_agent_config == "experimental":
-                    results_num = 10
-                    new_question_count = 5
-                else:
-                    results_num = 5
-                    new_question_count = 5
-                if file is not None:
-                    results_num = 15
-                    if rag:
-                        (
-                            search_result,
-                            final_context,
-                        ) = docs_agent.query_vector_store_to_build(
-                            question=question,
-                            token_limit=500000,
-                            results_num=results_num,
-                            max_sources=results_num,
-                        )
-                        final_context = (
-                            "This is the file: \n" + str(file) + "\n\n" + final_context
-                        )
-                    else:
-                        search_result = []
-                        link = ""
-                        final_context = "This is the file: \n" + str(file) + "\n"
-                else:
-                    # Issue if max_sources > results_num, so leave the same for now
-                    (
-                        search_result,
-                        final_context,
-                    ) = docs_agent.query_vector_store_to_build(
-                        question=question,
-                        token_limit=30000,
-                        results_num=results_num,
-                        max_sources=results_num,
-                    )
+                # Retrieve context from the local Chroma database.
                 (
-                    response,
-                    full_prompt,
-                ) = docs_agent.ask_content_model_with_context_prompt(
-                    context=final_context, question=question
+                    search_result,
+                    returned_context,
+                ) = docs_agent.query_vector_store_to_build(
+                    question=question,
+                    token_limit=500000,
+                    results_num=5,
+                    max_sources=5,
                 )
-                if len(search_result) >= 1:
-                    if search_result[0].section.url == "":
-                        link = str(search_result[0].section)
-                    else:
-                        link = search_result[0].section.url
-                search_results.append(search_result)
-                responses.append(response)
-                links.append(link)
-            elif "aqa" in product.models.language_model:
-                if product.db_type == "google_semantic_retriever":
-                    docs_agent = DocsAgent(config=product, init_chroma=False)
-                    label = f"[turquoise4 bold]Asking Gemini (model: {product.models.language_model}, "
-                    corpus_name = ""
-                    link = ""
-                    for db_config in product.db_configs:
-                        if db_config.db_type == "google_semantic_retriever":
-                            corpus_name = db_config.corpus_name
-                    if corpus_name != "":
-                        label += "source: " + corpus_name + ") "
-                    progress.update(
-                        task_docs_agent, description=label, total=None, refresh=True
-                    )
-                    (response, search_result) = docs_agent.ask_aqa_model_using_corpora(
-                        question=question
-                    )
-                    if len(search_result) >= 1:
-                        if search_result[0].section.url == "":
-                            link = str(search_result[0].section)
-                        else:
-                            link = search_result[0].section.url
-                    search_results.append(search_result)
-                    responses.append(response)
-                    link = ""
-                    links.append(link)
-                elif product.db_type == "chroma":
-                    docs_agent = DocsAgent(config=product, init_chroma=True)
-                    progress.update(
-                        task_docs_agent,
-                        description=f"[turquoise4 bold]Asking Gemini (model: {product.models.language_model}, source: {docs_agent.return_chroma_collection()}) ",
-                        total=None,
-                    )
-                    (
-                        response,
-                        search_result,
-                    ) = docs_agent.ask_aqa_model_using_local_vector_store(
-                        question=question, results_num=results_num
-                    )
-                    if len(search_result) >= 1:
-                        if search_result[0].section.url == "":
-                            link = str(search_result[0].section)
-                        else:
-                            link = search_result[0].section.url
-                    search_results.append(search_result)
-                    responses.append(response)
-                    links.append(link)
-                else:
-                    logging.error(f"Unknown db_type: {product.db_type}")
-        final_search = []
-        final_responses = []
-        final_response_md = ""
-        final_links = []
-        count = 0
-        # Prune the generated answers as needed
-        for item in search_results:
-            # Gemini-pro + chroma will give distance instead of probability
-            # if (item[0].probability != 0):
-            final_search.append(search_results[count])
-            final_responses.append(responses[count])
-            final_response_md += responses[count] + "\n"
-            final_links.append(links[count])
-            count += 1
-        count = 0
-        synthesize = False
-        synthesize_product = None
-        # Currently only triggers from a gemini entry into the provided products
-        for product in product_configs.products:
-            if "gemini" in product.models.language_model:
-                synthesize_product = product
-        if synthesize and not (synthesize_product == None):
-            docs_agent = DocsAgent(config=synthesize_product, init_chroma=False)
+                context_from_database = ""
+                chunk_count = 0
+                for item in search_result:
+                    context_from_database += item.section.content
+                    context_from_database += f"\nReference [{chunk_count}]\n\n"
+                    chunk_count += 1
+                # Construct the final context for the question.
+                final_context = (
+                    "\nTHE CONTENT BELOW IS RETRIEVED FROM THE LOCAL DATABASE:\n\n"
+                    + context_from_database.strip()
+                    + "\n\n"
+                    + str(file_content)
+                )
+            elif product.db_type == "google_semantic_retriever":
+                # Use an online corpus from the Semantic Retrieval API.
+                # Initialize Docs Agent.
+                docs_agent = DocsAgent(
+                    config=product, init_chroma=False, init_semantic=True
+                )
+                # Get the corpus name.
+                corpus_name = ""
+                for db_config in product.db_configs:
+                    if db_config.db_type == "google_semantic_retriever":
+                        corpus_name = db_config.corpus_name
+                # Set the progress bar.
+                label = f"[turquoise4 bold]Asking Gemini (model: {language_model}, source: {corpus_name}) "
+                progress.update(
+                    task_docs_agent, description=label, total=None, refresh=True
+                )
+                # Retrieve context from the online corpus.
+                context_chunks = docs_agent.retrieve_chunks_from_corpus(
+                    question, corpus_name=str(corpus_name)
+                )
+                context_from_corpus = ""
+                chunk_count = 0
+                for chunk in context_chunks.relevant_chunks:
+                    context_from_corpus += chunk.chunk.data.string_value
+                    context_from_corpus += f"\nReference [{chunk_count}]\n\n"
+                    chunk_count += 1
+                # Construct the final context for the question.
+                final_context = (
+                    "\nTHE CONTENT BELOW IS RETRIEVED FROM THE ONLINE DATABASE:\n\n"
+                    + context_from_corpus.strip()
+                    + "\n\n"
+                    + str(file_content)
+                )
+            else:
+                logging.error(f"Unknown db_type: {product.db_type}")
+                exit(1)
+        else:
+            # No RAG specified. No additional context to be retrieved from a database.
+            docs_agent = DocsAgent(
+                config=product, init_chroma=False, init_semantic=False
+            )
             progress.update(
                 task_docs_agent,
-                description=f"[turquoise4 bold]Asking {docs_agent.context_model} to synthesize a response",
+                description=f"[turquoise4 bold]Asking Gemini (model: {language_model}) ",
                 total=None,
             )
-            new_question = f"Help me synthesize the context above into a cohesive response to help me answer my original question. Only use content from the provided context. My original question: {question}"
-            (
-                good_response,
-                new_prompt,
-            ) = docs_agent.ask_content_model_with_context_prompt(
-                context=final_response_md, question=new_question, prompt=""
-            )
-            progress.update(task_docs_agent, visible=False, refresh=True)
-            # Final printing to console
-            ai_console.print()
-            ai_console.print(Markdown(good_response))
-        else:
-            # This returns the responses as is for each agent interaction
-            progress.update(task_docs_agent, visible=False, refresh=True)
-            ai_console.print()
-            ai_console.print(Markdown(final_response_md))
-        # Print results
-        for item in final_search:
-            count += 1
-        count = 0
+            final_context = file_content
+        # print(f"Context:\n{final_context}")
+        # print(f"Question:\n{question}")
+        # Ask Gemini with the question and final context.
+        (
+            response,
+            full_prompt,
+        ) = docs_agent.ask_content_model_with_context_prompt(
+            context=final_context, question=question
+        )
+        if return_output:
+            return str(response.strip())
         ai_console.print()
-        md_links = ""
-        for item in final_links:
-            if isinstance(item, str):
-                if not item.startswith("UUID"):
-                    md_links += f"\n* [{item}]({item})\n"
-                    count += 1
-        # Avoid printing links if first link is blank
-        if final_links[0] != "":
-            ai_console.print(Markdown("To verify this information, see:\n"))
-            ai_console.print(Markdown(md_links))
+        ai_console.print(Markdown(response.strip()))
diff --git a/examples/gemini/python/docs-agent/docs_agent/memory/__init__.py b/examples/gemini/python/docs-agent/docs_agent/memory/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/examples/gemini/python/docs-agent/docs_agent/memory/logging.py b/examples/gemini/python/docs-agent/docs_agent/memory/logging.py
index beb1280bc..fc443c169 100644
--- a/examples/gemini/python/docs-agent/docs_agent/memory/logging.py
+++ b/examples/gemini/python/docs-agent/docs_agent/memory/logging.py
@@ -17,6 +17,8 @@
 from datetime import datetime
 import pytz
 import os
+import re
+from uuid import UUID
 
 """Module to log interactions with the chatbot"""
 
@@ -49,13 +51,185 @@ def log_answerable_probability(user_question: str, probability: str):
         log_file.close()
 
 
+# Save a detailed record of a question and response pair as a file for debugging.
+def log_debug_info_to_file(
+    uid: UUID,
+    user_question: str,
+    response: str,
+    context: str,
+    top_source_url: str,
+    source_urls: str,
+    probability: str = "None",
+    server_url: str = "None",
+):
+    # Compose a filename
+    date = datetime.now(tz=pytz.utc)
+    date = date.astimezone(pytz.timezone("US/Pacific"))
+    date_formatted = str(date.strftime("%Y-%m-%d-%H-%M-%S"))
+    question_formatted = str(user_question).lower().replace(" ", "-").replace("?", "")
+    filename = date_formatted + "-" + question_formatted + "-" + str(uid) + ".txt"
+    # Set the directory location.
+    debug_dir = "./logs/debugs"
+    debug_filename = f"{debug_dir}/{filename}"
+    if not os.path.exists(debug_dir):
+        os.makedirs(debug_dir)
+    with open(debug_filename, "w", encoding="utf-8") as debug_file:
+        debug_file.write("UID: " + str(uid) + "\n")
+        debug_file.write("DATE: " + str(date) + "\n")
+        debug_file.write("SERVER URL: " + server_url.strip() + "\n")
+        debug_file.write("\n")
+        debug_file.write("TOP SOURCE URL: " + top_source_url.strip() + "\n")
+        debug_file.write("ANSWERABLE PROBABILITY: " + str(probability) + "\n")
+        debug_file.write("\n")
+        debug_file.write("QUESTION: " + user_question.strip() + "\n\n")
+        debug_file.write("RESPONSE:\n\n")
+        debug_file.write(response.strip() + "\n\n")
+        debug_file.write("CONTEXT:\n\n")
+        debug_file.write(context.strip() + "\n\n")
+        debug_file.write("SOURCE URLS:\n\n")
+        debug_file.write(source_urls.strip() + "\n\n")
+        debug_file.close()
+
+
+# Save the like and dislike interactions to the file for debugging.
+def log_feedback_to_file(uid: str, is_like, is_dislike):
+    # Search the debugs directory.
+    debug_dir = "./logs/debugs"
+    target_string = str(uid) + ".txt"
+    debug_filename = ""
+    for root, dirs, files in os.walk(debug_dir):
+        for file in files:
+            if file.endswith(target_string):
+                debug_filename = f"{debug_dir}/{file}"
+    if debug_filename != "":
+        with open(debug_filename, "a", encoding="utf-8") as debug_file:
+            if is_like != None:
+                debug_file.write("LIKE: " + str(is_like) + "\n")
+            if is_dislike != None:
+                debug_file.write("DISLIKE: " + str(is_dislike) + "\n")
+            debug_file.close()
+
+
+# Write captured debug logs into a CSV file.
+def write_logs_to_csv_file(log_date: str = "None"):
+    # Compose the output CSV filename.
+    output_filename = "debug-info-all.csv"
+    if log_date != "None":
+        output_filename = "debug-info-" + str(log_date) + ".csv"
+    # Write a header for this CSV file.
+    log_dir = "./logs"
+    out_csv_filename = log_dir + "/" + output_filename
+    line = f"DATE, UID, QUESTION, PROBABILITY, TOP SOURCE URL, DEBUG LINK, FEEDBACK"
+    with open(out_csv_filename, "w", encoding="utf-8") as csv_file:
+        csv_file.write(line + "\n")
+        csv_file.close()
+    # Search the debugs directory.
+    debug_dir = "./logs/debugs"
+    debug_filename = ""
+    for root, dirs, files in os.walk(debug_dir):
+        for file in files:
+            # Read all files if date is "None" else read files from the input date only.
+            ok_to_read = False
+            if file.endswith("txt"):
+                ok_to_read = True
+            if log_date != "None":
+                if file.startswith(log_date):
+                    ok_to_read = True
+                else:
+                    ok_to_read = False
+            if ok_to_read:
+                debug_filename = f"{debug_dir}/{file}"
+                debug_record = ""
+                # Open and read this debug information file.
+                with open(debug_filename, "r", encoding="utf-8") as debug_file:
+                    debug_record = debug_file.readlines()
+                    debug_file.close()
+                uid = ""
+                date = ""
+                server_url = "None"
+                top_source_url = ""
+                probability = ""
+                question = ""
+                like = "None"
+                dislike = "None"
+                filename = str(file)
+                # Scan the lines from thi debug info and extract fields.
+                for line in debug_record:
+                    match_uid = re.search(r"^UID:\s+(.*)$", line)
+                    match_date = re.search(r"^DATE:\s+(.*)$", line)
+                    match_server_url = re.search(r"^SERVER URL:\s+(.*)$", line)
+                    match_top_url = re.search(r"^TOP SOURCE URL:\s+(.*)$", line)
+                    match_prob = re.search(r"^ANSWERABLE PROBABILITY:\s+(.*)$", line)
+                    match_question = re.search(r"^QUESTION:\s+(.*)$", line)
+                    match_like = re.search(r"^LIKE:\s+(.*)$", line)
+                    match_dislike = re.search(r"^DISLIKE:\s+(.*)$", line)
+                    # Extract fields.
+                    if match_uid:
+                        uid = match_uid.group(1)
+                    elif match_date:
+                        date = match_date.group(1)
+                    elif match_server_url:
+                        server_url = match_server_url.group(1)
+                    elif match_top_url:
+                        top_source_url = match_top_url.group(1)
+                    elif match_prob:
+                        probability = match_prob.group(1)
+                    elif match_question:
+                        question = match_question.group(1)
+                    elif match_like:
+                        like = match_like.group(1)
+                    elif match_dislike:
+                        dislike = match_dislike.group(1)
+                # Write a short version of this debug information to the CSV file.
+                debug_file_link = filename
+                if server_url != "None":
+                    debug_file_link = server_url + "debugs/" + filename
+                feedback = "None"
+                if like == "True":
+                    feedback = "Like"
+                elif dislike == "True":
+                    feedback = "Dislike"
+                log_debug_info_to_csv_file(
+                    output_filename=output_filename,
+                    date=date,
+                    uid=uid,
+                    question=question,
+                    probability=probability,
+                    top_source_url=top_source_url,
+                    debug_file_link=debug_file_link,
+                    feedback=feedback,
+                )
+
+
+# Save the short version of debug information into a CSV file.
+def log_debug_info_to_csv_file(
+    output_filename: str,
+    date: str,
+    uid: str,
+    question: str,
+    probability: str = "None",
+    top_source_url: str = "None",
+    debug_file_link: str = "None",
+    feedback: str = "None",
+):
+    log_dir = "./logs"
+    log_filename = log_dir + "/" + str(output_filename)
+    if not os.path.exists(log_dir):
+        os.makedirs(log_dir)
+    question_formatted = str(question).lower().replace(",", " ").replace(";", "")
+    line = f"{date}, {uid}, {question_formatted}, {probability}, {top_source_url}, {debug_file_link}, {feedback}"
+    with open(log_filename, "a", encoding="utf-8") as log_file:
+        log_file.write(line + "\n")
+        log_file.close()
+
+
 # Print and log the question and response.
 def log_question(
     uid,
     user_question: str,
     response: str,
     probability: str = "None",
-    save: str = "True",
+    save: bool = True,
     logs_to_markdown: str = "False",
 ):
     date_format = "%m/%d/%Y %H:%M:%S %Z"
@@ -68,7 +242,7 @@ def log_question(
     # For the AQA model, also print the response's answerable_probability
     if probability != "None":
         print("Answerable probability: " + str(probability) + "\n")
-    if save == "True":
+    if save:
         log_dir = "./logs"
         log_filename = log_dir + "/chatui_logs.txt"
         if not os.path.exists(log_dir):
@@ -89,13 +263,14 @@ def log_question(
             log_question_to_file(user_question, response, probability)
 
 
-def log_like(is_like, uid, save: str = "True"):
+def log_like(is_like, uid, save: bool = True):
     date_format = "%m/%d/%Y %H:%M:%S %Z"
     date = datetime.now(tz=pytz.utc)
     date = date.astimezone(pytz.timezone("US/Pacific"))
+    print()
     print("UID: " + str(uid))
     print("Like: " + str(is_like))
-    if save == "True":
+    if save:
         log_dir = "./logs"
         log_filename = log_dir + "/chatui_logs.txt"
         with open(log_filename, "a", encoding="utf-8") as log_file:
@@ -104,3 +279,21 @@ def log_like(is_like, uid, save: str = "True"):
             )
             log_file.write("Like: " + str(is_like) + "\n\n")
             log_file.close()
+
+
+def log_dislike(is_dislike, uid, save: bool = True):
+    date_format = "%m/%d/%Y %H:%M:%S %Z"
+    date = datetime.now(tz=pytz.utc)
+    date = date.astimezone(pytz.timezone("US/Pacific"))
+    print()
+    print("UID: " + str(uid))
+    print("Dislike: " + str(is_dislike))
+    if save:
+        log_dir = "./logs"
+        log_filename = log_dir + "/chatui_logs.txt"
+        with open(log_filename, "a", encoding="utf-8") as log_file:
+            log_file.write(
+                "[" + date.strftime(date_format) + "][UID " + str(uid) + "]\n"
+            )
+            log_file.write("Disike: " + str(is_dislike) + "\n\n")
+            log_file.close()
diff --git a/examples/gemini/python/docs-agent/docs_agent/models/__init__.py b/examples/gemini/python/docs-agent/docs_agent/models/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/examples/gemini/python/docs-agent/docs_agent/postprocess/__init__.py b/examples/gemini/python/docs-agent/docs_agent/postprocess/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/examples/gemini/python/docs-agent/docs_agent/preprocess/README.md b/examples/gemini/python/docs-agent/docs_agent/preprocess/README.md
index 6086f70a1..0cb807fd8 100644
--- a/examples/gemini/python/docs-agent/docs_agent/preprocess/README.md
+++ b/examples/gemini/python/docs-agent/docs_agent/preprocess/README.md
@@ -1,30 +1,95 @@
 # Processing source documents in Docs Agent
 
 This `README` file provides information on the steps involved in the following two Python scripts,
-which are used to process source documents into plain text chunks and store them next to their
-generated embeddings in the vector database:
+which are used to process source documents into plain text chunks and generate embeddings for
+vector databases:
 
-- [`markdown_to_plain_text.py`][markdown-to-plain-text]
+- [`files_to_plain_text.py`][files-to-plain-text]
 - [`populate_vector_database.py`][populate-vector-database]
 
-**Important**: The `markdown_to_plain_text.py` script is being deprecated in
-favor of the [`files_to_plain_text.py`][files-to-plain-text] script.
-
 ![Docs Agent pre-processing flow](../../docs/images/docs-agent-pre-processing-01.png)
 
 **Figure 1**. Docs Agent's pre-processing flow from source documents to the vector database.
 
-## Steps in the markdown_to_plain_text.py script
+**Note**: The `markdown_to_plain_text.py` script is deprecated in favor of
+the `files_to_plain_text.py` script.
+
+## Docs Agent chunking technique example
+
+The [`files_to_plain_text.py`][files-to-plain-text] script splits documents
+into smaller chunks based on Markdown headings (#, ##, and ###).
+
+For example, consider the following Markdown page:
+
+```
+# Page title
+
+This is the introduction paragraph of this page.
+
+## Section 1
+
+This is the paragraph of section 1.
+
+### Sub-section 1.1
+
+This is the paragraph of sub-section 1.1.
+
+### Sub-section 1.2
+
+This is the paragraph of sub-section 1.2.
+
+## Section 2
+
+This is the paragraph of section 2.
+```
+
+This example Markdown page is split into the following 5 chunks:
+
+```
+# Page title
+
+This is the introduction paragraph of this page.
+```
 
-When processing Markdown files to plain text using the
-[`markdown_to_plain_text.py`][markdown-to-plain-text] script, the following events take place:
+```
+## Section 1
+
+This is the paragraph of section 1.
+```
+
+```
+### Sub-section 1.1
+
+This is the paragraph of sub-section 1.1.
+```
+
+```
+### Sub-section 1.2
+
+This is the paragraph of sub-section 1.2.
+```
+
+```
+## Section 2
+
+This is the paragraph of section 2.
+```
+
+Additionally, becasue the token size limitation of embedding models, the script
+recursively splits the chunks above until each chunk's size becomes less than
+5000 bytes (characters).
+
+## Steps in the files_to_plain_text.py script
+
+In the default setting, when processing Markdown files to plain text using the
+[`files_to_plain_text.py`][files-to-plain-text] script, the following events take place:
 
 1. Read the configuration file ([`config.yaml`][config-yaml]) to identify input and output
    directories.
-1. Construct an array of input sources.
+1. Construct an array of input sources (which are the `path` entries).
 1. **For** each input source, do the following:
-   1. Extract all input fields (path, URL, and excluded path).
-   1. Call the `process_markdown_files_from_source()` method using these input fields.
+   1. Extract all input fields (`path`, `url_prefix`, and more).
+   1. Call the `process_files_from_input()` method using these input fields.
    1. **For** each sub-path in the input directory and **for** each file in these directories:
       1. Check if the file extension is `.md` (that is, a Markdown file).
       1. Construct an output directory that preserves the path structure.
@@ -63,7 +128,7 @@ When processing plain text chunks to embeddings using the
 
 1. Read the configuration file ([`config.yaml`][config-yaml]) to identify the plain text directory
    and Chroma settings.
-1. Set up the PaLM API environment.
+1. Set up the Gemini API environment.
 1. Select the embeddings model.
 1. Configure the embedding function (including the API call limit).
 1. **For** each sub-path in the plain text directory and **for** each file in these directories:
@@ -73,12 +138,29 @@ When processing plain text chunks to embeddings using the
    1. Read the metadata associated with the text chunk file.
    1. Store the text chunk and metabase to the vector database, which also generates an embedding
       for the text chunk at the time of insertion.
-   1. Skip if the file size is larger than 10000 bytes (due to the API limit).
+   1. Skip if the file size is larger than 5000 bytes (due to the API limit).
    1. Skip if the text chunk is already in the vector database and the checksum hasn’t changed.
 
+### Delete chunks process
+
+The process below describes how the delete chunks feature is implemented in the
+`populate_vector_database.py` script:
+
+1. Read all exiting entries in the target database.
+2. Read all candidate entries in the `file_index.json` file (created after running the
+   `agent chunk` command).
+3. For each entry in the existing entries found in step 1:
+
+   Compare the `text_chunk_filename` fields (included in the entry's `metadata`).
+
+   1. If not found in the candidate entires in step 2, delete this entry in the database.
+
+   1. If found, compare  the `md_hash` fields:
+
+      If they are different, delete this entry in the database.
+
 <!-- Reference links -->
 
-[markdown-to-plain-text]: markdown_to_plain_text.py
 [files-to-plain-text]: files_to_plain_text.py
 [populate-vector-database]: populate_vector_database.py
 [config-yaml]: ../../config.yaml
diff --git a/examples/gemini/python/docs-agent/docs_agent/preprocess/__init__.py b/examples/gemini/python/docs-agent/docs_agent/preprocess/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/examples/gemini/python/docs-agent/docs_agent/preprocess/files_to_plain_text.py b/examples/gemini/python/docs-agent/docs_agent/preprocess/files_to_plain_text.py
index 141156053..f8926aee8 100644
--- a/examples/gemini/python/docs-agent/docs_agent/preprocess/files_to_plain_text.py
+++ b/examples/gemini/python/docs-agent/docs_agent/preprocess/files_to_plain_text.py
@@ -325,6 +325,8 @@ def process_fidl_file(
         filename_to_save = make_file_chunk_name(
             new_path=new_path, filename_prefix=filename_prefix, index=chunk_number
         )
+        # Get the text chunk filename string (after `docs-agent/data`).
+        text_chunk_filename = get_relative_path_and_filename(filename_to_save)
         # Prepare metadata for this FIDL protocol chunk.
         md_hash = uuid.uuid3(namespace_uuid, fidl_protocol)
         uuid_file = uuid.uuid3(namespace_uuid, filename_to_save)
@@ -350,6 +352,7 @@ def process_fidl_file(
             "previous_id": int(1),
             "URL": str(fidl_url),
             "md_hash": str(md_hash),
+            "text_chunk_filename": str(text_chunk_filename),
             "token_estimate": float(1.0),
             "full_token_estimate": float(1.0),
         }
@@ -410,7 +413,7 @@ def process_files_from_input(
     file_index = []
     full_file_metadata = {}
     resolved_output_path = resolve_path(product_config.output_path)
-    chunk_group_name = "text_chunks_" + '{:03d}'.format(input_path_count)
+    chunk_group_name = "text_chunks_" + "{:03d}".format(input_path_count)
     # Get the total file count.
     file_count = sum(len(files) for _, _, files in os.walk(resolve_path(inputpath)))
     # Set up a status bar for the terminal display.
@@ -432,11 +435,17 @@ def process_files_from_input(
             # Displays status bar
             progress_bar.set_description_str(f"Processing file {file}", refresh=True)
             progress_bar.update(1)
+            # Skip this file if it starts with `_`.
+            if file.startswith("_"):
+                continue
             # Get the full path to this input file.
             filename_to_open = os.path.join(root, file)
             # Construct a new sub-directory for storing output plain text files.
-            new_path = resolved_output_path + "/" + chunk_group_name + re.sub(
-                resolve_path(inputpath), "", os.path.join(root, "")
+            new_path = (
+                resolved_output_path
+                + "/"
+                + chunk_group_name
+                + re.sub(resolve_path(inputpath), "", os.path.join(root, ""))
             )
             is_exist = os.path.exists(new_path)
             if not is_exist:
@@ -445,66 +454,67 @@ def process_files_from_input(
             relative_path = make_relative_path(
                 file=file, root=root, inputpath=inputpath
             )
-            # Check the input file type: Markdown, FIDL, or HTML.
-            if file.endswith(".md") and not file.startswith("_"):
-                # Add filename to a list
-                file_index.append(relative_path)
-                # Increment the Markdown file count.
-                md_count += 1
-                # Process a Markdown file.
-                this_file_metadata = process_markdown_file(
-                    filename_to_open,
-                    root,
-                    inputpathitem,
-                    splitter,
-                    new_path,
-                    file,
-                    namespace_uuid,
-                    relative_path,
-                    url_prefix,
-                )
-                # Merge this file's metadata to the global metadata.
-                full_file_metadata.update(this_file_metadata)
-            elif file.endswith(".fidl") and not file.startswith("_"):
-                # Add filename to a list
-                file_index.append(relative_path)
-                # Increment the FIDL file count.
-                fidl_count += 1
-                # Process a FIDL protocol file.
-                this_file_metadata = process_fidl_file(
-                    filename_to_open,
-                    root,
-                    inputpathitem,
-                    splitter,
-                    new_path,
-                    file,
-                    namespace_uuid,
-                    relative_path,
-                    url_prefix,
-                )
-                # Merge this file's metadata to the global metadata.
-                full_file_metadata.update(this_file_metadata)
-            elif (
-                file.endswith(".htm") or file.endswith(".html")
-            ) and not file.startswith("_"):
-                # Add filename to a list
-                file_index.append(relative_path)
-                # Increment the HTML file count.
-                html_count += 1
-                # Process a HTML file.
-                this_file_metadata = process_html_file(
-                    filename_to_open,
-                    root,
-                    inputpathitem,
-                    splitter,
-                    new_path,
-                    file,
-                    namespace_uuid,
-                    relative_path,
-                    url_prefix,
-                )
-                # Merge this file's metadata to the global metadata.
-                full_file_metadata.update(this_file_metadata)
+            # Select Splitter mode: Markdown, FIDL, or HTML.
+            if splitter == "token_splitter" or splitter == "process_sections":
+                if file.endswith(".md"):
+                    # Add filename to a list
+                    file_index.append(relative_path)
+                    # Increment the Markdown file count.
+                    md_count += 1
+                    # Process a Markdown file.
+                    this_file_metadata = process_markdown_file(
+                        filename_to_open,
+                        root,
+                        inputpathitem,
+                        splitter,
+                        new_path,
+                        file,
+                        namespace_uuid,
+                        relative_path,
+                        url_prefix,
+                    )
+                    # Merge this file's metadata to the global metadata.
+                    full_file_metadata.update(this_file_metadata)
+            elif splitter == "fidl_splitter":
+                if file.endswith(".fidl"):
+                    # Add filename to a list
+                    file_index.append(relative_path)
+                    # Increment the FIDL file count.
+                    fidl_count += 1
+                    # Process a FIDL protocol file.
+                    this_file_metadata = process_fidl_file(
+                        filename_to_open,
+                        root,
+                        inputpathitem,
+                        splitter,
+                        new_path,
+                        file,
+                        namespace_uuid,
+                        relative_path,
+                        url_prefix,
+                    )
+                    # Merge this file's metadata to the global metadata.
+                    full_file_metadata.update(this_file_metadata)
+            else:
+                if file.endswith(".htm") or file.endswith(".html"):
+                    # Add filename to a list
+                    file_index.append(relative_path)
+                    # Increment the HTML file count.
+                    html_count += 1
+                    # Process a HTML file.
+                    this_file_metadata = process_html_file(
+                        filename_to_open,
+                        root,
+                        inputpathitem,
+                        splitter,
+                        new_path,
+                        file,
+                        namespace_uuid,
+                        relative_path,
+                        url_prefix,
+                    )
+                    # Merge this file's metadata to the global metadata.
+                    full_file_metadata.update(this_file_metadata)
 
     # The processing of input files is finished.
     progress_bar.set_description_str(f"Finished processing files.", refresh=False)
@@ -705,19 +715,21 @@ def get_chunk_size_distribution_from_product(input_product: ProductConfig):
                 else:
                     count = chunk_size_map["6000"]
                     chunk_size_map["6000"] = count + 1
+                total_file_count += 1
 
     # Print the distribution result.
-    print("\nDistribution of text chunk sizes and counts:")
+    print("\nSpread of text chunk sizes and counts:")
     prev_size = 0
     for key in list(chunk_size_map):
         count = chunk_size_map[key]
         if int(key) == 50:
-            print(f"Chunks smaller than {key} bytes: {count}")
+            print(f"- Chunks smaller than {key} bytes: {count}")
         elif int(key) == 6000:
-            print(f"Chunks larger than {key} bytes: {count}")
+            print(f"- Chunks larger than {key} bytes: {count}")
         else:
-            print(f"Chunks between {prev_size} and {key} bytes: {count}")
+            print(f"- Chunks between {prev_size} and {key} bytes: {count}")
         prev_size = int(key)
+    print(f"\nTotal number of chunks: {total_file_count}")
 
 
 # Given a ReadConfig object, process all products
diff --git a/examples/gemini/python/docs-agent/docs_agent/preprocess/splitters/__init__.py b/examples/gemini/python/docs-agent/docs_agent/preprocess/splitters/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/examples/gemini/python/docs-agent/docs_agent/preprocess/splitters/fidl_splitter.py b/examples/gemini/python/docs-agent/docs_agent/preprocess/splitters/fidl_splitter.py
index 8b7322d2b..50e409b3c 100644
--- a/examples/gemini/python/docs-agent/docs_agent/preprocess/splitters/fidl_splitter.py
+++ b/examples/gemini/python/docs-agent/docs_agent/preprocess/splitters/fidl_splitter.py
@@ -58,8 +58,8 @@ def divide_a_protocol(lines):
 def construct_chunks(library_name: str, protocol_name: str, lines):
     contents = []
     buffer_size = get_byte_size(lines)
-    if int(buffer_size) > 6000:
-        # If the protocol is larget than 6KB, divide it into two.
+    if int(buffer_size) > 5000:
+        # If the protocol is larget than 5KB, divide it into two.
         logging.info(
             "Found a text chunk ("
             + str(protocol_name)
diff --git a/examples/gemini/python/docs-agent/docs_agent/storage/__init__.py b/examples/gemini/python/docs-agent/docs_agent/storage/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/examples/gemini/python/docs-agent/docs_agent/tests/__init__.py b/examples/gemini/python/docs-agent/docs_agent/tests/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/examples/gemini/python/docs-agent/docs_agent/tests/cli_unit_tests.py b/examples/gemini/python/docs-agent/docs_agent/tests/cli_unit_tests.py
new file mode 100644
index 000000000..9cfb28c48
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/tests/cli_unit_tests.py
@@ -0,0 +1,11 @@
+"""Unit tests for Docs Agent CLI."""
+
+import unittest
+
+
+class DocsAgentCLIUnitTest(unittest.TestCase):
+  def test_basic(self):
+    self.assertEqual('hello'.upper(), 'HELLO')
+
+if __name__ == "__main__":
+  unittest.main()
diff --git a/examples/gemini/python/docs-agent/docs_agent/testing/test_vector_database.py b/examples/gemini/python/docs-agent/docs_agent/tests/test_vector_database.py
similarity index 100%
rename from examples/gemini/python/docs-agent/docs_agent/testing/test_vector_database.py
rename to examples/gemini/python/docs-agent/docs_agent/tests/test_vector_database.py
diff --git a/examples/gemini/python/docs-agent/docs_agent/utilities/__init__.py b/examples/gemini/python/docs-agent/docs_agent/utilities/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/examples/gemini/python/docs-agent/docs_agent/utilities/config.py b/examples/gemini/python/docs-agent/docs_agent/utilities/config.py
index 84c32e9f4..aa1846d93 100644
--- a/examples/gemini/python/docs-agent/docs_agent/utilities/config.py
+++ b/examples/gemini/python/docs-agent/docs_agent/utilities/config.py
@@ -309,6 +309,7 @@ def __init__(
         feedback_mode: str = "rewrite",
         enable_show_logs: str = "False",
         enable_logs_to_markdown: str = "False",
+        enable_logs_for_debugging: str = "False",
         enable_delete_chunks: str = "False",
         secondary_db_type: typing.Optional[str] = None,
         secondary_corpus_name: typing.Optional[str] = None,
@@ -328,6 +329,7 @@ def __init__(
         self.feedback_mode = feedback_mode
         self.enable_show_logs = enable_show_logs
         self.enable_logs_to_markdown = enable_logs_to_markdown
+        self.enable_logs_for_debugging = enable_logs_for_debugging
         self.enable_delete_chunks = enable_delete_chunks
         self.secondary_db_type = secondary_db_type
         self.secondary_corpus_name = secondary_corpus_name
@@ -350,6 +352,7 @@ def __str__(self):
 Feedback mode: {self.feedback_mode}\n\
 Enable show logs: {self.enable_show_logs}\n\
 Enable logs to Markdown: {self.enable_logs_to_markdown}\n\
+Enable logs for debugging: {self.enable_logs_for_debugging}\n\
 Enable delete chunks: {self.enable_delete_chunks}\n\
 Markdown splitter: {self.markdown_splitter}\n\
 Database type: {self.db_type}\n\
@@ -443,6 +446,10 @@ def returnProducts(self, product: typing.Optional[str] = None) -> ConfigFile:
                     enable_logs_to_markdown = item["enable_logs_to_markdown"]
                 except KeyError:
                     enable_logs_to_markdown = "False"
+                try:
+                    enable_logs_for_debugging = item["enable_logs_for_debugging"]
+                except KeyError:
+                    enable_logs_for_debugging = "False"
                 try:
                     enable_delete_chunks = item["enable_delete_chunks"]
                 except KeyError:
@@ -471,7 +478,7 @@ def returnProducts(self, product: typing.Optional[str] = None) -> ConfigFile:
                         app_port=app_port,
                         feedback_mode=feedback_mode,
                         enable_show_logs=enable_show_logs,
-                        enable_logs_to_markdown=enable_logs_to_markdown,
+                        enable_logs_for_debugging=enable_logs_for_debugging,
                         enable_delete_chunks=enable_delete_chunks,
                         secondary_db_type=secondary_db_type,
                         secondary_corpus_name=secondary_corpus_name,
@@ -528,7 +535,7 @@ def returnProducts(self, product: typing.Optional[str] = None) -> ConfigFile:
 
 # Function to make using common_options simpler
 def return_config_and_product(
-    config_file: typing.Optional[str] = None, product: list[str] = [""]
+    config_file: typing.Optional[str] = None, product: list[str] = [""], model: typing.Optional[str] = None
 ):
     if config_file is None:
         loaded_config = ReadConfig()
@@ -542,5 +549,9 @@ def return_config_and_product(
         for item in product:
             product_config = loaded_config.returnProducts(product=item)
             final_products.append(product_config.products[0])
+    # Overwrites the language_model for all products
+    if model is not None:
+        for item in final_products:
+            item.models.language_model = model
     product_config = ConfigFile(products=final_products)
     return loaded_config, product_config

From 0b491c39f17aeb27c878b72484358d28b6558f06 Mon Sep 17 00:00:00 2001
From: Kyo Lee <kyol@google.com>
Date: Sun, 12 May 2024 23:59:28 +0000
Subject: [PATCH 08/12] [Docs Agent] Bug fix - Fix the "index out of range"
 errors in Docs Agent's chat web app.

---
 .../python/docs-agent/docs_agent/agents/docs_agent.py       | 6 +++++-
 .../docs-agent/docs_agent/interfaces/chatbot/chatui.py      | 4 +++-
 2 files changed, 8 insertions(+), 2 deletions(-)

diff --git a/examples/gemini/python/docs-agent/docs_agent/agents/docs_agent.py b/examples/gemini/python/docs-agent/docs_agent/agents/docs_agent.py
index 820c22034..3c4ae3964 100644
--- a/examples/gemini/python/docs-agent/docs_agent/agents/docs_agent.py
+++ b/examples/gemini/python/docs-agent/docs_agent/agents/docs_agent.py
@@ -439,7 +439,11 @@ def query_vector_store_to_build(
         plain_token = 0
         sources = []
         final_pages = []
-        for i in range(max_sources):
+        # Quick fix: Ensure max_sources is not larger than the array size of search_result.
+        this_range = len(search_result)
+        if this_range > max_sources:
+            this_range = max_sources
+        for i in range(this_range):
             # The current section that is being built
             # eval turns str representation of array into an array
             curr_section_id = search_result[i].section.name_id
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/chatui.py b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/chatui.py
index 730353687..b8b0465a3 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/chatui.py
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/chatui.py
@@ -472,7 +472,9 @@ def ask_model(question, agent, template: str = "chatui/index.html"):
         # Log debug information.
 
         if docs_agent.config.enable_logs_for_debugging == "True":
-            top_source_url = search_result[0].section.url
+            top_source_url = ""
+            if len(search_result) > 0:
+                top_source_url = search_result[0].section.url
             source_urls = ""
             index = 1
             for result in search_result:

From d1879339f90bc3a59d04bde745f0fb34d0921864 Mon Sep 17 00:00:00 2001
From: Kyo Lee <kyol@google.com>
Date: Fri, 14 Jun 2024 20:34:18 +0000
Subject: [PATCH 09/12] [Docs Agent] Release of Docs Agent v 0.3.5.

What's changed:

- **`agent runtask` command:** Run tasks defined in `.yaml` files, for example,
  generating release notes based on commit messages.
- **Session Management:** Start and maintain sessions using `--new` and `--cont`
  flags with the `helpme` and `tellme` commands.
- **CLI Command Refactoring:** The CLI is more modular and easier to extend
  with new commands.
- **Web App Mode Updates:** A new `widget-pro` mode is added as an
  iFrame-friendly template.
- **Bug Fixes:**  Various bug fixes related to flags, UI, and file handling.
---
 examples/gemini/python/docs-agent/README.md   |  16 +-
 .../apps_script/helper_functions.gs           |  25 +
 examples/gemini/python/docs-agent/config.yaml |   4 +-
 .../python/docs-agent/docs/cli-reference.md   |  76 +-
 .../docs-agent/docs/config-reference.md       |   8 +-
 .../docs_agent/agents/docs_agent.py           |  71 +-
 .../docs_agent/interfaces/README.md           |  31 +-
 .../docs_agent/interfaces/chatbot/chatui.py   |  92 ++-
 .../chatbot/static/css/style-chatui-full.css  | 625 ++++++++++++++
 .../static/css/style-chatui-widget-pro.css    | 635 +++++++++++++++
 .../static/css/style-chatui-widget.css        | 624 ++++++++++++++
 .../chatbot/templates/admin/debugs.html       |   2 +-
 .../chatbot/templates/admin/logs.html         |   2 +-
 .../{chatui-1.5 => chatui-full}/base.html     |   2 +-
 .../{chatui-1.5 => chatui-full}/index.html    |   4 +-
 .../{chatui-1.5 => chatui-full}/result.html   |   0
 .../templates/chatui-widget-pro/base.html     |  28 +
 .../templates/chatui-widget-pro/index.html    |  16 +
 .../templates/chatui-widget-pro/result.html   | 218 +++++
 .../{chat-widget => chatui-widget}/base.html  |   2 +-
 .../{chat-widget => chatui-widget}/index.html |   4 +-
 .../result.html                               |   0
 .../docs_agent/interfaces/cli/__init__.py     |   0
 .../docs_agent/interfaces/cli/cli.py          |  37 +
 .../interfaces/{cli.py => cli/cli_admin.py}   | 342 +-------
 .../docs_agent/interfaces/cli/cli_common.py   |  67 ++
 .../docs_agent/interfaces/cli/cli_helpme.py   | 765 ++++++++++++++++++
 .../docs_agent/interfaces/cli/cli_posix.py    | 114 +++
 .../docs_agent/interfaces/cli/cli_runtask.py  | 476 +++++++++++
 .../interfaces/cli/cli_show_session.py        |  59 ++
 .../docs_agent/interfaces/cli/cli_tellme.py   | 127 +++
 .../docs_agent/interfaces/run_console.py      |  67 +-
 .../docs-agent/docs_agent/memory/logging.py   |  24 +-
 .../docs-agent/docs_agent/utilities/config.py | 142 +++-
 .../docs-agent/docs_agent/utilities/tasks.py  | 389 +++++++++
 .../gemini/python/docs-agent/pyproject.toml   |   2 +-
 .../python/docs-agent/{ => scripts}/helpme.sh |   4 +-
 .../python/docs-agent/{ => scripts}/tellme.sh |   0
 .../docs-agent/tasks/release-notes-task.yaml  |  16 +
 39 files changed, 4639 insertions(+), 477 deletions(-)
 create mode 100644 examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui-full.css
 create mode 100644 examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui-widget-pro.css
 create mode 100644 examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui-widget.css
 rename examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/{chatui-1.5 => chatui-full}/base.html (96%)
 rename examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/{chatui-1.5 => chatui-full}/index.html (83%)
 rename examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/{chatui-1.5 => chatui-full}/result.html (100%)
 create mode 100644 examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget-pro/base.html
 create mode 100644 examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget-pro/index.html
 create mode 100644 examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget-pro/result.html
 rename examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/{chat-widget => chatui-widget}/base.html (96%)
 rename examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/{chat-widget => chatui-widget}/index.html (83%)
 rename examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/{chat-widget => chatui-widget}/result.html (100%)
 create mode 100644 examples/gemini/python/docs-agent/docs_agent/interfaces/cli/__init__.py
 create mode 100644 examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli.py
 rename examples/gemini/python/docs-agent/docs_agent/interfaces/{cli.py => cli/cli_admin.py} (54%)
 create mode 100644 examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_common.py
 create mode 100644 examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_helpme.py
 create mode 100644 examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_posix.py
 create mode 100644 examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_runtask.py
 create mode 100644 examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_show_session.py
 create mode 100644 examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_tellme.py
 create mode 100644 examples/gemini/python/docs-agent/docs_agent/utilities/tasks.py
 rename examples/gemini/python/docs-agent/{ => scripts}/helpme.sh (90%)
 rename examples/gemini/python/docs-agent/{ => scripts}/tellme.sh (100%)
 create mode 100644 examples/gemini/python/docs-agent/tasks/release-notes-task.yaml

diff --git a/examples/gemini/python/docs-agent/README.md b/examples/gemini/python/docs-agent/README.md
index 357761fd2..d3d368eb9 100644
--- a/examples/gemini/python/docs-agent/README.md
+++ b/examples/gemini/python/docs-agent/README.md
@@ -63,6 +63,15 @@ The following list summarizes the tasks and features supported by Docs Agent:
   `gemini-1.5-pro-latest` and `text-embedding-004`. The new ["1.5"][new-15-mode] web app
   mode uses all three Gemini models to their strength: AQA (`aqa`), Gemini 1.0 Pro
   (`gemini-pro`), and Gemini 1.5 Pro (`gemini-1.5-pro-latest`).
+- **Complete a task using the Docs Agent CLI**: The `agent runtask` command allows you
+  to run pre-defined chains of prompts, which are referred to as tasks. These tasks
+  simplify complex interactions by defining a series of steps that the Docs Agent will
+  execute. The tasks are defined in .yaml files stored in the [`tasks`][tasks-dir]
+  directory of your Docs Agent project. To run a task in this directory, for example:
+
+  ```sh
+  agent runtask --task DraftReleaseNotes
+  ```
 
 For more information on Docs Agent's architecture and features,
 see the [Docs Agent concepts][docs-agent-concepts] page.
@@ -279,13 +288,17 @@ Update settings in the Docs Agent project to use your custom dataset:
 
    (**Optional**) Or if you want to use the Gemini AQA model and populate
    a corpus online via the [Semantic Retrieval API][semantic-api], use the
-   following settings:
+   following settings (and update the `corpus_name` field):
 
    ```
    models:
      - language_model: "models/aqa"
    ...
    db_type: "google_semantic_retriever"
+   db_configs:
+     ...
+     - db_type: "google_semantic_retriever"
+       corpus_name: "corpora/flutter-dev"
    ```
 
 7. Save the `config.yaml` file and exit the text editor.
@@ -416,3 +429,4 @@ Meggin Kearney (`@Meggin`), and Kyo Lee (`@kyolee415`).
 [cli-reference]: docs/cli-reference.md
 [chunking-process]: docs/chunking-process.md
 [new-15-mode]: docs/config-reference.md#app_mode
+[tasks-dir]: tasks/
diff --git a/examples/gemini/python/docs-agent/apps_script/helper_functions.gs b/examples/gemini/python/docs-agent/apps_script/helper_functions.gs
index 020897be4..3fbf96866 100644
--- a/examples/gemini/python/docs-agent/apps_script/helper_functions.gs
+++ b/examples/gemini/python/docs-agent/apps_script/helper_functions.gs
@@ -25,6 +25,17 @@ function folderExists(folderName) {
   }
 }
 
+// Checks to see if a folder already exists in the specified root folder
+function folderExistsInRoot(folderName, rootFolder) {
+  const folderIterator = rootFolder.getFoldersByName(folderName);
+  if(folderIterator.hasNext()) {
+    return true;
+  }
+  else {
+    return false;
+  }
+}
+
 // Checks to see if a folder already exists in the drive and exits if it doesn't. Useful for input directories
 function folderExistsInput(folderName){
   if (folderExists(folderName)) {
@@ -49,6 +60,20 @@ function folderExistsOrCreate(folderName){
     return true;
   }
 }
+
+// Checks to see if folder exists or creates it. Useful for output directories
+function folderExistsOrCreateSubdir(folderName, rootFolder){
+  if(folderExistsInRoot(folderName, rootFolder)) {
+    Logger.log("Folder exists: "+ folderName);
+    return true;
+  }
+  else {
+    Logger.log("Folder does not exist: "+ folderName + ". Creating the directory.");
+    rootFolder.createFolder(folderName);
+    return true;
+  }
+}
+
 // Checks to see if a file exists in a folder
 function checkFileExists(fileName,folderName){
   let folder = DriveApp.getFoldersByName(folderName);
diff --git a/examples/gemini/python/docs-agent/config.yaml b/examples/gemini/python/docs-agent/config.yaml
index b1f74bc5f..8d7fdc9df 100644
--- a/examples/gemini/python/docs-agent/config.yaml
+++ b/examples/gemini/python/docs-agent/config.yaml
@@ -15,7 +15,7 @@
 #
 
 configs:
-  - product_name: "Flutter"
+  - product_name: "Fuchsia"
     models:
       - language_model: "models/aqa"
         embedding_model: "models/embedding-001"
@@ -31,7 +31,7 @@ configs:
         vector_db_dir: "vector_stores/chroma"
         collection_name: "docs_collection"
       - db_type: "google_semantic_retriever"
-        corpus_name: "corpora/flutter-dev"
+        corpus_name: "corpora/fuchsia-dev"
     output_path: "data/plain_docs"
     inputs:
       - path: "/usr/local/home/user01/website/src"
diff --git a/examples/gemini/python/docs-agent/docs/cli-reference.md b/examples/gemini/python/docs-agent/docs/cli-reference.md
index e7385aa92..5967b65b9 100644
--- a/examples/gemini/python/docs-agent/docs/cli-reference.md
+++ b/examples/gemini/python/docs-agent/docs/cli-reference.md
@@ -94,6 +94,16 @@ a widget-friendly template:
 agent chatbot --app_mode widget
 ```
 
+### Launch the Docs Agent web app in full mode
+
+The command below launches the Docs Agent web app to use
+a special template that uses three Gemini models (AQA, Gemini 1.5,
+and Gemini 1.0):
+
+```sh
+agent chatbot --app_mode full
+```
+
 ### Launch the Docs Agent web app with a log view enabled
 
 The command below launches the Docs Agent web app while enabling
@@ -172,6 +182,15 @@ absolure or relative path, for example:
 agent helpme write comments for this C++ file? --file ../my-project/test.cc
 ```
 
+### Ask for advice using RAG
+
+The command below uses a local or online vector database (specified in
+the `config.yaml` file) to retrieve relevant context for the request:
+
+```sh
+agent helpme <REQUEST> --file <PATH_TO_FILE> --rag
+```
+
 ### Ask for advice in a session
 
 The command below starts a new session (`--new`), which tracks responses,
@@ -200,15 +219,63 @@ For example:
 agent helpme write a concept doc that delves into more details of these features? --cont
 ```
 
-### Ask for advice using RAG
+### Print the context in the current session
 
-The command below uses a local or online vector database (specified in
-the `config.yaml` file) to retrieve relevant context for the request:
+The command below prints the questions, files, and responses that
+are being used as context in the current session:
 
 ```sh
-agent helpme <REQUEST> --file <PATH_TO_FILE> --rag
+agent show-session
+```
+
+### Ask the model to perform the request to each file in a directory
+
+The command below applies the request to each file found in the
+specified directory:
+
+```sh
+agent helpme <REQUEST> --perfile <PATH_TO_DIRECTORY>
+```
+
+For example:
+
+```sh
+agent helpme explain what this file does? --perfile ~/my-project --new
 ```
 
+### Ask the model to include all files in a directory as context
+
+The command below includes all files found in the specified directory
+as context to the request:
+
+```sh
+agent helpme <REQUEST> --allfiles <PATH_TO_DIRECTORY>
+```
+
+For example:
+
+```sh
+agent helpme write a concept doc covering all features in this project? --allfiles ~/my-project --new
+```
+
+### Ask the model to run a pre-defined chain of prompts
+
+The command below runs a task (a sequence of prompts) defined in
+a `.yaml` file stored in the [`tasks`][tasks-dir] directory:
+
+```sh
+agent runtask --task <TASK>
+```
+
+For example:
+
+```sh
+agent runtask --task DraftReleaseNotes
+```
+
+To see the list of all tasks available in your project, run
+`agent runtask` without any arguments.
+
 ## Managing online corpora
 
 ### List all existing online corpora
@@ -267,3 +334,4 @@ agent delete-corpus --name corpora/example01
 [benchmarks-yaml]: ../docs_agent/benchmarks/benchmarks.yaml
 [set-up-docs-agent-cli]: ../docs_agent/interfaces/README.md
 [semantic-api]: https://ai.google.dev/docs/semantic_retriever
+[tasks-dir]: ../tasks
diff --git a/examples/gemini/python/docs-agent/docs/config-reference.md b/examples/gemini/python/docs-agent/docs/config-reference.md
index cbb5eda9e..2cdc30629 100644
--- a/examples/gemini/python/docs-agent/docs/config-reference.md
+++ b/examples/gemini/python/docs-agent/docs/config-reference.md
@@ -28,14 +28,10 @@ The options are:
   app_mode: "widget"
   ```
 
-* `1.5`: This special mode is designed to be used with the Gemini 1.5 Pro
-  model.
+* `full`: This special mode is designed to be used with Gemini 1.5 models.
 
   ```
-  models:
-    - language_model: "models/aqa"
-  ...
-  app_mode: "1.5"
+  app_mode: "full"
   ```
 
 When this field is not specified, the web app is set to use the standard mode.
diff --git a/examples/gemini/python/docs-agent/docs_agent/agents/docs_agent.py b/examples/gemini/python/docs-agent/docs_agent/agents/docs_agent.py
index 3c4ae3964..992a46c6f 100644
--- a/examples/gemini/python/docs-agent/docs_agent/agents/docs_agent.py
+++ b/examples/gemini/python/docs-agent/docs_agent/agents/docs_agent.py
@@ -49,8 +49,17 @@ def __init__(
     ):
         # Models settings
         self.config = config
+        self.language_model = str(self.config.models.language_model)
         self.embedding_model = str(self.config.models.embedding_model)
         self.api_endpoint = str(self.config.models.api_endpoint)
+
+        # Initialize the default Gemini model.
+        if self.language_model.startswith("models/gemini"):
+            self.gemini = Gemini(
+                models_config=config.models, conditions=config.conditions
+            )
+            self.context_model = self.language_model
+
         # Use the new chroma db for all queries
         # Should make a function for this or clean this behavior
         if init_chroma:
@@ -69,9 +78,28 @@ def __init__(
                     self.config.models.api_key, self.embedding_model
                 ),
             )
+
         # AQA model settings
         if init_semantic:
-            if self.config.models.language_model == "models/aqa":
+            # Except in "full" and "pro" modes, the semantic retriever option requires
+            # the AQA model. If not, exit the program.
+            if (
+                self.config.app_mode != "full"
+                and self.config.app_mode != "widget-pro"
+                and self.config.db_type == "google_semantic_retriever"
+            ):
+                if self.language_model != "models/aqa":
+                    logging.error(
+                        "The db_type `google_semnatic_retriever` option"
+                        + " requires the AQA model (`models/aqa`)."
+                    )
+                    exit(1)
+            # If the AQA model is selected or the web app is on "full" and "pro" modes.
+            if (
+                self.language_model == "models/aqa"
+                or self.config.app_mode == "full"
+                or self.config.app_mode == "widget-pro"
+            ):
                 # AQA model setup
                 self.generative_service_client = glm.GenerativeServiceClient()
                 self.retriever_service_client = glm.RetrieverServiceClient()
@@ -86,7 +114,7 @@ def __init__(
                 self.gemini = Gemini(
                     models_config=gemini_model_config, conditions=config.conditions
                 )
-            # Semantic retriever
+            # If semantic retriever is selected as the main database.
             if self.config.db_type == "google_semantic_retriever":
                 for item in self.config.db_configs:
                     if "google_semantic_retriever" in item.db_type:
@@ -99,13 +127,7 @@ def __init__(
                             )
                 self.aqa_response_buffer = ""
 
-        if self.config.models.language_model.startswith("models/gemini"):
-            self.gemini = Gemini(
-                models_config=config.models, conditions=config.conditions
-            )
-            self.context_model = self.config.models.language_model
-
-        # Always initialize the gemini-pro model for other tasks.
+        # Always initialize the Gemini 1.0 pro model for other tasks.
         gemini_pro_model_config = Models(
             language_model="models/gemini-pro",
             embedding_model=self.embedding_model,
@@ -115,10 +137,10 @@ def __init__(
             models_config=gemini_pro_model_config, conditions=config.conditions
         )
 
-        if self.config.app_mode == "1.5":
-            # Initialize the gemini-1.5.pro model for summarization.
+        if self.config.app_mode == "full" or self.config.app_mode == "widget-pro":
+            # Initialize the Gemini 1.5 model for generating main responses.
             gemini_15_model_config = Models(
-                language_model="models/gemini-1.5-pro-latest",
+                language_model=self.language_model,
                 embedding_model=self.embedding_model,
                 api_endpoint=self.api_endpoint,
             )
@@ -174,7 +196,7 @@ def ask_aqa_model_using_local_vector_store(
             )
             verbose_prompt += "\nID: " + index_id + "\n" + returned_context + "\n"
         req = glm.GenerateAnswerRequest(
-            model=self.config.models.language_model,
+            model="models/aqa",
             contents=[user_query_content],
             inline_passages=grounding_passages,
             answer_style=answer_style,
@@ -244,19 +266,14 @@ def ask_aqa_model_using_corpora(
             source=corpus_name, query=user_question_content
         )
 
-        if self.config.models.language_model == "models/aqa":
-            req = glm.GenerateAnswerRequest(
-                model=self.config.models.language_model,
-                contents=[user_question_content],
-                semantic_retriever=retriever_config,
-                answer_style=answer_style,
-            )
-        else:
-            self.aqa_response_buffer = ""
-            logging.error(
-                "This function should only be used with a setting of language_model models/aqa"
-            )
-            return self.config.conditions.model_error_message, search_result
+        # Ask the AQA model.
+        req = glm.GenerateAnswerRequest(
+            model="models/aqa",
+            contents=[user_question_content],
+            semantic_retriever=retriever_config,
+            answer_style=answer_style,
+        )
+
         try:
             aqa_response = self.generative_service_client.generate_answer(req)
             self.aqa_response_buffer = aqa_response
@@ -509,7 +526,7 @@ def ask_content_model_with_context_prompt(
             response = ""
             if model == "gemini-pro":
                 response = self.gemini_pro.generate_content(contents=new_prompt)
-            elif model == "gemini-1.5-pro":
+            elif model == "gemini-1.5":
                 response = self.gemini_15.generate_content(contents=new_prompt)
             else:
                 response = self.gemini.generate_content(contents=new_prompt)
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/README.md b/examples/gemini/python/docs-agent/docs_agent/interfaces/README.md
index adcb880b1..7a3d83b13 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/README.md
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/README.md
@@ -93,6 +93,9 @@ Setting up Docs Agent requires the following prerequisite items:
 **Note**: This step may not be necessary if you already have OAuth client
 credentials (via `gcloud`) stored on your host machine.
 
+**Note**: This step is **only necessary** if you plan on using the
+`agent tellme` command to interact with your online corpora on Google Cloud.
+
 1. Download the `client_secret.json` file from your
    [Google Cloud project][authorize-credentials].
 
@@ -142,18 +145,28 @@ from your `$HOME` directory.
    poetry install
    ```
 
+At this point, you can start using the `agent helpme`, `agent tellme`,
+and `agent runtask` commands to interact with the Gemini models from
+your terminal. For more information on these commands, see the
+[Interacting with language models][cli-reference-helpme] section in
+the CLI reference page.
+
+Proceed to the next section if you want to set up an alias for the
+`agent tellme` command.
+
 ## 5. Set up an alias to the gemini command
 
 **Note**: If your Docs Agent project is not cloned in the `$HOME` directory,
-you need to edit the `tellme.sh` script in your `docs-agent` project directory.
+you need to edit the `scripts/tellme.sh` script in your `docs-agent` project directory.
 
 Update your shell environment so that the `gemini` command can be run
 from anywhere in the terminal:
 
-1. (**Optional**) Open the `tellme.sh` file using a text editor, for example:
+1. (**Optional**) Open the `scripts/tellme.sh` file using a text editor,
+   for example:
 
    ```
-   nano tellme.sh
+   nano scripts/tellme.sh
    ```
 
    If necessary, adjust the path (`$HOME/docs-agent`) to match your
@@ -164,12 +177,12 @@ from anywhere in the terminal:
    docs_agent_dir="$HOME/docs-agent"
    ```
 
-   Save the file and close text editor.
+   Save the file and close the text editor.
 
 2. Add the following `alias` line to your `$HOME/.bashrc` file:
 
    ```
-   alias gemini='$HOME/docs-agent/tellme.sh'
+   alias gemini='$HOME/docs-agent/scripts/tellme.sh'
    ```
 
    Similarly, if necessary, you need to adjust the path
@@ -224,10 +237,11 @@ host machine is **not required**.
 
 To set up this `helpme` command in your terminal, do the following:
 
-1. (**Optional**) Open the `helpme.sh` file using a text editor, for example:
+1. (**Optional**) Open the `scripts/helpme.sh` file using a text editor,
+   for example:
 
    ```
-   nano helpme.sh
+   nano scripts/helpme.sh
    ```
 
    If necessary, adjust the path (`$HOME/docs-agent`) to match your
@@ -243,7 +257,7 @@ To set up this `helpme` command in your terminal, do the following:
 2. Add the following `alias` lines to your `$HOME/.bashrc` file:
 
    ```
-   alias gemini-pro='$HOME/docs-agent/helpme.sh'
+   alias gemini-pro='$HOME/docs-agent/scripts/helpme.sh'
    alias start_agent='script -f -o 200MiB -O /tmp/docs_agent_console_input'
    alias stop_agent='exit'
    ```
@@ -303,3 +317,4 @@ To set up this `helpme` command in your terminal, do the following:
 [oauth-client]: https://ai.google.dev/docs/oauth_quickstart#set-cloud
 [authorize-credentials]: https://ai.google.dev/docs/oauth_quickstart#authorize-credentials
 [genai-doc-site]: https://ai.google.dev/docs/gemini_api_overview
+[cli-reference-helpme]: ../../docs/cli-reference.md#interacting-with-language-models
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/chatui.py b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/chatui.py
index b8b0465a3..4b713050d 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/chatui.py
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/chatui.py
@@ -78,11 +78,14 @@ def construct_blueprint(
         app_template = "chatui-experimental/index.html"
         redirect_index = "chatui-experimental.index"
     elif app_mode == "widget":
-        app_template = "chat-widget/index.html"
-        redirect_index = "chat-widget.index"
-    elif app_mode == "1.5":
-        app_template = "chatui-1.5/index.html"
-        redirect_index = "chatui-1.5.index"
+        app_template = "chatui-widget/index.html"
+        redirect_index = "chatui-widget.index"
+    elif app_mode == "full":
+        app_template = "chatui-full/index.html"
+        redirect_index = "chatui-full.index"
+    elif app_mode == "widget-pro":
+        app_template = "chatui-widget-pro/index.html"
+        redirect_index = "chatui-widget-pro.index"
     else:
         app_template = "chatui/index.html"
         redirect_index = "chatui.index"
@@ -310,8 +313,35 @@ def ask_model(question, agent, template: str = "chatui/index.html"):
         can_be_logged = False
 
     # Retrieve context and ask the question.
-    if "gemini" in docs_agent.config.models.language_model:
-        # For the `gemini-*` model
+    if (
+        docs_agent.config.app_mode == "full"
+        or docs_agent.config.app_mode == "widget-pro"
+        or "aqa" in docs_agent.config.models.language_model
+    ):
+        # For "full" and "pro" modes, use the AQA model for the first request.
+        # For the AQA model, check the DB type.
+        if docs_agent.config.db_type == "chroma":
+            (
+                response,
+                search_result,
+            ) = docs_agent.ask_aqa_model_using_local_vector_store(
+                question=question, results_num=results_num
+            )
+        else:
+            (response, search_result) = docs_agent.ask_aqa_model_using_corpora(
+                question=question
+            )
+        # Extract context from this AQA model's response.
+        final_context = extract_context_from_search_result(search_result)
+        # Save this AQA model's response.
+        aqa_response_json = docs_agent.get_saved_aqa_response_json()
+        # Convert this AQA model's response to HTML for better rendering.
+        if aqa_response_json:
+            aqa_response_in_html = json.dumps(
+                type(aqa_response_json).to_dict(aqa_response_json), indent=2
+            )
+    else:
+        # For the `gemini-*` model, alway use the Chroma database.
         if docs_agent.config.docs_agent_config == "experimental":
             results_num = 10
             new_question_count = 5
@@ -325,9 +355,12 @@ def ask_model(question, agent, template: str = "chatui/index.html"):
             # response = ask_content_model_with_context(context="", question=question)
             # Issue if max_sources > results_num, so leave the same for now
         else:
+            this_token_limit = 30000
+            if docs_agent.config.models.language_model.startswith("models/gemini-1.5"):
+                this_token_limit = 50000
             search_result, final_context = docs_agent.query_vector_store_to_build(
                 question=question,
-                token_limit=30000,
+                token_limit=this_token_limit,
                 results_num=results_num,
                 max_sources=results_num,
             )
@@ -338,28 +371,6 @@ def ask_model(question, agent, template: str = "chatui/index.html"):
             aqa_response_in_html = ""
         except:
             logging.error("Failed to ask content model with context prompt.")
-    elif "aqa" in docs_agent.config.models.language_model:
-        # For the AQA model
-        if docs_agent.config.db_type == "chroma":
-            (
-                response,
-                search_result,
-            ) = docs_agent.ask_aqa_model_using_local_vector_store(
-                question=question, results_num=results_num
-            )
-        else:
-            (response, search_result) = docs_agent.ask_aqa_model_using_corpora(
-                question=question
-            )
-        # Extract context from this AQA model's response.
-        final_context = extract_context_from_search_result(search_result)
-        # Save this AQA model's response.
-        aqa_response_json = docs_agent.get_saved_aqa_response_json()
-        # Convert this AQA model's response to HTML for better rendering.
-        if aqa_response_json:
-            aqa_response_in_html = json.dumps(
-                type(aqa_response_json).to_dict(aqa_response_json), indent=2
-            )
 
     ### Check the AQA model's answerable_probability field
     probability = "None"
@@ -370,9 +381,13 @@ def ask_model(question, agent, template: str = "chatui/index.html"):
         except:
             probability = 0.0
 
-    # For the 1.5 mode, retrieve additional context from the secondary knowledge database.
+    # For "full" and "pro" modes, retrieve additional context from
+    # the secondary knowledge database.
     additional_context = ""
-    if docs_agent.config.app_mode == "1.5":
+    if (
+        docs_agent.config.app_mode == "full"
+        or docs_agent.config.app_mode == "widget-pro"
+    ):
         if docs_agent.config.secondary_db_type == "chroma":
             (
                 additional_search_result,
@@ -431,11 +446,14 @@ def ask_model(question, agent, template: str = "chatui/index.html"):
     new_uuid = uuid.uuid1()
     server_url = request.url_root.replace("http", "https")
 
-    ### The code below is added for the new Gemini 1.5 model.
-    # Ask the Gemini 1.5 model to generate a full summary.
-    if docs_agent.config.app_mode == "1.5" and docs_agent.config.db_type != "none":
+    ### The code below is added for "full" and "pro" modes.
+    # Ask the model to generate the main response.
+    if (
+        docs_agent.config.app_mode == "full"
+        or docs_agent.config.app_mode == "widget-pro"
+    ) and docs_agent.config.db_type != "none":
         if additional_context != "":
-            extended_context = f"RELEVANT CONTEXT FOUND IN DOCUMENTATION:\n\n{additional_context}\n\nRELEVANT CONVERSATIONS:\n\n{final_context}\n"
+            extended_context = f"RELEVANT CONTEXT FOUND IN SECONDARY KNOWLEDGE SOURCE:\n\n{additional_context}\n\nRELEVANT CONTEXT FOUND IN PRIMARY KNOWLEDGE SOURCE:\n\n{final_context}\n"
         else:
             extended_context = f"{final_context}\n"
         additional_condition = (
@@ -449,7 +467,7 @@ def ask_model(question, agent, template: str = "chatui/index.html"):
             context=extended_context,
             question=question,
             prompt=new_condition,
-            model="gemini-1.5-pro",
+            model="gemini-1.5",
         )
         log_lines = f"{response}\n\n{summary_response}"
     else:
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui-full.css b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui-full.css
new file mode 100644
index 000000000..4cdb442e3
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui-full.css
@@ -0,0 +1,625 @@
+/**
+ * Copyright 2023 Google LLC
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ======= General style for HTML elements ======= */
+
+body {
+    font: 16px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
+    color: #333;
+    font-weight: 300;
+    max-width: 960px;
+    margin: auto;
+    background-color: #d9d9d9;
+    padding-top: 15px;
+    padding-bottom: 15px;
+  }
+
+  a {
+    color: #0a619a;
+  }
+
+  p {
+    margin: 0 0 1em;
+    line-height: 130%;
+  }
+
+  h1 {
+    margin: 0 0 0.5em;
+    font-weight: 500;
+    font-size: 1.3em;
+    margin-left: 1.0em;
+    margin-top: 0.3em;
+  }
+
+  h2 {
+    margin: 0;
+    margin-top: 17px;
+    margin-bottom: 15px;
+    font-weight: normal;
+    font-size: 1.5em;
+  }
+
+  h3 {
+    margin: 0;
+    margin-top: 10px;
+    margin-bottom: 10px;
+  }
+
+  h4 {
+    color: #505050;
+    margin-top: 3px;
+    margin-left: 5px;
+    margin-bottom: 8px;
+  }
+
+  li {
+    margin: 0 0 0.3em;
+  }
+
+  code {
+    font-family: math;
+    color: darkgreen;
+    text-wrap: pretty;
+  }
+
+  /* ======= Style layout by ID ======= */
+
+  #callout-box {
+    margin: auto;
+    max-width: 800px;
+    font: 13px arial, sans-serif;
+    background-color: white;
+    border-style: solid;
+    border-width: 1px;
+    padding: 10px 25px;
+    box-shadow: 5px 5px 5px grey;
+    border-radius: 15px;
+  }
+
+  #important-box {
+    font-size: 0.9em;
+    font-family: system-ui;
+    line-height: 150%;
+    word-break: break-word;
+    #padding: 4px;
+    padding-top: 5px;
+    padding-bottom: 5px;
+    padding-left: 10px;
+    padding-right: 10px;
+    background-color: #fcb8a1;
+    border-radius: 5px;
+    border-width: 2px;
+    border-style: solid;
+  }
+
+  #tldr-response-box {
+    font-size: 0.8em;
+    font-family: sans-serif;
+    line-height: 140%;
+    margin-top: 10px;
+    margin-bottom: 25px;
+    border-width: 2px;
+    border-style: solid;
+    padding-top: 5px;
+    padding-bottom: 5px;
+    padding-left: 10px;
+    padding-right: 10px;
+    background-color: #b1d8f1;
+    border-radius: 5px;
+    border-width: 2px;
+    border-style: solid;
+  }
+
+  #response-box {
+    font-size: 1.0em;
+    font-family: sans-serif;
+    line-height: 140%;
+    margin-top: 10px;
+  }
+  #suggested-questions {
+    font-family: sans-serif;
+    word-break: break-word;
+  }
+
+  #context-content{
+    background: #d7dbd7;
+    font-family: sans-serif;
+    word-break: break-all;
+  }
+
+  #context-pre {
+    font-size: small;
+    font-family: monospace;
+    text-wrap: pretty;
+    margin-top: 0.3px;
+  }
+
+  #probability-box {
+    font-size: small;
+    padding: 4px;
+    margin-bottom: 10px;
+  }
+
+  #grounding-box {
+    font-size: small;
+    padding: 4px;
+    word-break: break-all;
+  }
+
+  #grounding-pre {
+    font-size: small;
+    font-family: monospace;
+    text-wrap: pretty;
+  }
+
+  #reference-box {
+    font-size: 0.9em;
+    font-family: system-ui;
+    text-wrap: pretty;
+    word-break: break-all;
+    margin-bottom: 12px;
+    line-height: 1.5em;
+  }
+
+  #reference-box-no-aqa {
+    font-size: 0.9em;
+    font-family: system-ui;
+    text-wrap: pretty;
+    word-break: break-all;
+    line-height: 1.5em;
+  }
+
+  #aqa-content{
+    background: #9fc7db;
+    font-family: math;
+  }
+
+  #aqa-label{
+    background: #49a5d2;
+  }
+
+  #aqa-json {
+    font-family: system-ui;
+    font-size: small;
+    text-wrap: pretty;
+    word-break: break-all;
+    margin: 0;
+  }
+
+  #rewrite-buttons-box {
+    margin-top: 12px;
+  }
+
+  #feedback-buttons-box {
+    margin-top: 12px;
+  }
+
+  #answerable-span {
+    font-size: small;
+    font-family: system-ui;
+    float: right;
+    padding: 10px;
+  }
+
+  /* ======= Style by class ======= */
+
+  .hidden {
+    display: none;
+  }
+
+  .disable {
+    display: none;
+  }
+
+  .header-wrapper {
+    display: flex;
+  }
+
+  .loading {
+    font: 15px arial, sans-serif;
+    width: 100%;
+    margin-left: 12px;
+    color: #505050;
+    padding: 2px;
+  }
+
+  .notselected {
+    background-color: #303936e6;
+    padding-top: 3px;
+    padding-bottom: 5px;
+  }
+
+  .notselected:hover {
+    background-color: #121a17e6;
+    cursor:pointer;
+  }
+
+  #like-button.selected {
+    background-color: #1e6a9c;
+    padding-top: 7px;
+    padding-bottom: 7px;
+  }
+
+  #dislike-button.selected {
+    background-color: #CF5C3F;
+    padding-top: 7px;
+    padding-bottom: 7px;
+  }
+
+  .selected:hover {
+    background-color: #0a619a;
+    cursor:pointer;
+  }
+
+  .rewrite {
+    padding: 15px;
+    border: 2px solid #000;
+    margin-top: 6px;
+    border-radius: 15px;
+  }
+
+  .feedback {
+    padding: 15px;
+    border: 2px solid #000;
+    margin-top: 6px;
+    border-radius: 15px;
+  }
+
+  .question, .response, .response-text, .fact-checked-text {
+    max-width: 700px;
+    margin-left: 3px;
+  }
+
+  .full-response {
+    max-width: 700px;
+    margin-left: 10px;
+  }
+
+  .related-questions {
+    margin-bottom: 20px;
+    font-size: 0.9em;
+    line-height: 140%;
+  }
+
+  /* ======= Style buttons by ID ======= */
+
+  #rewrite-button {
+    border: 0;
+    background-color: #cf633ff2;
+    color: #fff;
+    padding: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+    margin-top: 0.3em;
+  }
+
+  #rewrite-button:hover {
+    background: #ce3705f2;
+    cursor:pointer;
+  }
+
+  #feedback-button {
+    border: 0;
+    background-color: #cf633ff2;
+    color: #fff;
+    padding: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+    margin-top: 0.3em;
+  }
+
+  #feedback-button:hover {
+    background: #ce3705f2;
+    cursor:pointer;
+  }
+
+  #like-button {
+    border: 0;
+    color: #fff;
+    padding-left: 7px;
+    padding-right: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+  }
+
+  #dislike-button {
+    border: 0;
+    color: #fff;
+    padding-left: 7px;
+    padding-right: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+  }
+
+  #submit-button {
+    border: 0;
+    background: none;
+    background-color: #CF5C3F;
+    color: #fff;
+    padding: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+  }
+
+  #submit-button:hover {
+    background: #ce3705f2;
+    cursor:pointer;
+  }
+
+  #submit-result {
+    color: #027f02d6;
+    font-family: fantasy;
+  }
+  #feedback-submit-button {
+    border: 0;
+    background: none;
+    background-color: #CF5C3F;
+    color: #fff;
+    padding: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+  }
+
+  #feedback-submit-button:hover {
+    background: #ce3705f2;
+    cursor:pointer;
+  }
+
+  #feedback-submit-result {
+    color: #027f02d6;
+    font-family: fantasy;
+  }
+
+  #edit-text-area {
+    font: 13px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
+    max-height: 500px;
+    max-width: -webkit-fill-available;
+    height: 300px;
+    width: 650px;
+    padding: 8px;
+  }
+
+  #feedback-text-area {
+    font: 13px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
+    max-height: 500px;
+    max-width: -webkit-fill-available;
+    height: 300px;
+    width: 580px;
+    padding: 8px;
+  }
+
+  #rewrite-question-header {
+    margin: 0;
+    margin-bottom: 5px;
+  }
+
+  #rewrite-response-header {
+    margin: 0;
+    margin-top: 10px;
+    margin-bottom: 5px;
+  }
+
+  #user-id {
+    margin: 0;
+    margin-top: 10px;
+    margin-bottom: 15px;
+  }
+
+  #fact-check-url {
+    margin: 0 0 0.7em;
+  }
+
+  #source-para {
+    margin: 0 0 0.7em;
+  }
+
+  #distance-para {
+    margin: 0 0 0.7em;
+    font: 11px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
+  }
+  /* ======= Search Box ======= */
+
+  .search {
+    border: 2px solid #CF5C3F;
+    overflow: auto;
+    max-width: 700px;
+    margin-top: 15px;
+    margin-left: 10px;
+    margin-bottom: 10px;
+    border-radius: 5px;
+  }
+
+  .search input[type="text"] {
+    border: 0;
+    width: calc(100% - 65px);
+    padding: 10px;
+  }
+
+  .search input[type="text"]:focus {
+    outline: 0;
+  }
+
+  .search input[type="submit"] {
+    border: 0;
+    background: none;
+    background-color: #CF5C3F;
+    color: #fff;
+    float: right;
+    padding: 10px;
+    -moz-border-radius-top-right: 5px;
+    -webkit-border-radius-top-right: 5px;
+    -moz-border-radius-bottom-right: 5px;
+    -webkit-border-radius-bottom-right: 5px;
+    cursor:pointer;
+  }
+
+  /* ======= Accordion ======= */
+
+  .accordion {
+    max-width: 65em;
+    #margin-bottom: 1em;
+  }
+
+  .accordion > input[type="checkbox"] {
+    position: absolute;
+    left: -100vw;
+  }
+
+  .accordion .content {
+    overflow-y: hidden;
+    height: 0;
+    transition: height 0.3s ease;
+  }
+
+  .accordion .reference-content {
+    font-size: 15px;
+    font-family: serif;
+  }
+
+  .accordion > input[type="checkbox"]:checked ~ .content {
+    height: auto;
+    overflow: visible;
+    padding: 15px;
+    border: 2px solid #000;
+    margin-top: 6px;
+    border-radius: 15px;
+  }
+
+  .accordion .handle {
+    margin: 0;
+    font-size: 1.0em;
+  }
+
+  .accordion label {
+    display: block;
+    font-weight: normal;
+    border: 2px solid #000;
+    #padding: 12px;
+    background: #4490b8ab;
+    #border-radius: 15px;
+    padding: 5px;
+    #background: #027f023b;
+    border-radius: 10px;
+  }
+
+  .accordion label:hover,
+  .accordion label:focus {
+    background: #d9d9d9;
+    cursor:pointer;
+  }
+
+  .accordion .handle label::before {
+    font-family: fontawesome, sans-serif;
+    display: inline-block;
+    content: "\2964";
+    margin-right: 10px;
+    font-size: .58em;
+    line-height: 1.556em;
+    vertical-align: middle;
+  }
+
+  .accordion > input[type="checkbox"]:checked ~ .handle label::before {
+    content: "\2965";
+  }
+
+  .accordion p:last-child {
+    margin-bottom: 0;
+  }
+
+  /* ======= Accordion Source ======= */
+
+  .accordion-source {
+    max-width: 65em;
+    margin-bottom: 1em;
+  }
+
+  .accordion-source > input[type="checkbox"] {
+    position: absolute;
+    left: -100vw;
+  }
+
+  .accordion-source .content {
+    overflow-y: hidden;
+    height: 0;
+    transition: height 0.3s ease;
+  }
+
+  .accordion-source .content{
+    font-size: 13px;
+  }
+
+  .accordion-source > input[type="checkbox"]:checked ~ .content {
+    height: auto;
+    overflow: visible;
+    padding: 15px;
+    border: 2px solid #000;
+    margin-top: 6px;
+    border-radius: 15px;
+  }
+
+  .accordion-source .handle {
+    margin: 0;
+    font-size: 1em;
+    line-height: 1.2em;
+  }
+
+  .accordion-source label {
+    display: block;
+    font-weight: normal;
+    border: 1px solid #000;
+    padding: 6px;
+    background: #4490b8ab;
+    border-radius: 15px;
+  }
+
+  .accordion-source label:hover,
+  .accordion-source label:focus {
+    background: #d9d9d9;
+    cursor:pointer;
+  }
+
+  .accordion-source .handle label::before {
+    font-family: fontawesome, sans-serif;
+    display: inline-block;
+    content: "\2964";
+    margin-right: 10px;
+    font-size: .58em;
+    line-height: .556em;
+    vertical-align: middle;
+  }
+
+  .accordion-source > input[type="checkbox"]:checked ~ .handle label::before {
+    content: "\2965";
+  }
+
+  .accordion-source p:last-child {
+    margin-bottom: 0;
+  }
+
+/* Loader animation */
+/* Source: https://css-loaders.com/classic/ */
+.loader {
+  width: fit-content;
+  font-family: monospace;
+  font-size: 14px;
+  margin-left: 13px;
+  clip-path: inset(0 3ch 0 0);
+  animation: animation 1s steps(4) infinite;
+}
+.loader:before {
+  content:"Generating a response..."
+}
+@keyframes animation {to{clip-path: inset(0 -1ch 0 0)}}
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui-widget-pro.css b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui-widget-pro.css
new file mode 100644
index 000000000..b343fb6e1
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui-widget-pro.css
@@ -0,0 +1,635 @@
+/**
+ * Copyright 2023 Google LLC
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ======= General style for HTML elements ======= */
+
+body {
+    font: 16px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
+    color: #333;
+    font-weight: 300;
+    max-width: 960px;
+    margin: auto;
+    background-color: white;
+    padding-top: 15px;
+    padding-bottom: 15px;
+  }
+
+  a {
+    color: #0a619a;
+  }
+
+  p {
+    margin: 0 0 1em;
+    line-height: 130%;
+  }
+
+  h1 {
+    margin: 0 0 0.5em;
+    font-weight: 500;
+    font-size: 1.3em;
+    margin-top: 0.1em;
+    margin-left: 1.0em;
+    margin-bottom: 0.9em;
+  }
+
+  h2 {
+    margin: 0;
+    margin-top: 15px;
+    margin-bottom: 10px;
+    font-weight: normal;
+    font-size: 1.4em;
+  }
+
+  h3 {
+    margin: 0;
+    margin-top: 10px;
+    margin-bottom: 10px;
+  }
+
+  h4 {
+    color: #505050;
+    margin-top: 3px;
+    margin-left: 5px;
+    margin-bottom: 8px;
+  }
+
+  li {
+    margin: 0 0 0.3em;
+  }
+
+  code {
+    font-family: math;
+    color: darkgreen;
+    text-wrap: pretty;
+  }
+
+  /* ======= Style layout by ID ======= */
+
+  #iframe-box {
+    margin: 0px;
+    max-width: 760px;
+    font: 15px arial, sans-serif;
+    background-color: white;
+    padding-bottom: 0px;
+    padding-left: 0px;
+  }
+
+  #callout-box {
+    margin: auto;
+    max-width: 800px;
+    font: 13px arial, sans-serif;
+    background-color: white;
+    border-style: solid;
+    border-width: 1px;
+    padding: 10px 25px;
+    box-shadow: 5px 5px 5px grey;
+    border-radius: 15px;
+  }
+
+  #important-box {
+    font-size: 0.9em;
+    font-family: system-ui;
+    line-height: 150%;
+    word-break: break-word;
+    #padding: 4px;
+    padding-top: 5px;
+    padding-bottom: 5px;
+    padding-left: 10px;
+    padding-right: 10px;
+    background-color: #fcb8a1;
+    border-radius: 5px;
+    border-width: 2px;
+    border-style: solid;
+  }
+
+  #tldr-response-box {
+    font-size: 0.8em;
+    font-family: sans-serif;
+    line-height: 140%;
+    margin-top: 10px;
+    margin-bottom: 25px;
+    border-width: 2px;
+    border-style: solid;
+    padding-top: 5px;
+    padding-bottom: 5px;
+    padding-left: 10px;
+    padding-right: 10px;
+    background-color: #b1d8f1;
+    border-radius: 5px;
+    border-width: 2px;
+    border-style: solid;
+  }
+
+  #response-box {
+    font-size: 1.0em;
+    font-family: sans-serif;
+    line-height: 140%;
+    margin-top: 10px;
+  }
+  #suggested-questions {
+    font-family: sans-serif;
+    word-break: break-word;
+  }
+
+  #context-content{
+    background: #d7dbd7;
+    font-family: sans-serif;
+    word-break: break-all;
+  }
+
+  #context-pre {
+    font-size: small;
+    font-family: monospace;
+    text-wrap: pretty;
+    margin-top: 0.3px;
+  }
+
+  #probability-box {
+    font-size: small;
+    padding: 4px;
+    margin-bottom: 10px;
+  }
+
+  #grounding-box {
+    font-size: small;
+    padding: 4px;
+    word-break: break-all;
+  }
+
+  #grounding-pre {
+    font-size: small;
+    font-family: monospace;
+    text-wrap: pretty;
+  }
+
+  #reference-box {
+    font-size: 0.9em;
+    font-family: system-ui;
+    text-wrap: pretty;
+    word-break: break-all;
+    margin-bottom: 12px;
+    line-height: 1.5em;
+  }
+
+  #reference-box-no-aqa {
+    font-size: 0.9em;
+    font-family: system-ui;
+    text-wrap: pretty;
+    word-break: break-all;
+    line-height: 1.5em;
+  }
+
+  #aqa-content{
+    background: #9fc7db;
+    font-family: math;
+  }
+
+  #aqa-label{
+    background: #49a5d2;
+  }
+
+  #aqa-json {
+    font-family: system-ui;
+    font-size: small;
+    text-wrap: pretty;
+    word-break: break-all;
+    margin: 0;
+  }
+
+  #rewrite-buttons-box {
+    margin-top: 12px;
+  }
+
+  #feedback-buttons-box {
+    margin-top: 12px;
+  }
+
+  #answerable-span {
+    font-size: small;
+    font-family: system-ui;
+    float: right;
+    padding: 10px;
+  }
+
+  /* ======= Style by class ======= */
+
+  .hidden {
+    display: none;
+  }
+
+  .disable {
+    display: none;
+  }
+
+  .header-wrapper {
+    display: flex;
+  }
+
+  .loading {
+    font: 15px arial, sans-serif;
+    width: 100%;
+    margin-left: 12px;
+    color: #505050;
+    padding: 2px;
+  }
+
+  .notselected {
+    background-color: #303936e6;
+    padding-top: 3px;
+    padding-bottom: 5px;
+  }
+
+  .notselected:hover {
+    background-color: #121a17e6;
+    cursor:pointer;
+  }
+
+  #like-button.selected {
+    background-color: #1e6a9c;
+    padding-top: 7px;
+    padding-bottom: 7px;
+  }
+
+  #dislike-button.selected {
+    background-color: #CF5C3F;
+    padding-top: 7px;
+    padding-bottom: 7px;
+  }
+
+  .selected:hover {
+    background-color: #0a619a;
+    cursor:pointer;
+  }
+
+  .rewrite {
+    padding: 15px;
+    border: 2px solid #000;
+    margin-top: 6px;
+    border-radius: 15px;
+  }
+
+  .feedback {
+    padding: 15px;
+    border: 2px solid #000;
+    margin-top: 6px;
+    border-radius: 15px;
+  }
+
+  .question, .response, .response-text, .fact-checked-text {
+    max-width: 700px;
+    margin-left: 3px;
+  }
+
+  .full-response {
+    max-width: 700px;
+    margin-left: 10px;
+  }
+
+  .related-questions {
+    margin-bottom: 20px;
+    font-size: 0.9em;
+    line-height: 140%;
+  }
+
+  /* ======= Style buttons by ID ======= */
+
+  #rewrite-button {
+    border: 0;
+    background-color: #cf633ff2;
+    color: #fff;
+    padding: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+    margin-top: 0.3em;
+  }
+
+  #rewrite-button:hover {
+    background: #ce3705f2;
+    cursor:pointer;
+  }
+
+  #feedback-button {
+    border: 0;
+    background-color: #cf633ff2;
+    color: #fff;
+    padding: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+    margin-top: 0.3em;
+  }
+
+  #feedback-button:hover {
+    background: #ce3705f2;
+    cursor:pointer;
+  }
+
+  #like-button {
+    border: 0;
+    color: #fff;
+    padding-left: 7px;
+    padding-right: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+  }
+
+  #dislike-button {
+    border: 0;
+    color: #fff;
+    padding-left: 7px;
+    padding-right: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+  }
+
+  #submit-button {
+    border: 0;
+    background: none;
+    background-color: #CF5C3F;
+    color: #fff;
+    padding: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+  }
+
+  #submit-button:hover {
+    background: #ce3705f2;
+    cursor:pointer;
+  }
+
+  #submit-result {
+    color: #027f02d6;
+    font-family: fantasy;
+  }
+  #feedback-submit-button {
+    border: 0;
+    background: none;
+    background-color: #CF5C3F;
+    color: #fff;
+    padding: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+  }
+
+  #feedback-submit-button:hover {
+    background: #ce3705f2;
+    cursor:pointer;
+  }
+
+  #feedback-submit-result {
+    color: #027f02d6;
+    font-family: fantasy;
+  }
+
+  #edit-text-area {
+    font: 13px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
+    max-height: 500px;
+    max-width: -webkit-fill-available;
+    height: 300px;
+    width: 650px;
+    padding: 8px;
+  }
+
+  #feedback-text-area {
+    font: 13px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
+    max-height: 500px;
+    max-width: -webkit-fill-available;
+    height: 300px;
+    width: 580px;
+    padding: 8px;
+  }
+
+  #rewrite-question-header {
+    margin: 0;
+    margin-bottom: 5px;
+  }
+
+  #rewrite-response-header {
+    margin: 0;
+    margin-top: 10px;
+    margin-bottom: 5px;
+  }
+
+  #user-id {
+    margin: 0;
+    margin-top: 10px;
+    margin-bottom: 15px;
+  }
+
+  #fact-check-url {
+    margin: 0 0 0.7em;
+  }
+
+  #source-para {
+    margin: 0 0 0.7em;
+  }
+
+  #distance-para {
+    margin: 0 0 0.7em;
+    font: 11px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
+  }
+  /* ======= Search Box ======= */
+
+  .search {
+    border: 2px solid #CF5C3F;
+    overflow: auto;
+    max-width: 700px;
+    margin-top: 15px;
+    margin-left: 10px;
+    margin-bottom: 10px;
+    border-radius: 5px;
+  }
+
+  .search input[type="text"] {
+    border: 0;
+    width: calc(100% - 65px);
+    padding: 10px;
+  }
+
+  .search input[type="text"]:focus {
+    outline: 0;
+  }
+
+  .search input[type="submit"] {
+    border: 0;
+    background: none;
+    background-color: #CF5C3F;
+    color: #fff;
+    float: right;
+    padding: 10px;
+    -moz-border-radius-top-right: 5px;
+    -webkit-border-radius-top-right: 5px;
+    -moz-border-radius-bottom-right: 5px;
+    -webkit-border-radius-bottom-right: 5px;
+    cursor:pointer;
+  }
+
+  /* ======= Accordion ======= */
+
+  .accordion {
+    max-width: 65em;
+    #margin-bottom: 1em;
+  }
+
+  .accordion > input[type="checkbox"] {
+    position: absolute;
+    left: -100vw;
+  }
+
+  .accordion .content {
+    overflow-y: hidden;
+    height: 0;
+    transition: height 0.3s ease;
+  }
+
+  .accordion .reference-content {
+    font-size: 15px;
+    font-family: serif;
+  }
+
+  .accordion > input[type="checkbox"]:checked ~ .content {
+    height: auto;
+    overflow: visible;
+    padding: 15px;
+    border: 2px solid #000;
+    margin-top: 6px;
+    border-radius: 15px;
+  }
+
+  .accordion .handle {
+    margin: 0;
+    font-size: 1.0em;
+  }
+
+  .accordion label {
+    display: block;
+    font-weight: normal;
+    border: 2px solid #000;
+    #padding: 12px;
+    background: #4490b8ab;
+    #border-radius: 15px;
+    padding: 5px;
+    #background: #027f023b;
+    border-radius: 10px;
+  }
+
+  .accordion label:hover,
+  .accordion label:focus {
+    background: #d9d9d9;
+    cursor:pointer;
+  }
+
+  .accordion .handle label::before {
+    font-family: fontawesome, sans-serif;
+    display: inline-block;
+    content: "\2964";
+    margin-right: 10px;
+    font-size: .58em;
+    line-height: 1.556em;
+    vertical-align: middle;
+  }
+
+  .accordion > input[type="checkbox"]:checked ~ .handle label::before {
+    content: "\2965";
+  }
+
+  .accordion p:last-child {
+    margin-bottom: 0;
+  }
+
+  /* ======= Accordion Source ======= */
+
+  .accordion-source {
+    max-width: 65em;
+    margin-bottom: 1em;
+  }
+
+  .accordion-source > input[type="checkbox"] {
+    position: absolute;
+    left: -100vw;
+  }
+
+  .accordion-source .content {
+    overflow-y: hidden;
+    height: 0;
+    transition: height 0.3s ease;
+  }
+
+  .accordion-source .content{
+    font-size: 13px;
+  }
+
+  .accordion-source > input[type="checkbox"]:checked ~ .content {
+    height: auto;
+    overflow: visible;
+    padding: 15px;
+    border: 2px solid #000;
+    margin-top: 6px;
+    border-radius: 15px;
+  }
+
+  .accordion-source .handle {
+    margin: 0;
+    font-size: 1em;
+    line-height: 1.2em;
+  }
+
+  .accordion-source label {
+    display: block;
+    font-weight: normal;
+    border: 1px solid #000;
+    padding: 6px;
+    background: #4490b8ab;
+    border-radius: 15px;
+  }
+
+  .accordion-source label:hover,
+  .accordion-source label:focus {
+    background: #d9d9d9;
+    cursor:pointer;
+  }
+
+  .accordion-source .handle label::before {
+    font-family: fontawesome, sans-serif;
+    display: inline-block;
+    content: "\2964";
+    margin-right: 10px;
+    font-size: .58em;
+    line-height: .556em;
+    vertical-align: middle;
+  }
+
+  .accordion-source > input[type="checkbox"]:checked ~ .handle label::before {
+    content: "\2965";
+  }
+
+  .accordion-source p:last-child {
+    margin-bottom: 0;
+  }
+
+/* Loader animation */
+/* Source: https://css-loaders.com/classic/ */
+.loader {
+  width: fit-content;
+  font-family: monospace;
+  font-size: 14px;
+  margin-left: 13px;
+  clip-path: inset(0 3ch 0 0);
+  animation: animation 1s steps(4) infinite;
+}
+.loader:before {
+  content:"Generating a response..."
+}
+@keyframes animation {to{clip-path: inset(0 -1ch 0 0)}}
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui-widget.css b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui-widget.css
new file mode 100644
index 000000000..9658ef6a7
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui-widget.css
@@ -0,0 +1,624 @@
+/**
+ * Copyright 2023 Google LLC
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ======= General style for HTML elements ======= */
+
+body {
+    font: 16px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
+    color: #333;
+    font-weight: 300;
+    max-width: 960px;
+    margin: auto;
+    background-color: white;
+    padding-top: 15px;
+    padding-bottom: 15px;
+  }
+
+  a {
+    color: #0a619a;
+  }
+
+  p {
+    margin: 0 0 1em;
+    line-height: 130%;
+  }
+
+  h1 {
+    margin: 0 0 0.5em;
+    font-weight: 500;
+    font-size: 1.3em;
+    margin-top: 0.1em;
+    margin-left: 1.0em;
+    margin-bottom: 0.9em;
+  }
+
+  h2 {
+    margin: 0;
+    margin-top: 15px;
+    margin-bottom: 10px;
+    font-weight: normal;
+    font-size: 1.4em;
+  }
+
+  h3 {
+    margin: 0;
+    margin-top: 10px;
+    margin-bottom: 10px;
+  }
+
+  h4 {
+    color: #505050;
+    margin-top: 3px;
+    margin-left: 5px;
+    margin-bottom: 8px;
+  }
+
+  li {
+    margin: 0 0 0.3em;
+  }
+
+  code {
+    font-family: math;
+    color: darkgreen;
+    text-wrap: pretty;
+  }
+
+  /* ======= Style layout by ID ======= */
+
+  #iframe-box {
+    margin: 0px;
+    max-width: 760px;
+    font: 15px arial, sans-serif;
+    background-color: white;
+    padding-bottom: 0px;
+    padding-left: 0px;
+  }
+
+  #callout-box {
+    margin: auto;
+    max-width: 800px;
+    font: 13px arial, sans-serif;
+    background-color: white;
+    border-style: solid;
+    border-width: 1px;
+    padding: 10px 25px;
+    box-shadow: 5px 5px 5px grey;
+    border-radius: 15px;
+  }
+
+  #important-box {
+    font-size: 0.9em;
+    font-family: system-ui;
+    word-break: break-word;
+    line-height: 150%;
+    word-break: break-word;
+    padding: 4px;
+  }
+
+  #response-box {
+    font-size: 1.0em;
+    font-family: sans-serif;
+    line-height: 140%;
+    margin-top: 10px;
+  }
+
+  #suggested-questions {
+    font-family: sans-serif;
+    word-break: break-word;
+  }
+
+  #source-pages {
+    font-family: sans-serif;
+    word-break: break-all;
+  }
+
+  #context-content{
+    background: #d7dbd7;
+    font-family: sans-serif;
+    word-break: break-all;
+  }
+
+  #context-pre {
+    font-size: small;
+    font-family: monospace;
+    text-wrap: pretty;
+    margin-top: 0.3px;
+  }
+
+  #probability-box {
+    font-size: small;
+    padding: 4px;
+    margin-bottom: 10px;
+  }
+
+  #grounding-box {
+    font-size: small;
+    padding: 4px;
+    word-break: break-all;
+  }
+
+  #grounding-pre {
+    font-size: small;
+    font-family: monospace;
+    text-wrap: pretty;
+  }
+
+  #reference-box {
+    font-size: 0.9em;
+    font-family: system-ui;
+    text-wrap: pretty;
+    word-break: break-all;
+    margin-bottom: 12px;
+    line-height: 1.5em;
+  }
+
+  #reference-box-no-aqa {
+    font-size: 0.9em;
+    font-family: system-ui;
+    text-wrap: pretty;
+    word-break: break-all;
+    line-height: 1.5em;
+  }
+
+  #aqa-content{
+    background: #9fc7db;
+    font-family: math;
+  }
+
+  #aqa-label{
+    background: #49a5d2;
+  }
+
+  #aqa-json {
+    font-family: system-ui;
+    font-size: small;
+    text-wrap: pretty;
+    word-break: break-all;
+    margin: 0;
+  }
+
+  #rewrite-buttons-box {
+    margin-top: 12px;
+  }
+
+  #feedback-buttons-box {
+    margin-top: 12px;
+  }
+
+  #answerable-span {
+    font-size: small;
+    font-family: system-ui;
+    float: right;
+    padding: 10px;
+  }
+
+  /* ======= Style by class ======= */
+
+  .hidden {
+    display: none;
+  }
+
+  .disable {
+    display: none;
+  }
+
+  .header-wrapper {
+    display: flex;
+  }
+
+  .loading {
+    font: 15px arial, sans-serif;
+    width: 100%;
+    margin-left: 12px;
+    color: #505050;
+    padding: 2px;
+  }
+
+  .notselected {
+    background-color: #303936e6;
+    padding-top: 3px;
+    padding-bottom: 5px;
+  }
+
+  .notselected:hover {
+    background-color: #121a17e6;
+    cursor:pointer;
+  }
+
+  #like-button.selected {
+    background-color: #1e6a9c;
+    padding-top: 7px;
+    padding-bottom: 7px;
+  }
+
+  #dislike-button.selected {
+    background-color: #CF5C3F;
+    padding-top: 7px;
+    padding-bottom: 7px;
+  }
+
+  .selected:hover {
+    background-color: #0a619a;
+    cursor:pointer;
+  }
+
+  .rewrite {
+    padding: 15px;
+    border: 2px solid #000;
+    margin-top: 6px;
+    border-radius: 15px;
+  }
+
+  .feedback {
+    padding: 15px;
+    border: 2px solid #000;
+    margin-top: 6px;
+    border-radius: 15px;
+  }
+
+  .question, .response, .response-text, .fact-checked-text {
+    max-width: 700px;
+    margin-left: 3px;
+  }
+
+  .full-response {
+    max-width: 700px;
+    margin-left: 10px;
+  }
+
+  .related-questions {
+    margin-bottom: 20px;
+    font-size: 0.9em;
+    line-height: 140%;
+  }
+
+  .relevant-sources {
+    margin-bottom: 20px;
+    font-size: 0.9em;
+    line-height: 140%;
+  }
+
+  /* ======= Style buttons by ID ======= */
+
+  #rewrite-button {
+    border: 0;
+    background-color: #cf633ff2;
+    color: #fff;
+    padding: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+    margin-top: 0.3em;
+  }
+
+  #rewrite-button:hover {
+    background: #ce3705f2;
+    cursor:pointer;
+  }
+
+  #feedback-button {
+    border: 0;
+    background-color: #cf633ff2;
+    color: #fff;
+    padding: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+    margin-top: 0.3em;
+  }
+
+  #feedback-button:hover {
+    background: #ce3705f2;
+    cursor:pointer;
+  }
+
+  #like-button {
+    border: 0;
+    color: #fff;
+    padding-left: 7px;
+    padding-right: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+  }
+
+  #dislike-button {
+    border: 0;
+    color: #fff;
+    padding-left: 7px;
+    padding-right: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+  }
+
+  #submit-button {
+    border: 0;
+    background: none;
+    background-color: #CF5C3F;
+    color: #fff;
+    padding: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+  }
+
+  #submit-button:hover {
+    background: #ce3705f2;
+    cursor:pointer;
+  }
+
+  #submit-result {
+    color: #027f02d6;
+    font-family: fantasy;
+  }
+
+  #feedback-submit-button {
+    border: 0;
+    background: none;
+    background-color: #CF5C3F;
+    color: #fff;
+    padding: 7px;
+    border-radius: 5px;
+    cursor:pointer;
+  }
+
+  #feedback-submit-button:hover {
+    background: #ce3705f2;
+    cursor:pointer;
+  }
+
+  #feedback-submit-result {
+    color: #027f02d6;
+    font-family: fantasy;
+  }
+
+  #edit-text-area {
+    font: 13px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
+    max-height: 500px;
+    max-width: -webkit-fill-available;
+    height: 300px;
+    width: 580px;
+    padding: 8px;
+  }
+
+  #feedback-text-area {
+    font: 13px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
+    max-height: 500px;
+    max-width: -webkit-fill-available;
+    height: 300px;
+    width: 580px;
+    padding: 8px;
+  }
+
+  #rewrite-question-header {
+    margin: 0;
+    margin-bottom: 5px;
+  }
+
+  #rewrite-response-header {
+    margin: 0;
+    margin-top: 10px;
+    margin-bottom: 5px;
+  }
+
+  #user-id {
+    margin: 0;
+    margin-top: 10px;
+    margin-bottom: 15px;
+  }
+
+  #fact-check-url {
+    margin: 0 0 0.7em;
+  }
+
+  #source-para {
+    margin: 0 0 0.7em;
+  }
+
+  #distance-para {
+    margin: 0 0 0.7em;
+    font: 11px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
+  }
+  /* ======= Search Box ======= */
+
+  .search {
+    border: 2px solid #CF5C3F;
+    overflow: auto;
+    max-width: 600px;
+    margin-top: 15px;
+    margin-left: 10px;
+    margin-bottom: 10px;
+    border-radius: 5px;
+  }
+
+  .search input[type="text"] {
+    border: 0;
+    width: calc(100% - 65px);
+    padding: 10px;
+  }
+
+  .search input[type="text"]:focus {
+    outline: 0;
+  }
+
+  .search input[type="submit"] {
+    border: 0;
+    background: none;
+    background-color: #CF5C3F;
+    color: #fff;
+    float: right;
+    padding: 10px;
+    -moz-border-radius-top-right: 5px;
+    -webkit-border-radius-top-right: 5px;
+    -moz-border-radius-bottom-right: 5px;
+    -webkit-border-radius-bottom-right: 5px;
+    cursor:pointer;
+  }
+
+  /* ======= Accordion ======= */
+
+  .accordion {
+    max-width: 65em;
+    #margin-bottom: 1em;
+  }
+
+  .accordion > input[type="checkbox"] {
+    position: absolute;
+    left: -100vw;
+  }
+
+  .accordion .content {
+    overflow-y: hidden;
+    height: 0;
+    transition: height 0.3s ease;
+  }
+
+  .accordion .reference-content {
+    font-size: 15px;
+    font-family: serif;
+  }
+
+  .accordion > input[type="checkbox"]:checked ~ .content {
+    height: auto;
+    overflow: visible;
+    padding: 15px;
+    border: 2px solid #000;
+    margin-top: 6px;
+    border-radius: 15px;
+  }
+
+  .accordion .handle {
+    margin: 0;
+    font-size: 1.0em;
+  }
+
+  .accordion label {
+    display: block;
+    font-weight: normal;
+    border: 2px solid #000;
+    #padding: 12px;
+    background: #4490b8ab;
+    #border-radius: 15px;
+    padding: 5px;
+    #background: #027f023b;
+    border-radius: 10px;
+  }
+
+  .accordion label:hover,
+  .accordion label:focus {
+    background: #d9d9d9;
+    cursor:pointer;
+  }
+
+  .accordion .handle label::before {
+    font-family: fontawesome, sans-serif;
+    display: inline-block;
+    content: "\2964";
+    margin-right: 10px;
+    font-size: .58em;
+    line-height: 1.556em;
+    vertical-align: middle;
+  }
+
+  .accordion > input[type="checkbox"]:checked ~ .handle label::before {
+    content: "\2965";
+  }
+
+  .accordion p:last-child {
+    margin-bottom: 0;
+  }
+
+  /* ======= Accordion Source ======= */
+
+  .accordion-source {
+    max-width: 65em;
+    margin-bottom: 1em;
+  }
+
+  .accordion-source > input[type="checkbox"] {
+    position: absolute;
+    left: -100vw;
+  }
+
+  .accordion-source .content {
+    overflow-y: hidden;
+    height: 0;
+    transition: height 0.3s ease;
+  }
+
+  .accordion-source .content{
+    font-size: 13px;
+  }
+
+  .accordion-source > input[type="checkbox"]:checked ~ .content {
+    height: auto;
+    overflow: visible;
+    padding: 15px;
+    border: 2px solid #000;
+    margin-top: 6px;
+    border-radius: 15px;
+  }
+
+  .accordion-source .handle {
+    margin: 0;
+    font-size: 1em;
+    line-height: 1.2em;
+  }
+
+  .accordion-source label {
+    display: block;
+    font-weight: normal;
+    border: 1px solid #000;
+    padding: 6px;
+    background: #4490b8ab;
+    border-radius: 15px;
+  }
+
+  .accordion-source label:hover,
+  .accordion-source label:focus {
+    background: #d9d9d9;
+    cursor:pointer;
+  }
+
+  .accordion-source .handle label::before {
+    font-family: fontawesome, sans-serif;
+    display: inline-block;
+    content: "\2964";
+    margin-right: 10px;
+    font-size: .58em;
+    line-height: .556em;
+    vertical-align: middle;
+  }
+
+  .accordion-source > input[type="checkbox"]:checked ~ .handle label::before {
+    content: "\2965";
+  }
+
+  .accordion-source p:last-child {
+    margin-bottom: 0;
+  }
+
+/* Loader animation */
+/* Source: https://css-loaders.com/classic/ */
+.loader {
+  width: fit-content;
+  font-family: monospace;
+  font-size: 14px;
+  margin-left: 13px;
+  clip-path: inset(0 3ch 0 0);
+  animation: animation 1s steps(4) infinite;
+}
+.loader:before {
+  content:"Generating a response..."
+}
+@keyframes animation {to{clip-path: inset(0 -1ch 0 0)}}
+
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/admin/debugs.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/admin/debugs.html
index 4a04c62e3..76ab6130e 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/admin/debugs.html
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/admin/debugs.html
@@ -2,7 +2,7 @@
 <html lang="en">
 <head>
     <meta charset="UTF-8">
-    <link rel="stylesheet" href="{{ url_for('static', filename='css/style-widget.css') }}">
+    <link rel="stylesheet" href="{{ url_for('static', filename='css/style-chatui-widget.css') }}">
     <link rel="stylesheet" href="{{ url_for('static', filename='css/chatbox.css') }}">
     <link rel="stylesheet" href="{{ url_for('static', filename='css/style-logs.css') }}">
     <link rel="shortcut icon" href="{{ url_for('static', filename='images/favicon.png') }}">
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/admin/logs.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/admin/logs.html
index 7d1323833..77f84f136 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/admin/logs.html
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/admin/logs.html
@@ -2,7 +2,7 @@
 <html lang="en">
 <head>
     <meta charset="UTF-8">
-    <link rel="stylesheet" href="{{ url_for('static', filename='css/style-widget.css') }}">
+    <link rel="stylesheet" href="{{ url_for('static', filename='css/style-chatui-widget.css') }}">
     <link rel="stylesheet" href="{{ url_for('static', filename='css/chatbox.css') }}">
     <link rel="stylesheet" href="{{ url_for('static', filename='css/style-logs.css') }}">
     <link rel="shortcut icon" href="{{ url_for('static', filename='images/favicon.png') }}">
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-1.5/base.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-full/base.html
similarity index 96%
rename from examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-1.5/base.html
rename to examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-full/base.html
index 4ddd096b2..643e779c7 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-1.5/base.html
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-full/base.html
@@ -2,7 +2,7 @@
 <html lang="en">
 <head>
     <meta charset="UTF-8">
-    <link rel="stylesheet" href="{{ url_for('static', filename='css/style-chatui-1.5.css') }}">
+    <link rel="stylesheet" href="{{ url_for('static', filename='css/style-chatui-full.css') }}">
     <link rel="stylesheet" href="{{ url_for('static', filename='css/chatbox.css') }}">
     <link rel="shortcut icon" href="{{ url_for('static', filename='images/favicon.png') }}">
     <title>{{ product }} ChatBot</title>
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-1.5/index.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-full/index.html
similarity index 83%
rename from examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-1.5/index.html
rename to examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-full/index.html
index 0a08dc99e..5b57140e4 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-1.5/index.html
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-full/index.html
@@ -1,4 +1,4 @@
-{% extends 'chatui-1.5/base.html' %}
+{% extends 'chatui-full/base.html' %}
 
 {% block content %}
   <div class="search">
@@ -10,7 +10,7 @@
   <div class="loader hidden" id="loading-div"></div>
   {% if response %}
   <div class="full-response">
-    {% include 'chatui-1.5/result.html' %}
+    {% include 'chatui-full/result.html' %}
   </div>
   {% endif %}
 {% endblock %}
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-1.5/result.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-full/result.html
similarity index 100%
rename from examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-1.5/result.html
rename to examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-full/result.html
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget-pro/base.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget-pro/base.html
new file mode 100644
index 000000000..ec22387df
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget-pro/base.html
@@ -0,0 +1,28 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <link rel="stylesheet" href="{{ url_for('static', filename='css/style-chatui-widget-pro.css') }}">
+    <link rel="stylesheet" href="{{ url_for('static', filename='css/chatbox.css') }}">
+    <link rel="shortcut icon" href="{{ url_for('static', filename='images/favicon.png') }}">
+    <title>{{ product }} ChatBot</title>
+</head>
+<body>
+  <div id='iframe-box'>
+    <div name="title"><h1>{{ product }} Docs Agent 1.5</h1></div>
+    <div id='chat-border'>
+      <div class="body-content">
+        {% block content %} {% endblock %}
+      </div>
+    </div>
+  </div>
+  <br>
+  <br>
+  <script>
+    let urlRewrite = "{{ server_url }}rewrite";
+    let urlFeedback = "{{ server_url }}feedback";
+    let urlLike = "{{ server_url }}like";
+  </script>
+  <script src="{{ url_for('static', filename='javascript/app.js') }}"></script>
+</body>
+</html>
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget-pro/index.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget-pro/index.html
new file mode 100644
index 000000000..6c084fb06
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget-pro/index.html
@@ -0,0 +1,16 @@
+{% extends 'chatui-widget-pro/base.html' %}
+
+{% block content %}
+  <div class="search">
+    <form class="search-form" method="POST" action="/result">
+      <input type="text" name="question" placeholder="Ask me about topics related to {{product}}.">
+      <input type="submit" value="Ask" id="ask-button">
+    </form>
+  </div>
+  <div class="loader hidden" id="loading-div"></div>
+  {% if response %}
+  <div class="full-response">
+    {% include 'chatui-widget-pro/result.html' %}
+  </div>
+  {% endif %}
+{% endblock %}
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget-pro/result.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget-pro/result.html
new file mode 100644
index 000000000..e2c7ed471
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget-pro/result.html
@@ -0,0 +1,218 @@
+<!--Sets defaults-->
+{% if search_result[0] %}
+  {% set check_url = search_result[0].section.url %}
+  {% set probability = search_result[0].probability %}
+{% else %}
+  {% set check_url = "None" %}
+  {% set probability = "0.0" %}
+{% endif %}
+
+{% if aqa_response_in_html != "" %}
+  <!--Gemini AQA template-->
+  <div class="question">
+    <h2>{{ question | replace("+", " ") | replace("%3F", "?")}}</h2>
+  </div>
+  {% if probability|float > 0.7 %}
+  <div class="important-box" id="important-box">
+    <b>Important:</b> The answer below is generated by Gemini 1.5.
+    To verify this answer, please visit: {{named_link_html(check_url, check_url) | safe}}
+  </div>
+  <div class="response-text" id="tldr-response-box">
+    <h3>TL;DR</h3>
+    <span id="gemini-response">
+      {{ md_to_html(response) | safe }}
+    </span>
+  </div>
+  <div class="response-text" id="response-box">
+    <span id="gemini-response">
+      {{ md_to_html(summary_response) | safe }}
+    </span>
+  </div>
+  <div class="related-questions">
+    <h3>Suggested follow-up questions</h3>
+    <span id="suggested-questions">
+      {{ related_questions | safe }}
+    </span>
+  </div>
+  <div class="hidden">
+  <section class="accordion">
+    <input type="checkbox" name="collapse" id="handle1">
+    <h2 class="handle">
+      <label for="handle1">Context</label>
+    </h2>
+    <div class="content" id="context-content">
+      <div id="probability-box">
+        <b>Answerable probability</b>: {{"%.6f"|format(probability|float)}}
+      </div>
+      <div id="grounding-box">
+        <b>Grounding attributions</b>:
+	<pre id="grounding-pre">{{ final_context | safe }}</pre>
+      </div>
+      <div id="reference-box">
+      {% for source in search_result %}
+        {% set section_url = named_link_html(source.section.url, source.section.url) %}
+        <b>[{{loop.index}}]</b>{{ section_url | safe}}
+        <br>
+      {% endfor %}
+      </div>
+      <div class="hidden" id="aqa-box">
+        <section class="accordion">
+          <input type="checkbox" name="collapse" id="handle2">
+          <h2 class="handle">
+            <label for="handle2" id="aqa-label">AQA model's JSON response</label>
+          </h2>
+          <div class="content" id="aqa-content">
+	    <pre id="aqa-json">{{ aqa_response_in_html | safe }}</pre>
+          </div>
+        </section>
+      </div>
+    </div>
+  </section>
+  </div>
+  {% else %}
+  <div class="response-text" id="response-box">
+    <span id="gemini-response">
+      I'm sorry, but I cannot answer this question at the moment.
+      <b>The answer to your question does not seem to be available in documentation.</b><br>
+      <br>
+      However, based on the documentation available in the corpus, you could ask questions like:
+    </span>
+  </div>
+  <div class="related-questions">
+    <!--<h4>Suggested questions</h4>-->
+    <span id="suggested-questions">
+      {{ related_questions | safe }}
+    </span>
+  </div>
+  {% endif %}
+{% else %}
+  <!--Gemini Pro template-->
+  <div class="question">
+    <h2>{{ question | replace("+", " ") | replace("%3F", "?")}}</h2>
+  </div>
+  <div class="important-box" id="important-box">
+    <!--Uses the URL from the first result-->
+    <b>Important:</b> The answer below is generated by the Gemini 1.5 model.
+    To verify this answer, please visit: {{named_link_html(check_url, check_url) | safe}}
+  </div>
+  <div class="response-text" id="response-box">
+    <span id="gemini-response">
+      {{ md_to_html(response) | safe }}
+    </span>
+  </div>
+  <div class="related-questions">
+    <h4>Suggested questions</h4>
+    <span id="suggested-questions">
+      {{ related_questions | safe }}
+    <span>
+  </div>
+  <div class="hidden">
+  <section class="accordion">
+    <input type="checkbox" name="collapse" id="handle1">
+    <h2 class="handle">
+      <label for="handle1">Context</label>
+    </h2>
+    <div class="content" id="context-content">
+      <!-- {{ md_to_html(final_context) | safe }} -->
+      {% for source in search_result %}
+      <pre id="context-pre">{{md_to_html(source.section.content) | safe}}Reference <b>[{{loop.index}}]</b></pre>
+      {% endfor %}
+      <div id="reference-box-no-aqa">
+      {% for source in search_result %}
+        {% set section_url = named_link_html(source.section.url, source.section.url) %}
+        <b>[{{loop.index}}]</b>{{ section_url | safe}} ({{ source.distance }})
+        <br>
+      {% endfor %}
+      </div>
+    </div>
+  </section>
+  </div>
+{% endif %}
+
+<!--Feedback buttons at the bottom of the UI display.-->
+{% if feedback_mode == "rewrite" %}
+<div id="rewrite-buttons-box">
+  <input type="button" value="Rewrite this response" id="rewrite-button">
+  <input type="button" class="notselected" value="Like this response &#128077;" id="like-button">
+  {% if aqa_response_in_html != "" %}
+  <span id="answerable-span">
+    (<b>Answerable probability</b>: {{"%.3f"|format(probability|float)}})
+  </span>
+  {% endif %}
+</div>
+<div class="rewrite hidden" id="rewrite-box">
+  <h4 id="rewrite-question-header">Question:</h4>
+  <span id="rewrite-question-span">
+    <p>{{ question | replace("+", " ") | replace("%3F", "?")}}</p>
+  </span>
+  <h4 id="rewrite-response-header">Gemini's response:</h4>
+  <span id="rewrite-original-response-span">
+    {{ md_to_html(response) | safe }}
+  </span>
+  <h4>Rewrite:</h4>
+  <textarea id="edit-text-area">{{ response | safe }}</textarea>
+  <br>
+  <label for="user-id">User ID:</label>
+  <input type="text" id="user-id" name="user-id" placeholder="Optional">
+  <br>
+  <span id="submit-box">
+    <input type="button" value="Submit" id="submit-button">
+  </span>
+  <span id="submit-result">
+  </span>
+</div>
+<div class="hidden" id="uuid-box">
+  {{ uuid | safe }}
+</div>
+{% elif feedback_mode == "like_and_dislike" %}
+<div id="feedback-buttons-box">
+  <input type="button" class="notselected" value="Like this response &#128077;" id="like-button">
+  <input type="button" class="notselected" value="Disike &#128078;" id="dislike-button">
+  {% if aqa_response_in_html != "" %}
+  <span id="answerable-span">
+    (<b>Answerable probability</b>: {{"%.3f"|format(probability|float)}})
+  </span>
+  {% endif %}
+</div>
+<div class="hidden" id="uuid-box">
+  {{ uuid | safe }}
+</div>
+<div class="hidden" id="feedback-question-span">
+  {{ question | replace("+", " ") | replace("%3F", "?")}}
+</div>
+<div class="hidden" id="feedback-response-span">
+  {{ md_to_html(response) | safe }}
+</div>
+{% else %}
+<div id="feedback-buttons-box">
+  <input type="button" value="Provide feedback" id="feedback-button">
+  <input type="button" class="notselected" value="Like this response &#128077;" id="like-button">
+  {% if aqa_response_in_html != "" %}
+  <span id="answerable-span">
+    (<b>Answerable probability</b>: {{"%.3f"|format(probability|float)}})
+  </span>
+  {% endif %}
+</div>
+<div class="feedback hidden" id="feedback-box">
+  <h4>Feedback:</h4>
+  <textarea id="feedback-text-area"></textarea>
+  <br>
+  <label for="user-id">LDAP:</label>
+  <input type="text" id="user-id" name="user-id" placeholder="Optional">
+  <br>
+  <span id="feedback-submit-box">
+    <input type="button" value="Submit" id="feedback-submit-button">
+  </span>
+  <span id="feedback-submit-result">
+  </span>
+</div>
+<div class="hidden" id="uuid-box">
+  {{ uuid | safe }}
+</div>
+<div class="hidden" id="feedback-question-span">
+  {{ question | replace("+", " ") | replace("%3F", "?")}}
+</div>
+<div class="hidden" id="feedback-response-span">
+  {{ md_to_html(response) | safe }}
+</div>
+{% endif %}
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chat-widget/base.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget/base.html
similarity index 96%
rename from examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chat-widget/base.html
rename to examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget/base.html
index 22f2adb33..720260ca5 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chat-widget/base.html
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget/base.html
@@ -2,7 +2,7 @@
 <html lang="en">
 <head>
     <meta charset="UTF-8">
-    <link rel="stylesheet" href="{{ url_for('static', filename='css/style-widget.css') }}">
+    <link rel="stylesheet" href="{{ url_for('static', filename='css/style-chatui-widget.css') }}">
     <link rel="stylesheet" href="{{ url_for('static', filename='css/chatbox.css') }}">
     <link rel="shortcut icon" href="{{ url_for('static', filename='images/favicon.png') }}">
     <title>{{ product }} ChatBot</title>
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chat-widget/index.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget/index.html
similarity index 83%
rename from examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chat-widget/index.html
rename to examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget/index.html
index 437b9bfd2..5a6119bd7 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chat-widget/index.html
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget/index.html
@@ -1,4 +1,4 @@
-{% extends 'chat-widget/base.html' %}
+{% extends 'chatui-widget/base.html' %}
 
 {% block content %}
   <div class="search">
@@ -10,7 +10,7 @@
   <div class="loader hidden" id="loading-div"></div>
   {% if response %}
   <div class="full-response">
-    {% include 'chat-widget/result.html' %}
+    {% include 'chatui-widget/result.html' %}
   </div>
   {% endif %}
 {% endblock %}
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chat-widget/result.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget/result.html
similarity index 100%
rename from examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chat-widget/result.html
rename to examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget/result.html
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/__init__.py b/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli.py b/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli.py
new file mode 100644
index 000000000..b6ff095e2
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli.py
@@ -0,0 +1,37 @@
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Docs Agent CLI wrapper"""
+
+import click
+
+from docs_agent.interfaces.cli.cli_admin import cli_admin
+from docs_agent.interfaces.cli.cli_common import cli_common
+from docs_agent.interfaces.cli.cli_runtask import cli_runtask
+from docs_agent.interfaces.cli.cli_helpme import cli_helpme
+from docs_agent.interfaces.cli.cli_tellme import cli_tellme
+from docs_agent.interfaces.cli.cli_posix import cli_posix
+from docs_agent.interfaces.cli.cli_show_session import cli_show_session
+
+
+cli = click.CommandCollection(
+    sources=[cli_common, cli_admin, cli_runtask, cli_helpme, cli_tellme, cli_posix, cli_show_session],
+    help="With Docs Agent, you can populate vector databases, manage online corpora, and interact with Google's Gemini models.",
+)
+
+
+if __name__ == "__main__":
+    cli()
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/cli.py b/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_admin.py
similarity index 54%
rename from examples/gemini/python/docs-agent/docs_agent/interfaces/cli.py
rename to examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_admin.py
index 2acf992f1..73560d07b 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/cli.py
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_admin.py
@@ -14,14 +14,13 @@
 # limitations under the License.
 #
 
-"""Docs Agent CLI"""
+"""Docs Agent CLI client"""
 
 import click
 import sys
 import typing
-from functools import wraps
 from docs_agent.utilities import config
-from docs_agent.utilities.config import ReadDbConfigs, ConfigFile
+from docs_agent.utilities.config import ReadDbConfigs
 from docs_agent.utilities.config import return_config_and_product
 from docs_agent.utilities.helpers import (
     parallel_backup_dir,
@@ -34,34 +33,13 @@
 from docs_agent.preprocess import populate_vector_database as populate_script
 from docs_agent.benchmarks import run_benchmark_tests as benchmarks
 from docs_agent.interfaces import chatbot as chatbot_flask
-from docs_agent.interfaces import run_console as console
 from docs_agent.storage.google_semantic_retriever import SemanticRetriever
 from docs_agent.storage.chroma import ChromaEnhanced
 from docs_agent.memory.logging import write_logs_to_csv_file
+from docs_agent.interfaces.cli.cli_common import common_options
+from docs_agent.interfaces.cli.cli_common import show_config
 import socket
 import os
-import string
-import re
-import time
-
-
-def common_options(func):
-    @wraps(func)
-    @click.option(
-        "--config_file",
-        help="Specify a configuration file. Defaults to config.yaml.",
-        default=None,
-    )
-    @click.option(
-        "--product",
-        help="Specify a product. Defaults to all products. For chatbot, defaults to the first product in config.yaml.",
-        default=None,
-        multiple=True,
-    )
-    def wrapper(*args, **kwargs):
-        return func(*args, **kwargs)
-
-    return wrapper
 
 
 @click.group(invoke_without_command=True)
@@ -78,25 +56,6 @@ def cli_admin(ctx, config_file, product):
         show_config()
 
 
-@click.group(invoke_without_command=True)
-@common_options
-@click.pass_context
-def cli_client(ctx, config_file, product):
-    """With Docs Agent, you can interact with Google's Gemini
-    models as well as content stored in Google Semantic Retriever Corpora."""
-    ctx.ensure_object(dict)
-    # Print config.yaml if agent is run without a command.
-    if ctx.invoked_subcommand is None:
-        click.echo("Docs Agent configuration:\n")
-        show_config()
-
-
-cli = click.CommandCollection(
-    sources=[cli_admin, cli_client],
-    help="With Docs Agent, you can populate vector databases, manage online corpora, and interact with Google's Gemini models.",
-)
-
-
 @cli_admin.command()
 @common_options
 def chunk(config_file: typing.Optional[str], product: list[str] = [""]):
@@ -143,7 +102,10 @@ def populate(
     "--app_mode",
     default=None,
     show_default=True,
-    type=click.Choice(["web", "widget", "1.5", "experimental"], case_sensitive=False),
+    type=click.Choice(
+        ["web", "full", "widget", "widget-pro", "experimental"],
+        case_sensitive=False,
+    ),
     help="Specify a mode for the chatbot.",
 )
 @click.option(
@@ -198,20 +160,6 @@ def chatbot(
 
 
 @cli_admin.command()
-@common_options
-def show_config(config_file: typing.Optional[str], product: list[str] = [""]):
-    """Print the Docs Agent configuration."""
-    # Loads configurations from common options
-    loaded_config, product_config = return_config_and_product(
-        config_file=config_file, product=product
-    )
-    for item in product_config.products:
-        print("==========================")
-        print(item)
-
-
-@cli_client.command(name="tellme")
-@click.argument("words", nargs=-1)
 @click.option(
     "--model",
     help="Specify a model to use. Overrides the model (language_model) for all loaded product configurations.",
@@ -219,277 +167,11 @@ def show_config(config_file: typing.Optional[str], product: list[str] = [""]):
     multiple=False,
 )
 @common_options
-def tellme(words, config_file: typing.Optional[str], product: list[str] = [""], model: typing.Optional[str] = None):
-    """Answer a question related to the product."""
-    # Loads configurations from common options
-    loaded_config, product_configs = return_config_and_product(
-        config_file=config_file, product=product, model=model
-    )
-    if product == ():
-        # Extracts the first product by default
-        product_config = ConfigFile(products=[product_configs.products[0]])
-    else:
-        product_config = product_configs
-    question = ""
-    for word in words:
-        question += word + " "
-    console.ask_model(question.strip(), product_config)
-
-
-@cli_client.command()
-@click.argument("words", nargs=-1)
-@click.option(
-    "--model",
-    help="Specify a model to use. Overrides the model (language_model) for all loaded product configurations.",
-    default=None,
-    multiple=False,
-)
-@click.option(
-    "--file",
-    type=click.Path(),
-    help="Specify a file to be included as context.",
-)
-@click.option(
-    "--path",
-    type=click.Path(),
-    help="Specify a path where the request will be applied to all files found in the directory.",
-)
-@click.option(
-    "--file_ext",
-    help="Works with --path. Specify the file type to be selected. By default it is set to None.",
-)
-@click.option(
-    "--rag",
-    is_flag=True,
-    help="Add entries found in the knowledge database as context.",
-)
-@click.option(
-    "--yaml",
-    is_flag=True,
-    help="Works with --path. Store the output as a responses.yaml file.",
-)
-@click.option(
-    "--new",
-    is_flag=True,
-    help="Works with --file. Clear the previous responses buffer.",
-)
-@click.option(
-    "--cont",
-    is_flag=True,
-    help="Use the previous responses buffer as context.",
-)
-@common_options
-def helpme(
-    words,
+def benchmark(
     config_file: typing.Optional[str],
-    file: typing.Optional[str] = None,
-    path: typing.Optional[str] = None,
-    file_ext: typing.Optional[str] = None,
-    rag: bool = False,
-    yaml: bool = False,
-    new: bool = False,
-    cont: bool = False,
     product: list[str] = [""],
     model: typing.Optional[str] = None,
 ):
-    """Ask the AI model to perform a task from the terminal."""
-    # Loads configurations from common options
-    loaded_config, product_configs = return_config_and_product(
-        config_file=config_file, product=product, model=model
-    )
-    if product == ():
-        # Extracts the first product by default
-        product_config = ConfigFile(products=[product_configs.products[0]])
-    else:
-        product_config = product_configs
-
-    # Get the language model.
-    this_model = product_config.products[0].models.language_model
-    # This feature is only available to the Gemini Pro models (not AQA).
-    if not this_model.startswith("models/gemini"):
-        click.echo(f"File mode is not supported with this model: {this_model}")
-        exit(1)
-
-    # Get the question string.
-    question = ""
-    for word in words:
-        question += word + " "
-    question = question.strip()
-
-    # Set the filename for recording exchanges with the Gemini models.
-    history_file = "/tmp/docs_agent_responses"
-
-    # 4 different mode: Terminal output (default), All files, Single file,
-    # and Previous exchanges
-    helpme_mode = "TERMINAL_OUTPUT"
-    if path:
-        helpme_mode = "ALL_FILES"
-    elif file and file != "None":
-        helpme_mode = "SINGLE_FILE"
-    elif cont:
-        helpme_mode = "PREVIOUS_EXCHANGES"
-
-    if helpme_mode == "PREVIOUS_EXCHANGES":
-        # Continue mode, which uses the previous exchangs as the main context.
-        this_output = console.ask_model_with_file(
-            question.strip(),
-            product_config,
-            context_file=history_file,
-            rag=rag,
-            return_output=True,
-        )
-        print()
-        print(f"{this_output}")
-        print()
-        with open(history_file, "a", encoding="utf-8") as out_file:
-            out_file.write(f"QUESTION: {question}\n\n")
-            out_file.write(f"RESPONSE:\n\n{this_output}\n")
-            out_file.close()
-    elif helpme_mode == "SINGLE_FILE":
-        # Single file mode.
-        this_file = os.path.realpath(os.path.join(os.getcwd(), file))
-        this_output = ""
-        if cont:
-            # if the `--cont` flag is set, inlcude the previous exchanges as additional context.
-            this_output = console.ask_model_with_file(
-                question.strip(),
-                product_config,
-                file=this_file,
-                context_file=history_file,
-                rag=rag,
-                return_output=True,
-            )
-        else:
-            # By default, do not inlcude any additional context.
-            this_output = console.ask_model_with_file(
-                question.strip(),
-                product_config,
-                file=this_file,
-                rag=rag,
-                return_output=True,
-            )
-        print()
-        print(f"{this_output}")
-        print()
-        # Read the file content to be included in the history file.
-        file_content = ""
-        with open(this_file, "r", encoding="utf-8") as target_file:
-            file_content = target_file.read()
-            target_file.close()
-        # If the `--new` flag is set, overwrite the history file.
-        write_mode = "a"
-        if new:
-            write_mode = "w"
-        # Record this exchange in the history file.
-        with open(history_file, write_mode, encoding="utf-8") as out_file:
-            out_file.write(f"QUESTION: {question}\n\n")
-            out_file.write(f"FILE NAME: {file}\n")
-            out_file.write(f"FILE CONTENT:\n\n{file_content}\n")
-            out_file.write(f"RESPONSE:\n\n{this_output}\n")
-            out_file.close()
-    elif helpme_mode == "ALL_FILES":
-        # All files mode, which makes the request to all files in the path.
-        # Set the `file_type` variable for display only.
-        file_type = "." + str(file_ext)
-        if file_ext == None:
-            file_type = "All types"
-
-        # Ask the user to confirm.
-        if click.confirm(
-            f"Making a request to all files found in the path below:\n"
-            + f"Question: {question}\nPath:  {path}\nFile type: {file_type}\n"
-            + f"Do you want to continue?",
-            abort=True,
-        ):
-            print()
-            out_buffer = ""
-            for root, dirs, files in os.walk(resolve_path(path)):
-                for file in files:
-                    file_path = os.path.realpath(os.path.join(root, file))
-                    if file_ext == None:
-                        # Apply to all files.
-                        print(f"# File: {file_path}")
-                        this_output = console.ask_model_with_file(
-                            question,
-                            product_config,
-                            file=file_path,
-                            rag=rag,
-                            return_output=True,
-                        )
-                        this_output = this_output.strip()
-                        print()
-                        print(f"{this_output}")
-                        print()
-                        if yaml is True:
-                            out_buffer += (
-                                f"  - question: {question}\n"
-                                + f"    response: {this_output}\n"
-                                + f"    file: {file_path}\n"
-                            )
-                        time.sleep(2)
-                    elif file.endswith(file_ext):
-                        print(f"# File: {file_path}")
-                        this_output = console.ask_model_with_file(
-                            question,
-                            product_config,
-                            file=file_path,
-                            rag=rag,
-                            return_output=True,
-                        )
-                        this_output = this_output.strip()
-                        print()
-                        print(f"{this_output}")
-                        print()
-                        if yaml is True:
-                            out_buffer += (
-                                f"  - question: {question}\n"
-                                + f"    response: {this_output}\n"
-                                + f"    file: {file_path}\n"
-                            )
-                        time.sleep(2)
-
-            if yaml is True:
-                output_filename = "./responses.yaml"
-                with open(output_filename, "w", encoding="utf-8") as out_file:
-                    out_file.write("benchmarks:\n")
-                    out_file.write(out_buffer)
-                    out_file.close()
-    else:
-        # Terminal output mode, which reads the terminal ouput as context.
-        terminal_output = ""
-        # Set the default filename created from the `script` command.
-        file_path = "/tmp/docs_agent_console_input"
-        # Set the maximum number of lines to read from the terminal.
-        lines_limit = -150
-        # For the new 1.5 pro model, increase the limit to 5000 lines.
-        if this_model.startswith("models/gemini-1.5-pro"):
-            lines_limit = -5000
-        try:
-            with open(file_path, "r", encoding="utf-8") as file:
-                for line in file.readlines()[int(lines_limit) : -2]:
-                    # Filter non-ascii and control characters.
-                    printable = set(string.printable)
-                    line = "".join(filter(lambda x: x in printable, line))
-                    terminal_output += line
-                    # if an Enter key is detected, remove all saved lines.
-                    # This allows to read the output of the last commandline only.
-                    if re.search("^\[\?2004", line):
-                        terminal_output = ""
-        except:
-            terminal_output = "No console output is provided."
-        context = "THE FOLLOWING IS MY CONSOLE OUTPUT:\n\n" + terminal_output
-        console.ask_model_for_help(question, context, product_config)
-
-
-@cli_admin.command()
-@click.option(
-    "--model",
-    help="Specify a model to use. Overrides the model (language_model) for all loaded product configurations.",
-    default=None,
-    multiple=False,
-)
-@common_options
-def benchmark(config_file: typing.Optional[str], product: list[str] = [""],  model: typing.Optional[str] = None):
     """Run the Docs Agent benchmark test."""
     # Loads configurations from common options
     loaded_config, product_config = return_config_and_product(
@@ -743,5 +425,11 @@ def write_logs_to_csv(
         f.close()
 
 
+cli = click.CommandCollection(
+    sources=[cli_admin],
+    help="With Docs Agent admin, you can populate vector databases, manage online corpora, and maintain Docs Agent.",
+)
+
+
 if __name__ == "__main__":
     cli()
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_common.py b/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_common.py
new file mode 100644
index 000000000..596dbf41d
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_common.py
@@ -0,0 +1,67 @@
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Docs Agent CLI common options"""
+
+import click
+import typing
+from functools import wraps
+from docs_agent.utilities.config import return_config_and_product
+
+
+def common_options(func):
+    @wraps(func)
+    @click.option(
+        "--config_file",
+        help="Specify a configuration file. Defaults to config.yaml.",
+        default=None,
+    )
+    @click.option(
+        "--product",
+        help="Specify a product. Defaults to use all products. For web app, defaults to use the first product in config.yaml.",
+        default=None,
+        multiple=True,
+    )
+    def wrapper(*args, **kwargs):
+        return func(*args, **kwargs)
+
+    return wrapper
+
+
+@click.group(invoke_without_command=True)
+@common_options
+@click.pass_context
+def cli_common(ctx, config_file, product):
+    """With Docs Agent, you can populate vector databases,
+    manage online corpora, and interact with Google's Gemini
+    models."""
+    ctx.ensure_object(dict)
+    # Print config.yaml if agent is run without a command.
+    if ctx.invoked_subcommand is None:
+        click.echo("Docs Agent configuration:\n")
+        show_config()
+
+
+@cli_common.command()
+def show_config(config_file: typing.Optional[str] = "config.yaml", product: list[str] = [""]):
+    """Print the Docs Agent configuration."""
+    # Loads configurations from common options
+    loaded_config, product_config = return_config_and_product(
+        config_file=config_file, product=product
+    )
+    for item in product_config.products:
+        print("==========================")
+        print(item)
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_helpme.py b/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_helpme.py
new file mode 100644
index 000000000..d395834a7
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_helpme.py
@@ -0,0 +1,765 @@
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Docs Agent CLI client"""
+
+import click
+import typing
+from docs_agent.utilities import config
+from docs_agent.utilities.config import ConfigFile
+from docs_agent.utilities.config import return_config_and_product, get_project_path
+from docs_agent.utilities.helpers import resolve_path
+
+from docs_agent.interfaces import run_console as console
+from docs_agent.interfaces.cli.cli_common import common_options
+from docs_agent.interfaces.cli.cli_common import show_config
+import os
+import string
+import re
+import time
+import subprocess
+from pathlib import Path
+
+from rich.console import Console
+from rich.markdown import Markdown
+from rich.panel import Panel
+from rich.text import Text
+from rich.style import Style
+
+
+@click.group(invoke_without_command=True)
+@common_options
+@click.pass_context
+def cli_helpme(ctx, config_file, product):
+    """With Docs Agent, you can interact with Google's Gemini
+    models and manage online corpora on Google Cloud."""
+    ctx.ensure_object(dict)
+    # Print config.yaml if agent is run without a command.
+    if ctx.invoked_subcommand is None:
+        click.echo("Docs Agent configuration:\n")
+        show_config()
+
+
+@cli_helpme.command()
+@click.argument("words", nargs=-1)
+@click.option(
+    "--model",
+    help="Specify a language model.",
+    default=None,
+    multiple=False,
+)
+@click.option(
+    "--file",
+    type=click.Path(),
+    help="Specify a file to be included as context.",
+)
+@click.option(
+    "--perfile",
+    type=click.Path(),
+    help="Specify a path where the request will be applied to each file in the directory.",
+)
+@click.option(
+    "--allfiles",
+    type=click.Path(),
+    help="Specify a path where all files in the directory are used as context.",
+)
+@click.option(
+    "--file_ext",
+    help="Works with --perfile and --dir. Specify the file type to be selected. The default is set to use all files.",
+)
+@click.option(
+    "--yaml",
+    is_flag=True,
+    help="Store the output to responses.yaml.",
+)
+@click.option(
+    "--rag",
+    is_flag=True,
+    help="Add entries retrieved from the knowledge database as context.",
+)
+@click.option(
+    "--new",
+    is_flag=True,
+    help="Start a new session.",
+)
+@click.option(
+    "--cont",
+    is_flag=True,
+    help="Use the previous responses in the session as context.",
+)
+@click.option(
+    "--terminal",
+    is_flag=True,
+    help="Use the terminal output as context. (This option requires a special setup in the terminal.)",
+)
+@click.option(
+    "--force",
+    is_flag=True,
+    help="Do not ask the user to confirm interactively.",
+)
+@click.option(
+    "--check",
+    is_flag=True,
+    help="Print the current prompt for testing.",
+    hidden=True,
+)
+@click.option(
+    "--sleep",
+    type=int,
+    default=0,
+    help="Sleep for a specified duration (in seconds) after completing the command.",
+    hidden=True,
+)
+@click.option(
+    "--out",
+    type=click.Path(),
+    help="Specify an output file for saving the response. By default, the file is saved in the project's `agent_out/` directory. Use an absolute path if you want to save the file in a directory outside your current project.",
+)
+@click.option(
+    "--panel",
+    is_flag=True,
+    help="Use a panel for rendering the response.",
+)
+@common_options
+def helpme(
+    words,
+    config_file: typing.Optional[str] = None,
+    file: typing.Optional[str] = None,
+    perfile: typing.Optional[str] = None,
+    allfiles: typing.Optional[str] = None,
+    file_ext: typing.Optional[str] = None,
+    yaml: bool = False,
+    out: typing.Optional[str] = None,
+    rag: bool = False,
+    new: bool = False,
+    cont: bool = False,
+    terminal: bool = False,
+    force: bool = False,
+    check: bool = False,
+    sleep: int = 0,
+    panel: bool = False,
+    product: list[str] = [""],
+    model: typing.Optional[str] = None,
+):
+    """Ask an AI language model to perform a task from the terminal."""
+    # Initialize Rich console
+    ai_console = Console(width=120)
+    console_style = Style(color="cyan", bold=False)
+    use_panel = False
+    if panel:
+        use_panel = True
+    # Loads configurations from common options
+    loaded_config, product_configs = return_config_and_product(
+        config_file=config_file, product=product, model=model
+    )
+    if product == ():
+        # Extracts the first product by default
+        product_config = ConfigFile(products=[product_configs.products[0]])
+    else:
+        product_config = product_configs
+
+    # Get the language model.
+    this_model = product_config.products[0].models.language_model
+    # This feature is only available to the Gemini Pro models (not AQA).
+    if not this_model.startswith("models/gemini"):
+        click.echo(f"File mode is not supported with this model: {this_model}")
+        exit(1)
+
+    # Get the question string.
+    question = ""
+    for word in words:
+        question += word + " "
+    question = question.strip()
+    # This is wrapped around the question when --out flag is set, this will
+    # help format the output of the final file.
+    question_out_wrapper = (
+        f"The answer that you provide to the question below will be saved to a file "
+        + f"named {out}. Your response must only include what will go in this file. "
+        + f"Do your best to ensure that you provide a response that matches the format "
+        + f"of this file extension. For example .md indicates a Markdown file, .py "
+        + f"a Python file, and so on. Markdown files must always be in valid Markdown "
+        + f"format and begin with the # header (for instance, # <PAGE TITLE>).\n\n"
+    )
+    # Set output path to the agent_out directory.
+    if out is not None and out != "" and out != "None":
+        if out.startswith("~/"):
+            out = os.path.expanduser(out)
+        if out.startswith("/"):
+            base_out = os.path.dirname(out)
+            out = Path(out).name
+        # This includes paths like out.startswith("~")
+        else:
+            base_out = os.path.join(get_project_path(), "agent_out")
+        # Creates the output directory if it can't write, it will try home directory.
+        try:
+            os.makedirs(base_out, exist_ok=True)
+        except:
+            base_out = os.path.expanduser("~/docs_agent")
+            base_out = os.path.join(base_out, "agent_out")
+            try:
+                os.makedirs(base_out, exist_ok=True)
+            except:
+                base_out = os.path.join("/tmp/docs_agent", "agent_out")
+                try:
+                    os.makedirs(base_out, exist_ok=True)
+                except:
+                    print(f"Failed to create the output directory: {base_out}")
+                    exit(1)
+        if base_out.endswith("/"):
+            base_out = base_out[:-1]
+        out = base_out + "/" + out
+
+    # Print the prompt for testing.
+    if check:
+        print(f"Prompt: {question}")
+        exit(0)
+
+    # Set the filename for recording exchanges with the Gemini models.
+    history_file = "/tmp/docs_agent_responses"
+
+    # Different modes: Terminal output, Single file, Per file, All files,
+    # Previous exchanges, and No flags.
+    helpme_mode = ""
+    if perfile and perfile != "None":
+        helpme_mode = "PER_FILE"
+    elif allfiles and allfiles != "None":
+        helpme_mode = "ALL_FILES"
+    elif file and file != "None":
+        helpme_mode = "SINGLE_FILE"
+    elif cont:
+        helpme_mode = "PREVIOUS_EXCHANGES"
+    elif terminal:
+        helpme_mode = "TERMINAL_OUTPUT"
+    else:
+        helpme_mode = "NO_FLAGS"
+
+    # Select the mode.
+    if helpme_mode == "PREVIOUS_EXCHANGES":
+        if out is not None and out != "" and out != "None":
+            question = question_out_wrapper + question
+        # Continue mode, which uses the previous exchangs as the main context.
+        this_output = console.ask_model_with_file(
+            question.strip(),
+            product_config,
+            context_file=history_file,
+            rag=rag,
+            return_output=True,
+        )
+
+        # Render the response.
+        if use_panel:
+            ai_console.print("[Response]", style=console_style)
+            ai_console.print(Panel(Markdown(this_output, code_theme="manni")))
+        else:
+            print()
+            print(f"{this_output}")
+            print()
+
+        # Save the response as context.
+        with open(history_file, "a", encoding="utf-8") as out_file:
+            out_file.write(f"QUESTION: {question}\n\n")
+            out_file.write(f"RESPONSE:\n\n{this_output}\n\n")
+            out_file.close()
+
+        # Save this exchange in a YAML file.
+        if yaml is True:
+            output_filename = "./responses.yaml"
+            # Prepare output to be saved in the YAML file.
+            yaml_buffer = (
+                f"  - question: {question}\n" + f"    response: {this_output}\n"
+            )
+            with open(output_filename, "w", encoding="utf-8") as yaml_file:
+                yaml_file.write("logs:\n")
+                yaml_file.write(yaml_buffer)
+                yaml_file.close()
+
+        # Save the response to the `out` file.
+        if out is not None and out != "" and out != "None":
+            try:
+                with open(out, "w", encoding="utf-8") as out_file:
+                    out_file.write(f"{this_output}\n")
+                    out_file.close()
+            except:
+                print(f"Failed to write the output to file: {out}")
+
+    elif helpme_mode == "SINGLE_FILE":
+        if out is not None and out != "" and out != "None":
+            question = question_out_wrapper + question
+        # Single file mode.
+        if file.startswith("~/"):
+            file = os.path.expanduser(file)
+        this_file = os.path.realpath(os.path.join(os.getcwd(), file))
+        this_output = ""
+
+        # if the `--cont` flag is set, inlcude the previous exchanges as additional context.
+        context_file = None
+        if cont:
+            context_file = history_file
+
+        this_output = console.ask_model_with_file(
+            question.strip(),
+            product_config,
+            file=this_file,
+            context_file=context_file,
+            rag=rag,
+            return_output=True,
+        )
+
+        # Render the response.
+        if use_panel:
+            ai_console.print("[Response]", style=console_style)
+            ai_console.print(Panel(Markdown(this_output, code_theme="manni")))
+        else:
+            print()
+            print(f"{this_output}")
+            print()
+
+        # Read the file content to be included in the history file.
+        file_content = ""
+        try:
+            with open(this_file, "r", encoding="utf-8") as target_file:
+                file_content = target_file.read()
+                target_file.close()
+        except:
+            print(f"[Error] This file cannot be opened: {this_file}\n")
+            exit(1)
+
+        # If the `--new` flag is set, overwrite the history file.
+        write_mode = "a"
+        if new:
+            write_mode = "w"
+        # Record this exchange in the history file.
+        with open(history_file, write_mode, encoding="utf-8") as out_file:
+            out_file.write(f"QUESTION: {question}\n\n")
+            out_file.write(f"FILE NAME: {file}\n")
+            out_file.write(f"FILE CONTENT:\n\n{file_content}\n")
+            out_file.write(f"RESPONSE:\n\n{this_output}\n\n")
+            out_file.close()
+
+        # Save this exchange in a YAML file.
+        if yaml is True:
+            output_filename = "./responses.yaml"
+            # Prepare output to be saved in the YAML file.
+            yaml_buffer = (
+                f"  - question: {question}\n"
+                + f"    response: {this_output}\n"
+                + f"    file: {this_file}\n"
+            )
+            with open(output_filename, "w", encoding="utf-8") as yaml_file:
+                yaml_file.write("logs:\n")
+                yaml_file.write(yaml_buffer)
+                yaml_file.close()
+
+        # Save the response to the `out` file.
+        if out is not None and out != "" and out != "None":
+            try:
+                with open(out, "w", encoding="utf-8") as out_file:
+                    out_file.write(f"{this_output}\n")
+                    out_file.close()
+            except:
+                print(f"Failed to write the output to file: {out}")
+
+    elif helpme_mode == "PER_FILE":
+        # Per file mode, which makes the request to each file in the path.
+        if perfile.startswith("~/"):
+            perfile = os.path.expanduser(perfile)
+        this_path = os.path.realpath(resolve_path(perfile))
+        if not os.path.exists(this_path):
+            print(f"[Error] Cannot access the input path: {this_path}")
+            exit(1)
+        # Set the `file_type` variable for display only.
+        file_type = "." + str(file_ext)
+        if file_ext is None or file_ext == "":
+            file_type = "All types"
+
+        # if the `--cont` flag is set, inlcude the previous exchanges as additional context.
+        context_file = None
+        if cont:
+            context_file = history_file
+
+        # Ask the user to confirm.
+        confirm_string = (
+            f"Making a request to each file found in the path below:\n"
+            + f"Question: {question}\nPath: {this_path}\nFile type: {file_type}\n"
+        )
+        if force or click.confirm(
+            f"{confirm_string}" + f"Do you want to continue?",
+            abort=True,
+        ):
+            if force:
+                print(confirm_string)
+            else:
+                print()
+            out_buffer = ""
+            out_buffer_2 = ""
+            yaml_buffer = ""
+            for root, dirs, files in os.walk(resolve_path(perfile)):
+                for file in files:
+                    file_path = os.path.realpath(os.path.join(root, file))
+                    if file_ext == None:
+                        # Apply to all files.
+                        print(f"# File: {file_path}")
+                        try:
+                            with open(file_path, "r", encoding="utf-8") as auto:
+                                content = auto.read()
+                                auto.close()
+                        except:
+                            print(
+                                f"[Warning] Skipping this file because it cannot be opened: {file}\n"
+                            )
+                            continue
+                        this_output = console.ask_model_with_file(
+                            question,
+                            product_config,
+                            file=file_path,
+                            context_file=context_file,
+                            rag=rag,
+                            return_output=True,
+                        )
+                        this_output = this_output.strip()
+                        # Render the response.
+                        if use_panel:
+                            print()
+                            ai_console.print("[Response]", style=console_style)
+                            ai_console.print(
+                                Panel(Markdown(this_output, code_theme="manni"))
+                            )
+                            print()
+                        else:
+                            print()
+                            print(f"{this_output}")
+                            print()
+                        # Read the file content to be included in the history file.
+                        file_content = ""
+                        with open(file_path, "r", encoding="utf-8") as target_file:
+                            file_content = target_file.read()
+                            target_file.close()
+                        # Prepare output to be saved in the history file.
+                        out_buffer += (
+                            f"QUESTION: {question}\n\n"
+                            + f"FILE NAME: {file_path}\n"
+                            + f"FILE CONTENT:\n\n{file_content}\n"
+                            + f"RESPONSE:\n\n{this_output}\n\n"
+                        )
+                        # Prepare output to be saved in the `out` file.
+                        out_buffer_2 += (
+                            f"FILE NAME: {file_path}\n\n" + f"{this_output}\n\n"
+                        )
+                        # Prepare output to be saved in the YAML file.
+                        if yaml is True:
+                            yaml_buffer += (
+                                f"  - question: {question}\n"
+                                + f"    response: {this_output}\n"
+                                + f"    file: {file_path}\n"
+                            )
+                        time.sleep(3)
+                    elif file.endswith(file_ext):
+                        print(f"# File: {file_path}")
+                        try:
+                            with open(file_path, "r", encoding="utf-8") as auto:
+                                content = auto.read()
+                                auto.close()
+                        except:
+                            print(
+                                f"[Warning] Skipping this file becasue it cannot be opened: {file}\n"
+                            )
+                            continue
+                        this_output = console.ask_model_with_file(
+                            question,
+                            product_config,
+                            file=file_path,
+                            context_file=context_file,
+                            rag=rag,
+                            return_output=True,
+                        )
+                        this_output = this_output.strip()
+                        # Render the response.
+                        if use_panel:
+                            print()
+                            ai_console.print("[Response]", style=console_style)
+                            ai_console.print(
+                                Panel(Markdown(this_output, code_theme="manni"))
+                            )
+                            print()
+                        else:
+                            print()
+                            print(f"{this_output}")
+                            print()
+
+                        # Read the file content to be included in the history file.
+                        file_content = ""
+                        with open(file_path, "r", encoding="utf-8") as target_file:
+                            file_content = target_file.read()
+                            target_file.close()
+                        # Prepare output to be saved in the history file.
+                        out_buffer += (
+                            f"QUESTION: {question}\n\n"
+                            + f"FILE NAME: {file_path}\n"
+                            + f"FILE CONTENT:\n\n{file_content}\n"
+                            + f"RESPONSE:\n\n{this_output}\n\n"
+                        )
+                        # Prepare output to be saved in the `out` file.
+                        out_buffer_2 += (
+                            f"FILE NAME: {file_path}\n\n" + f"{this_output}\n\n"
+                        )
+                        # Prepare output to be saved in the YAML file.
+                        if yaml is True:
+                            yaml_buffer += (
+                                f"  - question: {question}\n"
+                                + f"    response: {this_output}\n"
+                                + f"    file: {file_path}\n"
+                            )
+                        time.sleep(3)
+
+            # If the `--new` flag is set, overwrite the history file.
+            write_mode = "a"
+            if new:
+                write_mode = "w"
+            # Record this exchange in the history file.
+            with open(history_file, write_mode, encoding="utf-8") as out_file:
+                out_file.write(out_buffer)
+                out_file.close()
+
+            if yaml is True:
+                output_filename = "./responses.yaml"
+                with open(output_filename, "w", encoding="utf-8") as yaml_file:
+                    yaml_file.write("logs:\n")
+                    yaml_file.write(yaml_buffer)
+                    yaml_file.close()
+
+            # Save the responses to the `out` file.
+            if out is not None and out != "" and out != "None":
+                try:
+                    with open(out, "w", encoding="utf-8") as out_file:
+                        out_file.write(f"{out_buffer_2}")
+                        out_file.close()
+                except:
+                    print(f"Failed to write the output to file: {out}")
+
+    elif helpme_mode == "ALL_FILES":
+        if allfiles.startswith("~/"):
+            allfiles = os.path.expanduser(allfiles)
+        # All files mode, which makes all files in the path to be included as context.
+        this_path = os.path.realpath(resolve_path(allfiles))
+        if not os.path.exists(this_path):
+            print(f"[Error] Cannot access the input path: {this_path}")
+            exit(1)
+        # Set the `file_type` variable for display only.
+        file_type = "." + str(file_ext)
+        if file_ext is None or file_ext == "":
+            file_type = "All types"
+
+        # Ask the user to confirm.
+        confirm_string = (
+            f"Adding all files found in the path below to context:\n"
+            + f"Question: {question}\nPath: {this_path}\nFile type: {file_type}\n"
+        )
+        if force or click.confirm(
+            f"{confirm_string}" + f"Do you want to continue?",
+            abort=True,
+        ):
+            if force:
+                print(confirm_string)
+            else:
+                print()
+            context_buffer = ""
+            for root, dirs, files in os.walk(resolve_path(allfiles)):
+                for file in files:
+                    file_path = os.path.realpath(os.path.join(root, file))
+                    file_content = ""
+                    if file_ext == None:
+                        # Apply to all files.
+                        print(f"# File: {file_path}")
+                        try:
+                            with open(file_path, "r", encoding="utf-8") as auto:
+                                file_content = auto.read()
+                                auto.close()
+                            context_buffer += (
+                                f"FILE NAME: {file_path}\n"
+                                + f"FILE CONTENT:\n\n{file_content}\n\n"
+                            )
+                            print(f"Adding this file to context: {file}\n")
+                        except:
+                            print(
+                                f"[Warning] Skipping this file becasue it cannot be opened: {file}\n"
+                            )
+                            continue
+                    elif file.endswith(file_ext):
+                        print(f"# File: {file_path}")
+                        try:
+                            with open(file_path, "r", encoding="utf-8") as auto:
+                                file_content = auto.read()
+                                auto.close()
+                            context_buffer += (
+                                f"FILE NAME: {file_path}\n"
+                                + f"FILE CONTENT:\n\n{file_content}\n\n"
+                            )
+                            print(f"Adding this file to context: {file}\n")
+                        except:
+                            print(
+                                f"[Warning] Skipping this file becasue it cannot be opened: {file}\n"
+                            )
+                            continue
+
+            context = (
+                "THE FOLLOWING ARE THE CONTENTS OF ALL THE FILES FOUND IN THE TARGET DIRECTORY:\n\n"
+                + context_buffer
+            )
+
+            # If the `--new` flag is set, overwrite the history file.
+            write_mode = "a"
+            if new:
+                write_mode = "w"
+            elif cont:
+                write_mode = "a"
+            # Record this context in the history file.
+            with open(history_file, write_mode, encoding="utf-8") as out_file:
+                out_file.write(context)
+                out_file.close()
+
+            this_output = console.ask_model_with_file(
+                question.strip(),
+                product_config,
+                file=None,
+                context_file=history_file,
+                rag=rag,
+                return_output=True,
+            )
+            this_output = this_output.strip()
+            # Render the response.
+            if use_panel:
+                ai_console.print("[Response]", style=console_style)
+                ai_console.print(Panel(Markdown(this_output, code_theme="manni")))
+            else:
+                print()
+                print(f"{this_output}")
+                print()
+
+            # Record this output in the history file.
+            write_mode = "a"
+            with open(history_file, write_mode, encoding="utf-8") as out_file:
+                out_file.write(f"QUESTION: {question}\n\n")
+                out_file.write(f"RESPONSE:\n\n{this_output}\n\n")
+                out_file.close()
+
+            # Save this exchange in a YAML file.
+            if yaml is True:
+                output_filename = "./responses.yaml"
+                # Prepare output to be saved in the YAML file.
+                yaml_buffer = (
+                    f"  - question: {question}\n"
+                    + f"    response: {this_output}\n"
+                    + f"    path: {this_path}\n"
+                )
+                with open(output_filename, "w", encoding="utf-8") as yaml_file:
+                    yaml_file.write("logs:\n")
+                    yaml_file.write(yaml_buffer)
+                    yaml_file.close()
+
+            # Save the response to the `out` file.
+            if out is not None and out != "" and out != "None":
+                try:
+                    with open(out, "w", encoding="utf-8") as out_file:
+                        out_file.write(f"{this_output}\n")
+                        out_file.close()
+                except:
+                    print(f"Failed to write the output to file: {out}")
+
+    elif helpme_mode == "TERMINAL_OUTPUT":
+        # Terminal output mode, which reads the terminal ouput as context.
+        terminal_output = ""
+        # Set the default filename created from the `script` command.
+        file_path = "/tmp/docs_agent_console_input"
+        # Set the maximum number of lines to read from the terminal.
+        lines_limit = -150
+        # For the new 1.5 pro model, increase the limit to 5000 lines.
+        if this_model.startswith("models/gemini-1.5"):
+            lines_limit = -5000
+        try:
+            with open(file_path, "r", encoding="utf-8") as file:
+                for line in file.readlines()[int(lines_limit) : -2]:
+                    # Filter non-ascii and control characters.
+                    printable = set(string.printable)
+                    line = "".join(filter(lambda x: x in printable, line))
+                    terminal_output += line
+                    # if an Enter key is detected, remove all saved lines.
+                    # This allows to read the output of the last commandline only.
+                    if re.search("^\[\?2004", line):
+                        terminal_output = ""
+        except:
+            terminal_output = "No console output is provided."
+        context = "THE FOLLOWING IS MY CONSOLE OUTPUT:\n\n" + terminal_output
+        console.ask_model_for_help(question, context, product_config)
+    else:
+        # Default mode. No additional context is provided.
+        this_output = console.ask_model_without_context(
+            question.strip(),
+            product_config,
+            return_output=True,
+        )
+
+        # Render the response.
+        if use_panel:
+            ai_console.print("[Response]", style=console_style)
+            ai_console.print(Panel(Markdown(this_output, code_theme="manni")))
+        else:
+            print()
+            print(f"{this_output}")
+            print()
+
+        # If the `--new` flag is set, overwrite the history file.
+        if new:
+            write_mode = "w"
+            # Record this exchange in the history file.
+            with open(history_file, write_mode, encoding="utf-8") as out_file:
+                out_file.write(f"QUESTION: {question}\n\n")
+                out_file.write(f"RESPONSE:\n\n{this_output}\n\n")
+                out_file.close()
+
+        # Save this exchange in a YAML file.
+        if yaml is True:
+            output_filename = "./responses.yaml"
+            # Prepare output to be saved in the YAML file.
+            yaml_buffer = (
+                f"  - question: {question}\n" + f"    response: {this_output}\n"
+            )
+            with open(output_filename, "w", encoding="utf-8") as yaml_file:
+                yaml_file.write("logs:\n")
+                yaml_file.write(yaml_buffer)
+                yaml_file.close()
+
+        # Save the response to the `out` file.
+        if out is not None and out != "" and out != "None":
+            try:
+                with open(out, "w", encoding="utf-8") as out_file:
+                    out_file.write(f"{this_output}\n")
+                    out_file.close()
+            except:
+                print(f"Failed to write the output to file: {out}")
+
+    # When the --sleep flag is provided, sleep for the specified duration.
+    if sleep > 0:
+        time.sleep(int(sleep))
+
+
+cli = click.CommandCollection(
+    sources=[cli_helpme],
+    help="With Docs Agent, you can interact with Google's Gemini models.",
+)
+
+
+if __name__ == "__main__":
+    cli()
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_posix.py b/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_posix.py
new file mode 100644
index 000000000..1b10e8097
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_posix.py
@@ -0,0 +1,114 @@
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Docs Agent CLI client"""
+
+import click
+import typing
+
+from docs_agent.interfaces.cli.cli_common import common_options
+from docs_agent.interfaces.cli.cli_common import show_config
+
+import subprocess
+import os
+import time
+
+
+@click.group(invoke_without_command=True)
+@common_options
+@click.pass_context
+def cli_posix(ctx, config_file, product):
+    """With Docs Agent, you can interact with Google's Gemini
+    models and manage online corpora on Google Cloud."""
+    ctx.ensure_object(dict)
+    # Print config.yaml if agent is run without a command.
+    if ctx.invoked_subcommand is None:
+        click.echo("Docs Agent configuration:\n")
+        show_config()
+
+
+@cli_posix.command(name="posix")
+@click.argument("words", nargs=-1)
+@click.option(
+    "--new",
+    is_flag=True,
+    help="Start a new session.",
+)
+@click.option(
+    "--cont",
+    is_flag=True,
+    help="Use the previous responses in the session as context.",
+)
+@click.option(
+    "--sleep",
+    type=int,
+    default=0,
+    help="Sleep for a specified duration (in seconds) after completing the command.",
+    hidden=True,
+)
+@common_options
+def posix(
+    words,
+    config_file: typing.Optional[str] = None,
+    new: bool = False,
+    cont: bool = False,
+    sleep: int = 0,
+    product: list[str] = [""],
+):
+    """Run a POSIX command and add its output into context."""
+    # Set the filename for recording exchanges with the Gemini models.
+    history_file = "/tmp/docs_agent_responses"
+
+    # Get the question string.
+    command = ""
+    for word in words:
+        if word.startswith("~/"):
+            word = os.path.expanduser(word)
+        command += word + " "
+    command = command.strip()
+
+    print(f"Running a POSIX command: {command}")
+    print()
+    subprocess.run(command.split())
+    result = subprocess.run(command.split(), stdout=subprocess.PIPE)
+    this_output = result.stdout.decode("utf-8")
+
+    # If the `--new` flag is set, overwrite the history file.
+    write_mode = "None"
+    if new:
+        write_mode = "w"
+    elif cont:
+        write_mode = "a"
+    if write_mode != "None":
+        # Record this exchange in the history file.
+        with open(history_file, write_mode, encoding="utf-8") as out_file:
+            out_file.write(f"POSIX COMMAND: {command}\n\n")
+            out_file.write(f"RESPONSE:\n\n{this_output}\n\n")
+            out_file.close()
+
+    # When the --sleep flag is provided, sleep for the specified duration.
+    if sleep > 0:
+        time.sleep(int(sleep))
+
+
+cli = click.CommandCollection(
+    sources=[cli_posix],
+    help="With Docs Agent, you can interact with Google's Gemini models.",
+)
+
+
+if __name__ == "__main__":
+    cli()
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_runtask.py b/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_runtask.py
new file mode 100644
index 000000000..7deea6f0b
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_runtask.py
@@ -0,0 +1,476 @@
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Docs Agent CLI client - run agent"""
+
+import click
+import typing
+from absl import logging
+from docs_agent.utilities import config
+from docs_agent.interfaces import run_console as console
+from docs_agent.utilities.helpers import get_project_path
+from docs_agent.utilities.config import return_config_and_product
+from docs_agent.utilities.tasks import return_tasks_config
+from docs_agent.utilities.tasks import combine_yaml_files, TaskConfigFile
+from docs_agent.interfaces.cli.cli_common import common_options
+from docs_agent.interfaces.cli.cli_helpme import helpme
+from docs_agent.interfaces.cli.cli_tellme import tellme
+from docs_agent.interfaces.cli.cli_posix import posix
+import os
+import re
+import time
+
+from rich.console import Console
+from rich.markdown import Markdown
+from rich.panel import Panel
+from rich.text import Text
+from rich.style import Style
+
+
+@click.group(invoke_without_command=True)
+@common_options
+@click.pass_context
+def cli_runtask(ctx, config_file, product):
+    """With Docs Agent, you can interact with Google's Gemini
+    models and manage online corpora on Google Cloud."""
+    ctx.ensure_object(dict)
+    # Print config.yaml if task is run without a command.
+    # if ctx.invoked_subcommand is None:
+    #     click.echo("Docs Agent configuration:\n")
+    #     show_config()
+
+
+@cli_runtask.command(name="runtask")
+@click.argument("words", nargs=-1)
+@click.option(
+    "--model",
+    help="Specify a model to use. Overrides the model for all configured help me tasks and configuration. model setting is not overwritten for tellme tasks.",
+    default=None,
+    multiple=False,
+)
+@click.option(
+    "--task_config",
+    help="Specify a yaml file or path that contains configuration of tasks.",
+    default=os.path.join(get_project_path(), "tasks"),
+    multiple=False,
+)
+@click.option(
+    "--force",
+    is_flag=True,
+    help="Do not ask the user to confirm interactively.",
+)
+@click.option(
+    "--task",
+    help="Specify a task to run.",
+    default=None,
+    multiple=True,
+)
+@click.pass_context
+def runtask(
+    # Words can be used to try to find an agent or match to helpme/tellme
+    ctx,
+    words,
+    task_config: typing.Optional[str],
+    task: typing.Optional[str],
+    model: typing.Optional[str] = None,
+    force: bool = False,
+    # task: typing.Optional[str] = None,
+):
+    """Perform tasks defined in a yaml file."""
+    # Initialize Rich console
+    ai_console = Console(width=120)
+    console_style = Style(color="default", bold=True)
+    use_panel = True
+    task_config_user = os.path.join(os.path.expanduser("~/docs_agent"), "tasks")
+    # Read the agent config.
+    if task_config is None:
+        task_config = os.path.join(get_project_path(), "tasks")
+    if task_config.endswith(".yaml"):
+        tasks_config = return_tasks_config(tasks_file=task_config, task=task)
+    else:
+        # Checks for --task flag to make sure we run right tasks
+        tasks_config = combine_yaml_files(
+            path=os.path.join(get_project_path(), "tasks")
+        )
+        try:
+            combined_tasks_config = []
+            tasks_config_user = combine_yaml_files(path=task_config_user)
+            combined_tasks_config.extend(tasks_config_user.tasks)
+            combined_tasks_config.extend(tasks_config.tasks)
+            tasks_config = TaskConfigFile(tasks=combined_tasks_config)
+        except:
+            tasks_config = TaskConfigFile(tasks=tasks_config.tasks)
+        if task is not None and task != ():
+            for item in task:
+                loaded_tasks_config = tasks_config.return_task(task=item)
+                if loaded_tasks_config is None:
+                    print()
+                    print(f"Task {item} not found.")
+                    print()
+                    print("These are the available tasks:")
+                    print()
+                    for item in tasks_config.tasks:
+                        print()
+                        print(f"- {item.name}")
+                        if item.description:
+                            print(f"  Description: {item.description}")
+                        print(f"  Usage: agent runtask --task {item.name}")
+                    print()
+                    print("You can also ask me to try to find a task for you.")
+                    print()
+                    print("Usage: agent runtask how can I generate release notes?")
+                    exit(1)
+                else:
+                    tasks_config = loaded_tasks_config
+        # This checks if a task is specified and if not, suggest a task.
+        elif (task is None or task == ()) and words == "":
+            print()
+            print(
+                f"No task specified. Please specify a task. These are the available tasks:"
+            )
+            for item in tasks_config.tasks:
+                print()
+                print(f"- {item.name}")
+                if item.description:
+                    print(f"  Description: {item.description}")
+                print(f"  Usage: agent runtask --task {item.name}")
+            exit(1)
+    # Get the words before any flags.
+    user_query = ""
+    if words == () and (task == () or task == None) and (task_config == "" or task_config == None or task_config == os.path.join(get_project_path(), "tasks")):
+        print()
+        print("Please specify a task. These are the available tasks:")
+        for item in tasks_config.tasks:
+            print()
+            print(f"- {item.name}")
+            if item.description:
+                print(f"  Description: {item.description}")
+            print(f"  Usage: agent runtask --task {item.name}")
+        exit(1)
+    for word in words:
+        user_query += word + " "
+    if user_query != "":
+        # Ask the model and retrieve the response.
+        # md_cb_pattern = r"^```(?:\w+)?\s*\n(.*?)(?=^```)```"
+        md_cb_regex = r"```\n(.*)\n```\s*((.|\n|\s)*)"
+        task_assist = f"Given this question: {user_query}, which name of this list of values: {tasks_config} may be the best match? Return the task name in a markdown code block such as name=<name>. Then, justify your response below that. If you cannot find a good match for the task in the provided list and the user is asking a conceptual question, return name=tellme. If you think the user is asking for help with a task and not a conceptual question, return name=helpme. Return name=tellme if you think the user query is a conceptual question.Return name=helpme if you are unsure."
+        # This should be fixed to not rely on product_config
+        if model is None:
+            model = "models/gemini-1.5-flash-latest"
+        loaded_config, product_configs = return_config_and_product(model=model)
+        output = console.ask_model_without_context(
+            task_assist, product_configs, return_output=True
+        )
+        try:
+            result = re.match(md_cb_regex, output)
+            if result is None:
+                print(f"The LLM did not return a valid response for {user_query}. Try again.")
+                exit(1)
+            found = re.search("^name=(.*)$", result.group(1))
+            if found is None:
+                print(f"The LLM did not return a valid response for {user_query}. Try again.")
+                exit(1)
+            # Prints the remaining response from model
+            print()
+            print(result.group(2))
+            if (
+                found.group(1) != "helpme"
+                and found.group(1) != "tellme"
+                and click.confirm(
+                    f"Should I execute the {found.group(1)} task?",
+                    abort=True,
+                )
+            ):
+                print()
+                ctx.invoke(
+                    runtask,
+                    words="",
+                    force=True,
+                    task=found.group(1).split(),
+                    model="models/gemini-1.5-flash-latest",
+                )
+                exit(1)
+            elif found.group(1) == "helpme":
+            #     and click.confirm(
+            #     f"Should I try to help you with {user_query}?",
+            #     abort=True,
+            # ):
+                print()
+                ctx.invoke(
+                    helpme,
+                    words=user_query.split(),
+                    force=True,
+                    new=True,
+                    model="models/gemini-1.5-flash-latest",
+                )
+                print("Follow-up usage: agent helpme <more text> --cont --models/gemini-1.5-flash-latest")
+                exit(1)
+            elif found.group(1) == "tellme":
+            # and click.confirm(
+            #     f"Should I tell you more about your question - {user_query}?",
+            #     abort=True,
+            # ):
+                print()
+                # Call helpme instead of tellme until rag is properly hooked in
+                ctx.invoke(
+                    tellme,
+                    words=user_query.split(),
+                )
+                # print("Follow-up usage: agent helpme <more text> --cont")
+                exit(1)
+            else:
+                print()
+                ctx.invoke(
+                    helpme,
+                    words=user_query.split(),
+                    force=True,
+                    model="models/gemini-1.5-flash-latest",
+                )
+                print("Follow-up usage: agent helpme <more text> --cont")
+                exit(1)
+        except:
+            exit(1)
+    for curr_task in tasks_config.tasks:
+        # Extract the top level model specified in the task config.
+        top_level_model = ""
+        if model is not None:
+            top_level_model = model
+        else:
+            top_level_model = curr_task.model
+        if not top_level_model.startswith("models/gemini"):
+            click.echo(
+                f"runtask mode is not supported with this model: {top_level_model} for {curr_task.name}"
+            )
+            exit(1)
+
+        # Extract the preamble in the task config.
+        this_preamble = ""
+        if (
+            hasattr(curr_task, "preamble")
+            and curr_task.preamble is not None
+            and curr_task.preamble != ""
+        ):
+            this_preamble = curr_task.preamble
+
+        # Ask the user to confirm.
+        if force or click.confirm(
+            f"\nPreparing to launch task:\n\n"
+            + f"{curr_task}"
+            + f"Do you want to launch the task?",
+            abort=True,
+        ):
+            print()
+            if use_panel:
+                ai_console.print(f"Starting task: {curr_task.name}", style="cyan")
+            else:
+                print(f"Starting task: {curr_task.name}")
+                # print(f"{curr_task}")
+            this_step = 0
+            for task in curr_task.steps:
+                this_step += 1
+                # Determine the states of the `is_new` and `is_cont` flags.
+                is_new = False
+                is_cont = True
+                # The first step is always new by default.
+                if int(this_step) == 1:
+                    is_new = True
+                if hasattr(task, "flags"):
+                    if (
+                        hasattr(task.flags, "new")
+                        and task.flags.new is not None
+                        and task.flags.new != ""
+                    ):
+                        if bool(task.flags.new):
+                            is_new = True
+                        else:
+                            is_new = False
+                    # `is_cont` is always True, unless specified.
+                    if (
+                        hasattr(task.flags, "cont")
+                        and task.flags.cont is not None
+                        and task.flags.cont != ""
+                    ):
+                        if bool(task.flags.cont):
+                            is_cont = True
+                        else:
+                            is_cont = False
+
+                # If is_new is True, is_cont is always False.
+                if is_new:
+                    is_cont = False
+
+                # Select the model for this task. Use the top level model by default.
+                this_model = top_level_model
+                if hasattr(task, "flags"):
+                    if hasattr(task.flags, "model"):
+                        if task.flags.model is not None and task.flags.model != "":
+                            this_model = task.flags.model
+
+                # Determine the values for these fields used by helpme and tellme.
+                this_file = None
+                this_perfile = None
+                this_allfiles = None
+                this_file_ext = None
+                this_out = None
+                this_yaml = None
+                this_rag = None
+                this_terminal = None
+                if hasattr(task, "flags"):
+                    if hasattr(task.flags, "file"):
+                        this_file = task.flags.file
+                    if hasattr(task.flags, "perfile"):
+                        this_perfile = task.flags.perfile
+                    if hasattr(task.flags, "allfiles"):
+                        this_allfiles = task.flags.allfiles
+                    if hasattr(task.flags, "file_ext"):
+                        this_file_ext = task.flags.file_ext
+                    if hasattr(task.flags, "out"):
+                        this_out = task.flags.out
+                    if hasattr(task.flags, "yaml"):
+                        this_yaml = task.flags.yaml
+                    if hasattr(task.flags, "rag"):
+                        this_rag = task.flags.rag
+                    if hasattr(task.flags, "terminal"):
+                        this_terminal = task.flags.terminal
+
+                # Set the out filename to the default name.
+                if this_out is None or this_out == "":
+                    this_out = (
+                        "out-"
+                        + curr_task.name
+                        + "-step-"
+                        + "{:02d}".format(this_step)
+                        + ".md"
+                    )
+
+                # Select the command type: helpme, tellme, posix
+                if task.function == "helpme":
+                    # helpme Task
+                    # Render this step information.
+                    if use_panel:
+                        print()
+                        ai_console.print(
+                            Panel(
+                                f"Prompt (helpme): {task.prompt}",
+                                title=f"Step {this_step}. {task.name}",
+                                title_align="left",
+                                padding=(1, 2),
+                            ),
+                            style=console_style,
+                        )
+                        print()
+                    else:
+                        print()
+                        print(f"===================")
+                        print(f"Starting task: {task.name}")
+                        print(f"Prompt: {task.prompt}")
+                        print(f"===================")
+                        print()
+                    overwrite_words = this_preamble + "\n" + task.prompt
+                    overwrite_words = overwrite_words.split()
+                    ctx.invoke(
+                        helpme,
+                        words=overwrite_words,
+                        force=True,
+                        file=this_file,
+                        perfile=this_perfile,
+                        allfiles=this_allfiles,
+                        file_ext=this_file_ext,
+                        out=this_out,
+                        yaml=this_yaml,
+                        rag=this_rag,
+                        new=bool(is_new),
+                        cont=bool(is_cont),
+                        panel=bool(use_panel),
+                        terminal=this_terminal,
+                        model=this_model,
+                    )
+                elif task.function == "tellme":
+                    # tellme Task
+                    # Note: Usually don't want to overwrite model from curr_task.model in
+                    # case it is aqa.
+                    # Render this step information.
+                    if use_panel:
+                        print()
+                        ai_console.print(
+                            Panel(
+                                f"Prompt (tellme): {task.prompt}",
+                                title=f"Step {this_step}. {task.name}",
+                                title_align="left",
+                                padding=(1, 2),
+                            ),
+                            style=console_style,
+                        )
+                        print()
+                    else:
+                        print()
+                        print(f"===================")
+                        print(f"Answering question: {task.name}")
+                        print(f"Prompt: {task.prompt}")
+                        print(f"===================")
+                        print()
+                    overwrite_words = this_preamble + "\n" + task.prompt
+                    overwrite_words = overwrite_words.split()
+                    ctx.invoke(
+                        tellme,
+                        words=overwrite_words,
+                        new=bool(is_new),
+                        cont=bool(is_cont),
+                        model=this_model,
+                    )
+                elif task.function == "posix":
+                    # Render this step information.
+                    if use_panel:
+                        print()
+                        ai_console.print(
+                            Panel(
+                                f"Command (posix): {task.prompt}",
+                                title=f"Step {this_step}. {task.name}",
+                                title_align="left",
+                                padding=(1, 2),
+                            ),
+                            style=console_style,
+                        )
+                        print()
+                    else:
+                        print()
+                        print(f"===================")
+                        print(f"Running POSIX command: {task.name}")
+                        print(f"Command: {task.prompt}")
+                        print(f"===================")
+                        print()
+                    overwrite_words = task.prompt
+                    overwrite_words = overwrite_words.split()
+                    ctx.invoke(
+                        posix,
+                        words=overwrite_words,
+                        new=bool(is_new),
+                        cont=bool(is_cont),
+                    )
+                else:
+                    logging.error("Unsupported task function: %s", task.function)
+                    exit(1)
+                time.sleep(3)
+
+
+cli = click.CommandCollection(
+    sources=[cli_runtask],
+    help="With Docs Agent, you can interact with Google's Gemini models.",
+)
+
+
+if __name__ == "__main__":
+    cli()
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_show_session.py b/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_show_session.py
new file mode 100644
index 000000000..9a49c0e69
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_show_session.py
@@ -0,0 +1,59 @@
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Docs Agent CLI client"""
+
+import click
+import typing
+from docs_agent.utilities import config
+
+from docs_agent.interfaces.cli.cli_common import common_options
+from docs_agent.interfaces.cli.cli_common import show_config
+
+
+@click.group(invoke_without_command=True)
+@common_options
+@click.pass_context
+def cli_show_session(ctx, config_file, product):
+    """With Docs Agent, you can interact with Google's Gemini
+    models and manage online corpora on Google Cloud."""
+    ctx.ensure_object(dict)
+    # Print config.yaml if agent is run without a command.
+    if ctx.invoked_subcommand is None:
+        click.echo("Docs Agent configuration:\n")
+        show_config()
+
+
+@cli_show_session.command(name="show-session")
+@common_options
+def show_session(config_file: typing.Optional[str] = None, product: list[str] = [""]):
+    """Print the questions and responses being tracked in the current session."""
+    # Set the filename for recording exchanges with the Gemini models.
+    history_file = "/tmp/docs_agent_responses"
+    with open(history_file, "r", encoding="utf-8") as target_file:
+        context = target_file.read()
+        target_file.close()
+    print(context.strip() + "\n")
+
+
+cli = click.CommandCollection(
+    sources=[cli_show_session],
+    help="With Docs Agent, you can interact with Google's Gemini models.",
+)
+
+
+if __name__ == "__main__":
+    cli()
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_tellme.py b/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_tellme.py
new file mode 100644
index 000000000..155c00841
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_tellme.py
@@ -0,0 +1,127 @@
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Docs Agent CLI client"""
+
+import click
+import typing
+from docs_agent.utilities import config
+from docs_agent.utilities.config import ConfigFile
+from docs_agent.utilities.config import return_config_and_product
+
+from docs_agent.interfaces import run_console as console
+from docs_agent.interfaces.cli.cli_common import common_options
+from docs_agent.interfaces.cli.cli_common import show_config
+import time
+
+
+@click.group(invoke_without_command=True)
+@common_options
+@click.pass_context
+def cli_tellme(ctx, config_file, product):
+    """With Docs Agent, you can interact with Google's Gemini
+    models and manage online corpora on Google Cloud."""
+    ctx.ensure_object(dict)
+    # Print config.yaml if agent is run without a command.
+    if ctx.invoked_subcommand is None:
+        click.echo("Docs Agent configuration:\n")
+        show_config()
+
+
+@cli_tellme.command(name="tellme")
+@click.argument("words", nargs=-1)
+@click.option(
+    "--model",
+    help="Specify a language model.",
+    default=None,
+    multiple=False,
+)
+@click.option(
+    "--new",
+    is_flag=True,
+    help="Start a new session.",
+)
+@click.option(
+    "--cont",
+    is_flag=True,
+    help="Use the previous responses in the session as context.",
+)
+@click.option(
+    "--sleep",
+    type=int,
+    default=0,
+    help="Sleep for a specified duration (in seconds) after completing the command.",
+    hidden=True,
+)
+@common_options
+def tellme(
+    words,
+    config_file: typing.Optional[str] = None,
+    new: bool = False,
+    cont: bool = False,
+    sleep: int = 0,
+    product: list[str] = [""],
+    model: typing.Optional[str] = None,
+):
+    """Answer a question related to the product."""
+    # Loads configurations from common options
+    loaded_config, product_configs = return_config_and_product(
+        config_file=config_file, product=product, model=model
+    )
+    if product == ():
+        # Extracts the first product by default
+        product_config = ConfigFile(products=[product_configs.products[0]])
+    else:
+        product_config = product_configs
+
+    # Set the filename for recording exchanges with the Gemini models.
+    history_file = "/tmp/docs_agent_responses"
+
+    # Get the question string.
+    question = ""
+    for word in words:
+        question += word + " "
+    question = question.strip()
+
+    # Ask the model and retrieve the response.
+    this_output = console.ask_model(question, product_config, return_output=True)
+
+    # If the `--new` flag is set, overwrite the history file.
+    write_mode = "None"
+    if new:
+        write_mode = "w"
+    elif cont:
+        write_mode = "a"
+    if write_mode != "None":
+        # Record this exchange in the history file.
+        with open(history_file, write_mode, encoding="utf-8") as out_file:
+            out_file.write(f"QUESTION: {question}\n\n")
+            out_file.write(f"RESPONSE:\n\n{this_output}\n\n")
+            out_file.close()
+
+    # When the --sleep flag is provided, sleep for the specified duration.
+    if sleep > 0:
+        time.sleep(int(sleep))
+
+
+cli = click.CommandCollection(
+    sources=[cli_tellme],
+    help="With Docs Agent, you can interact with Google's Gemini models.",
+)
+
+
+if __name__ == "__main__":
+    cli()
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/run_console.py b/examples/gemini/python/docs-agent/docs_agent/interfaces/run_console.py
index 84b3ca775..3374441cd 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/run_console.py
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/run_console.py
@@ -57,7 +57,7 @@ def ask_model_for_help(question: str, context: str, product_configs: ConfigFile)
 
 # This function is used by the `tellme` command to ask the Gemini AQA model
 # a question from an online corpus.
-def ask_model(question: str, product_configs: ConfigFile):
+def ask_model(question: str, product_configs: ConfigFile, return_output: bool = False):
     # Initialize Rich console
     ai_console = Console(width=160)
     full_prompt = ""
@@ -167,6 +167,8 @@ def ask_model(question: str, product_configs: ConfigFile):
             final_response_md += responses[count] + "\n"
             final_links.append(links[count])
             count += 1
+
+        # Print the response.
         count = 0
         synthesize = False
         synthesize_product = None
@@ -174,6 +176,7 @@ def ask_model(question: str, product_configs: ConfigFile):
         for product in product_configs.products:
             if "gemini" in product.models.language_model:
                 synthesize_product = product
+
         if synthesize and not (synthesize_product == None):
             docs_agent = DocsAgent(config=synthesize_product, init_chroma=False)
             progress.update(
@@ -197,23 +200,67 @@ def ask_model(question: str, product_configs: ConfigFile):
             progress.update(task_docs_agent, visible=False, refresh=True)
             ai_console.print()
             ai_console.print(Markdown(final_response_md))
-        # Print results
-        for item in final_search:
-            count += 1
-        count = 0
-        ai_console.print()
-        ai_console.print(Markdown("To verify this information, see:\n"))
+
+        # Get the link to the source.
         md_links = ""
         for item in final_links:
             if isinstance(item, str):
                 if not item.startswith("UUID"):
                     md_links += f"\n* [{item}]({item})\n"
-                    count += 1
+
+        # Print the link to the source.
+        ai_console.print()
+        ai_console.print(Markdown("To verify this information, see:\n"))
         ai_console.print(Markdown(md_links))
 
+        # Retrun the output if the `return_output` flag is set.
+        if return_output:
+            return_string = (
+                str(final_response_md.strip())
+                + "\n\nTo verify this information, see:\n"
+                + md_links
+            )
+            return return_string
 
-# This function is used by the `helpme` command to ask the Gemini AQA model
-# a question from an online corpus.
+
+# This function is used by the `helpme` command to ask a Gemini model
+# a question without additional context.
+def ask_model_without_context(
+    question: str,
+    product_configs: ConfigFile,
+    return_output: bool = False,
+):
+    # Initialize Rich console
+    ai_console = Console(width=160)
+    full_prompt = ""
+    final_context = ""
+    response = ""
+
+    # Use the first product by default.
+    product = product_configs.products[0]
+    language_model = product.models.language_model
+    with Progress(transient=True) as progress:
+        task_docs_agent = progress.add_task(
+            "[turquoise4 bold]Starting Docs Agent ", total=None, refresh=True
+        )
+        # Initialize Docs Agent.
+        docs_agent = DocsAgent(config=product, init_chroma=False, init_semantic=False)
+        # Set the progress bar.
+        label = f"[turquoise4 bold]Asking Gemini (model: {language_model}) "
+        progress.update(task_docs_agent, description=label, total=None, refresh=True)
+        final_context = "No additional context is necessary for this question."
+        # Ask Gemini with the question without additional context.
+        response = docs_agent.ask_content_model_with_context(
+            context=final_context, question=question
+        )
+    if return_output:
+        return str(response.strip())
+    ai_console.print()
+    ai_console.print(Markdown(response.strip()))
+
+
+# This function is used by the `helpme` command to ask a Gemini model
+# a question with various context sources.
 def ask_model_with_file(
     question: str,
     product_configs: ConfigFile,
diff --git a/examples/gemini/python/docs-agent/docs_agent/memory/logging.py b/examples/gemini/python/docs-agent/docs_agent/memory/logging.py
index fc443c169..c2625e8da 100644
--- a/examples/gemini/python/docs-agent/docs_agent/memory/logging.py
+++ b/examples/gemini/python/docs-agent/docs_agent/memory/logging.py
@@ -25,7 +25,17 @@
 
 # Save this question and response pair as a file.
 def log_question_to_file(user_question: str, response: str, probability: str = "None"):
-    filename = str(user_question).lower().replace(" ", "-").replace("?", "")
+    filename = (
+        str(user_question)
+        .lower()
+        .replace(" ", "-")
+        .replace("?", "")
+        .replace("'", "")
+        .replace("`", "")
+    )
+    filename = re.sub("[^a-zA-Z0-9\-]", "", filename)
+    if len(filename) > 64:
+        filename = filename[:64]
     log_dir = "./logs/responses"
     log_filename = f"{log_dir}/{filename}.md"
     if not os.path.exists(log_dir):
@@ -66,7 +76,17 @@ def log_debug_info_to_file(
     date = datetime.now(tz=pytz.utc)
     date = date.astimezone(pytz.timezone("US/Pacific"))
     date_formatted = str(date.strftime("%Y-%m-%d-%H-%M-%S"))
-    question_formatted = str(user_question).lower().replace(" ", "-").replace("?", "")
+    question_formatted = (
+        str(user_question)
+        .lower()
+        .replace(" ", "-")
+        .replace("?", "")
+        .replace("'", "")
+        .replace("`", "")
+    )
+    question_formatted = re.sub("[^a-zA-Z0-9\-]", "", question_formatted)
+    if len(question_formatted) > 32:
+        question_formatted = question_formatted[:32]
     filename = date_formatted + "-" + question_formatted + "-" + str(uid) + ".txt"
     # Set the directory location.
     debug_dir = "./logs/debugs"
diff --git a/examples/gemini/python/docs-agent/docs_agent/utilities/config.py b/examples/gemini/python/docs-agent/docs_agent/utilities/config.py
index aa1846d93..bf815842a 100644
--- a/examples/gemini/python/docs-agent/docs_agent/utilities/config.py
+++ b/examples/gemini/python/docs-agent/docs_agent/utilities/config.py
@@ -49,13 +49,22 @@ def __init__(
         self.secondary_corpus_name = secondary_corpus_name
 
     def __str__(self):
-        return f"Database type: {self.db_type}\n\
-Vector database dir: {self.vector_db_dir}\n\
-Collection name: {self.collection_name}\n\
-Corpus name: {self.corpus_name}\n\
-Corpus display: {self.corpus_display}\n\
-Secondary database type: {self.secondary_db_type}\n\
-Secondary corpus name: {self.secondary_corpus_name}\n"
+        help_str = ""
+        help_str += f"Database type: {self.db_type}\n"
+        if self.vector_db_dir is not None and self.vector_db_dir != "":
+            help_str += f"Vector database dir: {self.vector_db_dir}\n"
+        if self.collection_name is not None and self.collection_name != "":
+            help_str += f"Collection name: {self.collection_name}\n"
+        if self.corpus_name is not None and self.corpus_name != "":
+            help_str += f"Corpus name: {self.corpus_name}\n"
+        if self.corpus_display is not None and self.corpus_display != "":
+            help_str += f"Corpus display: {self.corpus_display}\n"
+        if self.secondary_db_type is not None and self.secondary_db_type != "":
+            help_str += f"Secondary database type: {self.secondary_db_type}\n"
+        if self.secondary_corpus_name is not None and self.secondary_corpus_name != "":
+            help_str += f"Secondary corpus name: {self.secondary_corpus_name}\n"
+        return help_str
+
 
     def return_vector_db_dir(self):
         return self.vector_db_dir
@@ -118,10 +127,16 @@ def __init__(
         self.exclude_path = exclude_path
 
     def __str__(self):
-        return f"Path: {self.path}\n\
-URL prefix: {self.url_prefix}\n\
-Include path html: {self.include_path_html}\n\
-Exclude path: {self.exclude_path}\n"
+        help_str = ""
+        if self.path is not None and self.path != "":
+            help_str += f"Path: {self.path}\n"
+        if self.url_prefix is not None and self.url_prefix != "":
+            help_str += f"URL prefix: {self.url_prefix}\n"
+        if self.include_path_html is not None and self.include_path_html != "":
+            help_str += f"Include path html: {self.include_path_html}\n"
+        if self.exclude_path is not None and self.exclude_path != "":
+            help_str += f"Exclude path: {self.exclude_path}\n"
+        return help_str
 
 
 class ReadInputs:
@@ -187,12 +202,20 @@ def __init__(
             self.embedding_api_call_period = embedding_api_call_period
 
     def __str__(self):
-        return f"Language model: {self.language_model}\n\
-Embedding model: {self.embedding_model}\n\
-API key: {self.api_key}\n\
-API endpoint: {self.api_endpoint}\n\
-Embedding API call limit: {str(self.embedding_api_call_limit)}\n\
-Embedding API call period: {str(self.embedding_api_call_period)}\n"
+        help_str = ""
+        if self.language_model is not None and self.language_model != "":
+            help_str += f"Language model: {self.language_model}\n"
+        if self.embedding_model is not None and self.embedding_model != "":
+            help_str += f"Embedding model: {self.embedding_model}\n"
+        if self.api_key is not None and self.api_key != "":
+            help_str += f"API key: {self.api_key}\n"
+        if self.api_endpoint is not None and self.api_endpoint != "":
+            help_str += f"API endpoint: {self.api_endpoint}\n"
+        if self.embedding_api_call_limit is not None and self.embedding_api_call_limit != "":
+            help_str += f"Embedding API call limit: {self.embedding_api_call_limit}\n"
+        if self.embedding_api_call_period is not None and self.embedding_api_call_period != "":
+            help_str += f"Embedding API call period: {self.embedding_api_call_period}\n"
+        return help_str
 
 
 class ReadModels:
@@ -256,9 +279,14 @@ def __init__(
             self.model_error_message = model_error_message
 
     def __str__(self):
-        return f"Condition text: {self.condition_text}\n\
-Fact check question: {self.fact_check_question}\n\
-Model error message: {self.model_error_message}\n"
+        help_str = ""
+        if self.condition_text is not None and self.condition_text != "":
+            help_str += f"Condition text: {self.condition_text}\n"
+        if self.fact_check_question is not None and self.fact_check_question != "":
+            help_str += f"Fact check question: {self.fact_check_question}\n"
+        if self.model_error_message is not None and self.model_error_message != "":
+            help_str += f"Model error message: {self.model_error_message}\n"
+        return help_str
 
 
 class ReadConditions:
@@ -345,25 +373,46 @@ def __str__(self):
         for item in self.db_configs:
             dbconfigs.append(str(item))
         db_config_str = "\n".join(dbconfigs)
-        return f"Product: {self.product_name}\n\
-Docs Agent config: {self.docs_agent_config}\n\
-App mode: {self.app_mode}\n\
-App port: {self.app_port}\n\
-Feedback mode: {self.feedback_mode}\n\
-Enable show logs: {self.enable_show_logs}\n\
-Enable logs to Markdown: {self.enable_logs_to_markdown}\n\
-Enable logs for debugging: {self.enable_logs_for_debugging}\n\
-Enable delete chunks: {self.enable_delete_chunks}\n\
-Markdown splitter: {self.markdown_splitter}\n\
-Database type: {self.db_type}\n\
-Secondary database type: {self.secondary_db_type}\n\
-Secondary corpus name: {self.secondary_corpus_name}\n\
-Output path: {self.output_path}\n\
-\nDatabase configs:\n{db_config_str}\n\
-Log level: {self.log_level}\n\
-\nModels:\n{self.models}\n\
-\nInputs:\n{input_str}\n\
-Conditions:\n{self.conditions}"
+        help_str = ""
+        if self.product_name is not None and self.product_name != "":
+            help_str += f"Product: {self.product_name}\n"
+        if self.docs_agent_config is not None and self.docs_agent_config != "":
+            help_str += f"Docs Agent config: {self.docs_agent_config}\n"
+        if self.app_mode is not None and self.app_mode != "":
+            help_str += f"App mode: {self.app_mode}\n"
+        if self.app_port is not None and self.app_port != "":
+            help_str += f"App port: {self.app_port}\n"
+        if self.feedback_mode is not None and self.feedback_mode != "":
+            help_str += f"Feedback mode: {self.feedback_mode}\n"
+        if self.enable_show_logs is not None and self.enable_show_logs != "":
+            help_str += f"Enable show logs: {self.enable_show_logs}\n"
+        if self.enable_logs_to_markdown is not None and self.enable_logs_to_markdown != "":
+            help_str += f"Enable logs to Markdown: {self.enable_logs_to_markdown}\n"
+        if self.enable_logs_for_debugging is not None and self.enable_logs_for_debugging != "":
+            help_str += f"Enable logs for debugging: {self.enable_logs_for_debugging}\n"
+        if self.enable_delete_chunks is not None and self.enable_delete_chunks != "":
+            help_str += f"Enable delete chunks: {self.enable_delete_chunks}\n"
+        if self.markdown_splitter is not None and self.markdown_splitter != "":
+            help_str += f"Markdown splitter: {self.markdown_splitter}\n"
+        if self.db_type is not None and self.db_type != "":
+            help_str += f"Database type: {self.db_type}\n"
+        if self.secondary_db_type is not None and self.secondary_db_type != "":
+            help_str += f"Secondary database type: {self.secondary_db_type}\n"
+        if self.secondary_corpus_name is not None and self.secondary_corpus_name != "":
+            help_str += f"Secondary corpus name: {self.secondary_corpus_name}\n"
+        if self.output_path is not None and self.output_path != "":
+            help_str += f"Output path: {self.output_path}\n"
+        if db_config_str != "":
+            help_str += f"\nDatabase configs:\n{db_config_str}\n"
+        if self.log_level is not None and self.log_level != "":
+            help_str += f"Log level: {self.log_level}\n"
+        if self.models is not None and self.models != "":
+            help_str += f"\nModels:\n{self.models}\n"
+        if input_str != "":
+            help_str += f"\nInputs:\n{input_str}\n"
+        if self.conditions is not None and self.conditions != "":
+            help_str += f"Conditions:\n{self.conditions}\n"
+        return help_str
 
 
 # This class is used to store the content of a list of
@@ -420,7 +469,13 @@ def returnProducts(self, product: typing.Optional[str] = None) -> ConfigFile:
                     logging.error(f"Your configuration is missing a {error}")
                     return sys.exit()
                 # Set the default value of `app_mode` to "web"
-                supported_app_modes = ["web", "experimental", "widget", "1.5"]
+                supported_app_modes = [
+                    "web",
+                    "full",
+                    "widget",
+                    "widget-pro",
+                    "experimental",
+                ]
                 try:
                     app_mode = item["app_mode"]
                 except KeyError:
@@ -478,6 +533,7 @@ def returnProducts(self, product: typing.Optional[str] = None) -> ConfigFile:
                         app_port=app_port,
                         feedback_mode=feedback_mode,
                         enable_show_logs=enable_show_logs,
+                        enable_logs_to_markdown=enable_logs_to_markdown,
                         enable_logs_for_debugging=enable_logs_for_debugging,
                         enable_delete_chunks=enable_delete_chunks,
                         secondary_db_type=secondary_db_type,
@@ -535,14 +591,16 @@ def returnProducts(self, product: typing.Optional[str] = None) -> ConfigFile:
 
 # Function to make using common_options simpler
 def return_config_and_product(
-    config_file: typing.Optional[str] = None, product: list[str] = [""], model: typing.Optional[str] = None
+    config_file: typing.Optional[str] = None,
+    product: list[str] = [""],
+    model: typing.Optional[str] = None,
 ):
     if config_file is None:
         loaded_config = ReadConfig()
     else:
         loaded_config = ReadConfig(yaml_path=config_file)
     final_products = []
-    if product == ():
+    if product == () or product == [""]:
         product_config = loaded_config.returnProducts()
         final_products = product_config.products
     else:
diff --git a/examples/gemini/python/docs-agent/docs_agent/utilities/tasks.py b/examples/gemini/python/docs-agent/docs_agent/utilities/tasks.py
new file mode 100644
index 000000000..ad6b85ef2
--- /dev/null
+++ b/examples/gemini/python/docs-agent/docs_agent/utilities/tasks.py
@@ -0,0 +1,389 @@
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Read the configuration file to import tasks"""
+
+import os
+import sys
+import yaml
+import typing
+
+from absl import logging
+from docs_agent.utilities.helpers import get_project_path, resolve_path
+
+
+class Flags:
+    def __init__(
+        self,
+        model: typing.Optional[str] = None,
+        file: typing.Optional[str] = None,
+        perfile: typing.Optional[str] = None,
+        allfiles: typing.Optional[str] = None,
+        file_ext: typing.Optional[str] = None,
+        rag: typing.Optional[bool] = False,
+        yaml: typing.Optional[str] = None,
+        out: typing.Optional[str] = None,
+        new: typing.Optional[bool] = None,
+        cont: typing.Optional[str] = None,
+        terminal: typing.Optional[str] = None,
+    ):
+        self.model = model
+        self.file = file
+        self.perfile = perfile
+        self.allfiles = allfiles
+        self.file_ext = file_ext
+        self.rag = rag
+        self.yaml = yaml
+        self.out = out
+        self.new = new
+        self.cont = cont
+        self.terminal = terminal
+
+    def __str__(self):
+        help_str = ""
+        if self.model is not None or self.model != "":
+            help_str += f"Model: {self.model}\n"
+        if self.file is not None and self.file != "":
+            help_str += f"File: {self.file}\n"
+        if self.perfile is not None and self.perfile != "":
+            help_str += f"Per file: {self.perfile}\n"
+        if self.allfiles is not None and self.allfiles != "":
+            help_str += f"All files: {self.allfiles}\n"
+        if self.file_ext is not None and self.file_ext != "":
+            help_str += f"File ext: {self.file_ext}\n"
+        if self.rag is not None and self.rag!= False:
+            help_str += f"RAG: {str(self.rag)}\n"
+        if self.yaml is not None and self.yaml != "":
+            help_str += f"YAML: {self.yaml}\n"
+        if self.out is not None and self.out != "":
+            help_str += f"Out: {self.out}\n"
+        if self.new is not None and self.new != False:
+            help_str += f"New: {str(self.new)}\n"
+        if self.cont is not None and self.cont != "":
+            help_str += f"Cont: {self.cont}\n"
+        if self.terminal is not None and self.terminal != "":
+            help_str += f"Terminal: {self.terminal}\n"
+        return help_str
+
+
+def dictionaryToFlags(flags: dict) -> Flags:
+    if "model" in flags:
+        model = str(flags["model"])
+    else:
+        model = ""
+    if "file" in flags:
+        file = str(flags["file"])
+    else:
+        file = ""
+    if "perfile" in flags:
+        perfile = str(flags["perfile"])
+    else:
+        perfile = ""
+    if "allfiles" in flags:
+        allfiles = str(flags["allfiles"])
+    else:
+        allfiles = ""
+    if "file_ext" in flags:
+        file_ext = str(flags["file_ext"])
+    else:
+        file_ext = ""
+    if "rag" in flags:
+        rag = bool(flags["rag"])
+    else:
+        rag = False
+    if "yaml" in flags:
+        yaml = str(flags["yaml"])
+    else:
+        yaml = ""
+    if "out" in flags:
+        out = str(flags["out"])
+    else:
+        out = ""
+    if "new" in flags:
+        new = bool(flags["new"])
+    else:
+        new = None
+    if "cont" in flags:
+        cont = str(flags["cont"])
+    else:
+        cont = ""
+    if "terminal" in flags:
+        terminal = str(flags["terminal"])
+    else:
+        terminal = ""
+    flags = Flags(
+        model=model,
+        file=file,
+        perfile=perfile,
+        allfiles=allfiles,
+        file_ext=file_ext,
+        rag=rag,
+        yaml=yaml,
+        out=out,
+        new=new,
+        cont=cont,
+        terminal=terminal,
+    )
+    return flags
+
+
+class Step:
+    def __init__(
+        self,
+        name: str,
+        function: typing.Optional[str],
+        prompt: typing.Optional[str],
+        flags: typing.Optional[Flags] = None,
+        description: typing.Optional[str] = None,
+    ):
+        self.name = name
+        self.function = function
+        self.prompt = prompt
+        self.flags = flags
+        self.description = description
+
+    def __str__(self):
+        help_str = ""
+        if self.name is not None or self.name != "":
+            help_str += f"Task: {self.name}\n"
+        if self.description is not None or self.description != "":
+            help_str += f"Description: {self.description}\n"
+        if self.function is not None or self.function != "":
+            help_str += f"Function: {self.function}\n"
+        if self.prompt is not None or self.prompt != "":
+            help_str += f"Prompt: {self.prompt}\n"
+        if self.flags is not None:
+            help_str += f"Flags:\n{self.flags}\n"
+        return help_str
+
+
+class ReadSteps:
+    # Tries to ingest a list of dictionaries with step_list values
+    def __init__(self, step_list: list[dict]):
+        self.step_list = step_list
+
+    def returnSteps(self) -> list[Step]:
+        inputs = []
+        for item in self.step_list:
+            try:
+                # Creates a Flags object from the dictionary
+                if "flags" in item:
+                    flags = dictionaryToFlags(item["flags"])
+                else:
+                    flags = None
+                # Using .get let's you specify optional keys
+                step_item = Step(
+                    name=item["name"],
+                    function=item["function"],
+                    prompt=item["prompt"],
+                    flags=flags,
+                    description=item.get("description", None),
+                )
+                inputs.append(step_item)
+            except KeyError as error:
+                logging.error(f"The task is missing a key {error}")
+                # Exits the scripts if there is missing input keys
+                return sys.exit(1)
+        return inputs
+
+    def __str__(self):
+        return self.step_list
+
+
+# Class to define an task that performs tasks
+class TaskConfig:
+    def __init__(
+        self,
+        name: str,
+        steps: list[Step],
+        # Required as a main model. Any task can overwrite this value with it's
+        # own model.
+        model: str,
+        preamble: typing.Optional[str] = None,
+        description: typing.Optional[str] = None,
+    ):
+        self.name = name
+        self.model = model
+        self.preamble = preamble
+        self.description = description
+        self.steps = steps
+
+    def __str__(self):
+        help_str = ""
+        if self.name is not None:
+            help_str += f"Name: {self.name}\n"
+        if self.model is not None:
+            help_str += f"Model: {self.model}\n"
+        if self.description is not None:
+            help_str += f"Description: {self.description}\n"
+        if self.preamble is not None:
+            help_str += f"Preamble: {self.preamble}\n"
+        # Extracts the list of Steps
+        steps = []
+        if self.steps is not None:
+            for step in self.steps:
+                steps.append(str(step))
+            task_str = "\n".join(steps)
+            help_str += f"\nSteps:\n\n{task_str}\n"
+        return help_str
+
+
+# This class is used to store the content of a list of
+# TaskConfig
+class TaskConfigFile:
+    def __init__(
+        self,
+        tasks: list[TaskConfig],
+    ):
+        self.tasks = tasks
+
+    def __str__(self):
+        output =  []
+        for item in self.tasks:
+            output.append(str(item))
+        return str(output)
+
+    def return_first(self):
+        return self.tasks[0]
+
+    def return_task(self, task: str):
+        for item in self.tasks:
+            if item.name == task:
+                return TaskConfigFile(tasks=[item])
+
+
+# Class used to read a specific task configuration file or defaults
+# to a yaml file in the tasks directory of the project
+# printing the object returns the path as a string
+# returnTasks() with an optional task flag will return
+# all task configurations or the specified one
+class ReadTaskConfig:
+    # Tries to ingest a task configuration file and validate its keys.
+    def __init__(
+        self, yaml_path: str = os.path.join(get_project_path(), "tasks/release-notes-task.yaml")
+    ):
+        self.yaml_path = yaml_path
+        try:
+            with open(self.yaml_path, "r", encoding="utf-8") as inp_yaml:
+                self.config_values = yaml.safe_load(inp_yaml)
+        except FileNotFoundError:
+            logging.error(f"The task file {self.yaml_path} does not exist.")
+            return sys.exit(1)
+
+    def __str__(self):
+        return self.yaml_path
+
+    def returnTasks(self, task: typing.Optional[str] = None) -> TaskConfigFile:
+        tasks = []
+        try:
+            for item in self.config_values["tasks"]:
+                name = ""
+                try:
+                    name = item["name"]
+                except KeyError as error:
+                    logging.error(f"Your task configuration is missing a {error}")
+                    return sys.exit()
+                try:
+                    name = item["model"]
+                except KeyError as error:
+                    logging.error(f"Your task configuration is missing a {error}")
+                    return sys.exit()
+                try:
+                    task_config = TaskConfig(
+                        name=item["name"],
+                        steps=item["steps"],
+                        model=item["model"],
+                        description=item.get("description", None),
+                        preamble=item.get("preamble", None),
+                    )
+                    new_steps = ReadSteps(step_list=item["steps"]).returnSteps()
+                    task_config.steps = new_steps
+                    tasks.append(task_config)
+                except KeyError as error:
+                    if name != "":
+                        logging.error(
+                            f"A product configuration, {name} is missing a key {error}"
+                        )
+                        return sys.exit(1)
+            if task is not None:
+                match = False
+                for item in tasks:
+                    # If task is found, update tasks to a single entry in products
+                    if item.name == task:
+                        match = True
+                        tasks = [item]
+                        break
+                # Exits if product is not found
+                if not match:
+                    logging.error(
+                        f"The task {task} does not exist in {self.yaml_path}"
+                    )
+                    return sys.exit(1)
+            tasks_file = TaskConfigFile(tasks=tasks)
+            return tasks_file
+        except KeyError as error:
+            logging.error(
+                f"Your task configuration file is missing a configuration key {error} in {self.yaml_path}"
+            )
+            return sys.exit(1)
+
+
+# Function to make using common_options simpler
+def return_tasks_config(
+    tasks_file: typing.Optional[str],
+    task: typing.Optional[tuple[str]],
+):
+    loaded_tasks = ReadTaskConfig(yaml_path=tasks_file)
+    final_tasks = []
+    if task == () or task == [""]:
+        task_config = loaded_tasks.returnTasks()
+        final_tasks = task_config.tasks
+    else:
+        for item in task:
+            task_config = loaded_tasks.returnTasks(task=item)
+            final_tasks.append(task_config.tasks[0])
+    task_config = TaskConfigFile(tasks=final_tasks)
+    return task_config
+
+
+# # Function to return a list of tasks from a given task configuration file
+# def return_list_of_tasks(task_config: ReadTaskConfig) -> list[TaskConfig]:
+#     tasks = task_config.returnTasks()
+#     final_tasks = []
+#     for item in tasks:
+#         final_tasks.append(item)
+#     return final_tasks
+
+
+# Function to convert all yaml files inside a path into a single yaml
+def combine_yaml_files(
+    path: typing.Optional[str] = None,
+):
+    final_tasks = []
+    for root, dirs, files in os.walk(resolve_path(path)):
+        for file in files:
+            if file.endswith(".yaml"):
+                full_file_path = os.path.join(resolve_path(path), file)
+                task_config = ReadTaskConfig(yaml_path=full_file_path).returnTasks()
+                if len(task_config.tasks) == 1:
+                    final_tasks.append(task_config.tasks[0])
+                else:
+                    for item in task_config.tasks:
+                        final_tasks.append(item)
+    merged_config = TaskConfigFile(tasks=final_tasks)
+    # for item in merged_config.tasks:
+    #     print(item)
+    return merged_config
diff --git a/examples/gemini/python/docs-agent/pyproject.toml b/examples/gemini/python/docs-agent/pyproject.toml
index 719a54e7f..d69516204 100644
--- a/examples/gemini/python/docs-agent/pyproject.toml
+++ b/examples/gemini/python/docs-agent/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "docs-agent"
-version = "0.3.3"
+version = "0.3.5"
 description = ""
 authors = ["Docs Agent contributors"]
 readme = "README.md"
diff --git a/examples/gemini/python/docs-agent/helpme.sh b/examples/gemini/python/docs-agent/scripts/helpme.sh
similarity index 90%
rename from examples/gemini/python/docs-agent/helpme.sh
rename to examples/gemini/python/docs-agent/scripts/helpme.sh
index 52e287b05..183306c7f 100755
--- a/examples/gemini/python/docs-agent/helpme.sh
+++ b/examples/gemini/python/docs-agent/scripts/helpme.sh
@@ -47,7 +47,7 @@ fi
 
 # Check if the POETRY_ACTIVE environment variable is set
 if [ -z "$POETRY_ACTIVE" ]; then
-  cd "$docs_agent_dir" && poetry run agent helpme "$request" --file "$filename"
+  cd "$docs_agent_dir" && poetry run agent helpme $request --file "$filename"
 else
-  agent helpme "$request" --file "$filename"
+  agent helpme $request --file "$filename"
 fi
diff --git a/examples/gemini/python/docs-agent/tellme.sh b/examples/gemini/python/docs-agent/scripts/tellme.sh
similarity index 100%
rename from examples/gemini/python/docs-agent/tellme.sh
rename to examples/gemini/python/docs-agent/scripts/tellme.sh
diff --git a/examples/gemini/python/docs-agent/tasks/release-notes-task.yaml b/examples/gemini/python/docs-agent/tasks/release-notes-task.yaml
new file mode 100644
index 000000000..27a47b5b8
--- /dev/null
+++ b/examples/gemini/python/docs-agent/tasks/release-notes-task.yaml
@@ -0,0 +1,16 @@
+tasks:
+  - name: "DraftReleaseNotes"
+    model: "models/gemini-1.5-flash-latest"
+    description: "An agent that generates a draft of Docs Agent release notes from git commit messages."
+    steps:
+      - name: "Read git commit messages"
+        description: "Load the git log commit messages from the local Fuchsia checkout since a target date."
+        function: "posix"
+        prompt: "git --no-pager log --since=2024-05-26"
+      - name: "Create an initial release notes draft"
+        function: "helpme"
+        prompt: "Based on the commit messages above, create an intial release notes draft that introduces new features and brings attention to important messages."
+      - name: "Revise the release notes draft"
+        function: "helpme"
+        prompt: "Revise the release notes draft above to be more concise and better structured, and if appropriate, include practical examples."
+

From 9d8f9fcde8ebfe671714f1b5703561761258dbf2 Mon Sep 17 00:00:00 2001
From: Kyo Lee <kyol@google.com>
Date: Fri, 14 Jun 2024 20:40:06 +0000
Subject: [PATCH 10/12] [cli] Update the pyproject.toml file missed in the
 previos commit for 0.3.5 release.

---
 examples/gemini/python/docs-agent/pyproject.toml | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/examples/gemini/python/docs-agent/pyproject.toml b/examples/gemini/python/docs-agent/pyproject.toml
index d69516204..095ec4904 100644
--- a/examples/gemini/python/docs-agent/pyproject.toml
+++ b/examples/gemini/python/docs-agent/pyproject.toml
@@ -48,6 +48,4 @@ build-backend = "poetry.core.masonry.api"
 
 
 [tool.poetry.scripts]
-agent = "docs_agent.interfaces.cli:cli"
-agent_admin = "docs_agent.interfaces.cli:cli_admin"
-agent_client = "docs_agent.interfaces.cli:cli_client"
+agent = "docs_agent.interfaces.cli.cli:cli"

From 65ceb6ab6a6bf1141e5eaefb6a84ccccc563de31 Mon Sep 17 00:00:00 2001
From: Kyo Lee <kyol@google.com>
Date: Fri, 14 Jun 2024 20:52:39 +0000
Subject: [PATCH 11/12] [Docs Agent] Remove unused CSS files from the previous
 0.3.5 version commit.

---
 .../chatbot/static/css/style-chatui-1.5.css   | 625 ------------------
 .../chatbot/static/css/style-widget.css       | 624 -----------------
 2 files changed, 1249 deletions(-)
 delete mode 100644 examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui-1.5.css
 delete mode 100644 examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-widget.css

diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui-1.5.css b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui-1.5.css
deleted file mode 100644
index 4cdb442e3..000000000
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-chatui-1.5.css
+++ /dev/null
@@ -1,625 +0,0 @@
-/**
- * Copyright 2023 Google LLC
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/* ======= General style for HTML elements ======= */
-
-body {
-    font: 16px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
-    color: #333;
-    font-weight: 300;
-    max-width: 960px;
-    margin: auto;
-    background-color: #d9d9d9;
-    padding-top: 15px;
-    padding-bottom: 15px;
-  }
-
-  a {
-    color: #0a619a;
-  }
-
-  p {
-    margin: 0 0 1em;
-    line-height: 130%;
-  }
-
-  h1 {
-    margin: 0 0 0.5em;
-    font-weight: 500;
-    font-size: 1.3em;
-    margin-left: 1.0em;
-    margin-top: 0.3em;
-  }
-
-  h2 {
-    margin: 0;
-    margin-top: 17px;
-    margin-bottom: 15px;
-    font-weight: normal;
-    font-size: 1.5em;
-  }
-
-  h3 {
-    margin: 0;
-    margin-top: 10px;
-    margin-bottom: 10px;
-  }
-
-  h4 {
-    color: #505050;
-    margin-top: 3px;
-    margin-left: 5px;
-    margin-bottom: 8px;
-  }
-
-  li {
-    margin: 0 0 0.3em;
-  }
-
-  code {
-    font-family: math;
-    color: darkgreen;
-    text-wrap: pretty;
-  }
-
-  /* ======= Style layout by ID ======= */
-
-  #callout-box {
-    margin: auto;
-    max-width: 800px;
-    font: 13px arial, sans-serif;
-    background-color: white;
-    border-style: solid;
-    border-width: 1px;
-    padding: 10px 25px;
-    box-shadow: 5px 5px 5px grey;
-    border-radius: 15px;
-  }
-
-  #important-box {
-    font-size: 0.9em;
-    font-family: system-ui;
-    line-height: 150%;
-    word-break: break-word;
-    #padding: 4px;
-    padding-top: 5px;
-    padding-bottom: 5px;
-    padding-left: 10px;
-    padding-right: 10px;
-    background-color: #fcb8a1;
-    border-radius: 5px;
-    border-width: 2px;
-    border-style: solid;
-  }
-
-  #tldr-response-box {
-    font-size: 0.8em;
-    font-family: sans-serif;
-    line-height: 140%;
-    margin-top: 10px;
-    margin-bottom: 25px;
-    border-width: 2px;
-    border-style: solid;
-    padding-top: 5px;
-    padding-bottom: 5px;
-    padding-left: 10px;
-    padding-right: 10px;
-    background-color: #b1d8f1;
-    border-radius: 5px;
-    border-width: 2px;
-    border-style: solid;
-  }
-
-  #response-box {
-    font-size: 1.0em;
-    font-family: sans-serif;
-    line-height: 140%;
-    margin-top: 10px;
-  }
-  #suggested-questions {
-    font-family: sans-serif;
-    word-break: break-word;
-  }
-
-  #context-content{
-    background: #d7dbd7;
-    font-family: sans-serif;
-    word-break: break-all;
-  }
-
-  #context-pre {
-    font-size: small;
-    font-family: monospace;
-    text-wrap: pretty;
-    margin-top: 0.3px;
-  }
-
-  #probability-box {
-    font-size: small;
-    padding: 4px;
-    margin-bottom: 10px;
-  }
-
-  #grounding-box {
-    font-size: small;
-    padding: 4px;
-    word-break: break-all;
-  }
-
-  #grounding-pre {
-    font-size: small;
-    font-family: monospace;
-    text-wrap: pretty;
-  }
-
-  #reference-box {
-    font-size: 0.9em;
-    font-family: system-ui;
-    text-wrap: pretty;
-    word-break: break-all;
-    margin-bottom: 12px;
-    line-height: 1.5em;
-  }
-
-  #reference-box-no-aqa {
-    font-size: 0.9em;
-    font-family: system-ui;
-    text-wrap: pretty;
-    word-break: break-all;
-    line-height: 1.5em;
-  }
-
-  #aqa-content{
-    background: #9fc7db;
-    font-family: math;
-  }
-
-  #aqa-label{
-    background: #49a5d2;
-  }
-
-  #aqa-json {
-    font-family: system-ui;
-    font-size: small;
-    text-wrap: pretty;
-    word-break: break-all;
-    margin: 0;
-  }
-
-  #rewrite-buttons-box {
-    margin-top: 12px;
-  }
-
-  #feedback-buttons-box {
-    margin-top: 12px;
-  }
-
-  #answerable-span {
-    font-size: small;
-    font-family: system-ui;
-    float: right;
-    padding: 10px;
-  }
-
-  /* ======= Style by class ======= */
-
-  .hidden {
-    display: none;
-  }
-
-  .disable {
-    display: none;
-  }
-
-  .header-wrapper {
-    display: flex;
-  }
-
-  .loading {
-    font: 15px arial, sans-serif;
-    width: 100%;
-    margin-left: 12px;
-    color: #505050;
-    padding: 2px;
-  }
-
-  .notselected {
-    background-color: #303936e6;
-    padding-top: 3px;
-    padding-bottom: 5px;
-  }
-
-  .notselected:hover {
-    background-color: #121a17e6;
-    cursor:pointer;
-  }
-
-  #like-button.selected {
-    background-color: #1e6a9c;
-    padding-top: 7px;
-    padding-bottom: 7px;
-  }
-
-  #dislike-button.selected {
-    background-color: #CF5C3F;
-    padding-top: 7px;
-    padding-bottom: 7px;
-  }
-
-  .selected:hover {
-    background-color: #0a619a;
-    cursor:pointer;
-  }
-
-  .rewrite {
-    padding: 15px;
-    border: 2px solid #000;
-    margin-top: 6px;
-    border-radius: 15px;
-  }
-
-  .feedback {
-    padding: 15px;
-    border: 2px solid #000;
-    margin-top: 6px;
-    border-radius: 15px;
-  }
-
-  .question, .response, .response-text, .fact-checked-text {
-    max-width: 700px;
-    margin-left: 3px;
-  }
-
-  .full-response {
-    max-width: 700px;
-    margin-left: 10px;
-  }
-
-  .related-questions {
-    margin-bottom: 20px;
-    font-size: 0.9em;
-    line-height: 140%;
-  }
-
-  /* ======= Style buttons by ID ======= */
-
-  #rewrite-button {
-    border: 0;
-    background-color: #cf633ff2;
-    color: #fff;
-    padding: 7px;
-    border-radius: 5px;
-    cursor:pointer;
-    margin-top: 0.3em;
-  }
-
-  #rewrite-button:hover {
-    background: #ce3705f2;
-    cursor:pointer;
-  }
-
-  #feedback-button {
-    border: 0;
-    background-color: #cf633ff2;
-    color: #fff;
-    padding: 7px;
-    border-radius: 5px;
-    cursor:pointer;
-    margin-top: 0.3em;
-  }
-
-  #feedback-button:hover {
-    background: #ce3705f2;
-    cursor:pointer;
-  }
-
-  #like-button {
-    border: 0;
-    color: #fff;
-    padding-left: 7px;
-    padding-right: 7px;
-    border-radius: 5px;
-    cursor:pointer;
-  }
-
-  #dislike-button {
-    border: 0;
-    color: #fff;
-    padding-left: 7px;
-    padding-right: 7px;
-    border-radius: 5px;
-    cursor:pointer;
-  }
-
-  #submit-button {
-    border: 0;
-    background: none;
-    background-color: #CF5C3F;
-    color: #fff;
-    padding: 7px;
-    border-radius: 5px;
-    cursor:pointer;
-  }
-
-  #submit-button:hover {
-    background: #ce3705f2;
-    cursor:pointer;
-  }
-
-  #submit-result {
-    color: #027f02d6;
-    font-family: fantasy;
-  }
-  #feedback-submit-button {
-    border: 0;
-    background: none;
-    background-color: #CF5C3F;
-    color: #fff;
-    padding: 7px;
-    border-radius: 5px;
-    cursor:pointer;
-  }
-
-  #feedback-submit-button:hover {
-    background: #ce3705f2;
-    cursor:pointer;
-  }
-
-  #feedback-submit-result {
-    color: #027f02d6;
-    font-family: fantasy;
-  }
-
-  #edit-text-area {
-    font: 13px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
-    max-height: 500px;
-    max-width: -webkit-fill-available;
-    height: 300px;
-    width: 650px;
-    padding: 8px;
-  }
-
-  #feedback-text-area {
-    font: 13px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
-    max-height: 500px;
-    max-width: -webkit-fill-available;
-    height: 300px;
-    width: 580px;
-    padding: 8px;
-  }
-
-  #rewrite-question-header {
-    margin: 0;
-    margin-bottom: 5px;
-  }
-
-  #rewrite-response-header {
-    margin: 0;
-    margin-top: 10px;
-    margin-bottom: 5px;
-  }
-
-  #user-id {
-    margin: 0;
-    margin-top: 10px;
-    margin-bottom: 15px;
-  }
-
-  #fact-check-url {
-    margin: 0 0 0.7em;
-  }
-
-  #source-para {
-    margin: 0 0 0.7em;
-  }
-
-  #distance-para {
-    margin: 0 0 0.7em;
-    font: 11px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
-  }
-  /* ======= Search Box ======= */
-
-  .search {
-    border: 2px solid #CF5C3F;
-    overflow: auto;
-    max-width: 700px;
-    margin-top: 15px;
-    margin-left: 10px;
-    margin-bottom: 10px;
-    border-radius: 5px;
-  }
-
-  .search input[type="text"] {
-    border: 0;
-    width: calc(100% - 65px);
-    padding: 10px;
-  }
-
-  .search input[type="text"]:focus {
-    outline: 0;
-  }
-
-  .search input[type="submit"] {
-    border: 0;
-    background: none;
-    background-color: #CF5C3F;
-    color: #fff;
-    float: right;
-    padding: 10px;
-    -moz-border-radius-top-right: 5px;
-    -webkit-border-radius-top-right: 5px;
-    -moz-border-radius-bottom-right: 5px;
-    -webkit-border-radius-bottom-right: 5px;
-    cursor:pointer;
-  }
-
-  /* ======= Accordion ======= */
-
-  .accordion {
-    max-width: 65em;
-    #margin-bottom: 1em;
-  }
-
-  .accordion > input[type="checkbox"] {
-    position: absolute;
-    left: -100vw;
-  }
-
-  .accordion .content {
-    overflow-y: hidden;
-    height: 0;
-    transition: height 0.3s ease;
-  }
-
-  .accordion .reference-content {
-    font-size: 15px;
-    font-family: serif;
-  }
-
-  .accordion > input[type="checkbox"]:checked ~ .content {
-    height: auto;
-    overflow: visible;
-    padding: 15px;
-    border: 2px solid #000;
-    margin-top: 6px;
-    border-radius: 15px;
-  }
-
-  .accordion .handle {
-    margin: 0;
-    font-size: 1.0em;
-  }
-
-  .accordion label {
-    display: block;
-    font-weight: normal;
-    border: 2px solid #000;
-    #padding: 12px;
-    background: #4490b8ab;
-    #border-radius: 15px;
-    padding: 5px;
-    #background: #027f023b;
-    border-radius: 10px;
-  }
-
-  .accordion label:hover,
-  .accordion label:focus {
-    background: #d9d9d9;
-    cursor:pointer;
-  }
-
-  .accordion .handle label::before {
-    font-family: fontawesome, sans-serif;
-    display: inline-block;
-    content: "\2964";
-    margin-right: 10px;
-    font-size: .58em;
-    line-height: 1.556em;
-    vertical-align: middle;
-  }
-
-  .accordion > input[type="checkbox"]:checked ~ .handle label::before {
-    content: "\2965";
-  }
-
-  .accordion p:last-child {
-    margin-bottom: 0;
-  }
-
-  /* ======= Accordion Source ======= */
-
-  .accordion-source {
-    max-width: 65em;
-    margin-bottom: 1em;
-  }
-
-  .accordion-source > input[type="checkbox"] {
-    position: absolute;
-    left: -100vw;
-  }
-
-  .accordion-source .content {
-    overflow-y: hidden;
-    height: 0;
-    transition: height 0.3s ease;
-  }
-
-  .accordion-source .content{
-    font-size: 13px;
-  }
-
-  .accordion-source > input[type="checkbox"]:checked ~ .content {
-    height: auto;
-    overflow: visible;
-    padding: 15px;
-    border: 2px solid #000;
-    margin-top: 6px;
-    border-radius: 15px;
-  }
-
-  .accordion-source .handle {
-    margin: 0;
-    font-size: 1em;
-    line-height: 1.2em;
-  }
-
-  .accordion-source label {
-    display: block;
-    font-weight: normal;
-    border: 1px solid #000;
-    padding: 6px;
-    background: #4490b8ab;
-    border-radius: 15px;
-  }
-
-  .accordion-source label:hover,
-  .accordion-source label:focus {
-    background: #d9d9d9;
-    cursor:pointer;
-  }
-
-  .accordion-source .handle label::before {
-    font-family: fontawesome, sans-serif;
-    display: inline-block;
-    content: "\2964";
-    margin-right: 10px;
-    font-size: .58em;
-    line-height: .556em;
-    vertical-align: middle;
-  }
-
-  .accordion-source > input[type="checkbox"]:checked ~ .handle label::before {
-    content: "\2965";
-  }
-
-  .accordion-source p:last-child {
-    margin-bottom: 0;
-  }
-
-/* Loader animation */
-/* Source: https://css-loaders.com/classic/ */
-.loader {
-  width: fit-content;
-  font-family: monospace;
-  font-size: 14px;
-  margin-left: 13px;
-  clip-path: inset(0 3ch 0 0);
-  animation: animation 1s steps(4) infinite;
-}
-.loader:before {
-  content:"Generating a response..."
-}
-@keyframes animation {to{clip-path: inset(0 -1ch 0 0)}}
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-widget.css b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-widget.css
deleted file mode 100644
index 9658ef6a7..000000000
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/static/css/style-widget.css
+++ /dev/null
@@ -1,624 +0,0 @@
-/**
- * Copyright 2023 Google LLC
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/* ======= General style for HTML elements ======= */
-
-body {
-    font: 16px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
-    color: #333;
-    font-weight: 300;
-    max-width: 960px;
-    margin: auto;
-    background-color: white;
-    padding-top: 15px;
-    padding-bottom: 15px;
-  }
-
-  a {
-    color: #0a619a;
-  }
-
-  p {
-    margin: 0 0 1em;
-    line-height: 130%;
-  }
-
-  h1 {
-    margin: 0 0 0.5em;
-    font-weight: 500;
-    font-size: 1.3em;
-    margin-top: 0.1em;
-    margin-left: 1.0em;
-    margin-bottom: 0.9em;
-  }
-
-  h2 {
-    margin: 0;
-    margin-top: 15px;
-    margin-bottom: 10px;
-    font-weight: normal;
-    font-size: 1.4em;
-  }
-
-  h3 {
-    margin: 0;
-    margin-top: 10px;
-    margin-bottom: 10px;
-  }
-
-  h4 {
-    color: #505050;
-    margin-top: 3px;
-    margin-left: 5px;
-    margin-bottom: 8px;
-  }
-
-  li {
-    margin: 0 0 0.3em;
-  }
-
-  code {
-    font-family: math;
-    color: darkgreen;
-    text-wrap: pretty;
-  }
-
-  /* ======= Style layout by ID ======= */
-
-  #iframe-box {
-    margin: 0px;
-    max-width: 760px;
-    font: 15px arial, sans-serif;
-    background-color: white;
-    padding-bottom: 0px;
-    padding-left: 0px;
-  }
-
-  #callout-box {
-    margin: auto;
-    max-width: 800px;
-    font: 13px arial, sans-serif;
-    background-color: white;
-    border-style: solid;
-    border-width: 1px;
-    padding: 10px 25px;
-    box-shadow: 5px 5px 5px grey;
-    border-radius: 15px;
-  }
-
-  #important-box {
-    font-size: 0.9em;
-    font-family: system-ui;
-    word-break: break-word;
-    line-height: 150%;
-    word-break: break-word;
-    padding: 4px;
-  }
-
-  #response-box {
-    font-size: 1.0em;
-    font-family: sans-serif;
-    line-height: 140%;
-    margin-top: 10px;
-  }
-
-  #suggested-questions {
-    font-family: sans-serif;
-    word-break: break-word;
-  }
-
-  #source-pages {
-    font-family: sans-serif;
-    word-break: break-all;
-  }
-
-  #context-content{
-    background: #d7dbd7;
-    font-family: sans-serif;
-    word-break: break-all;
-  }
-
-  #context-pre {
-    font-size: small;
-    font-family: monospace;
-    text-wrap: pretty;
-    margin-top: 0.3px;
-  }
-
-  #probability-box {
-    font-size: small;
-    padding: 4px;
-    margin-bottom: 10px;
-  }
-
-  #grounding-box {
-    font-size: small;
-    padding: 4px;
-    word-break: break-all;
-  }
-
-  #grounding-pre {
-    font-size: small;
-    font-family: monospace;
-    text-wrap: pretty;
-  }
-
-  #reference-box {
-    font-size: 0.9em;
-    font-family: system-ui;
-    text-wrap: pretty;
-    word-break: break-all;
-    margin-bottom: 12px;
-    line-height: 1.5em;
-  }
-
-  #reference-box-no-aqa {
-    font-size: 0.9em;
-    font-family: system-ui;
-    text-wrap: pretty;
-    word-break: break-all;
-    line-height: 1.5em;
-  }
-
-  #aqa-content{
-    background: #9fc7db;
-    font-family: math;
-  }
-
-  #aqa-label{
-    background: #49a5d2;
-  }
-
-  #aqa-json {
-    font-family: system-ui;
-    font-size: small;
-    text-wrap: pretty;
-    word-break: break-all;
-    margin: 0;
-  }
-
-  #rewrite-buttons-box {
-    margin-top: 12px;
-  }
-
-  #feedback-buttons-box {
-    margin-top: 12px;
-  }
-
-  #answerable-span {
-    font-size: small;
-    font-family: system-ui;
-    float: right;
-    padding: 10px;
-  }
-
-  /* ======= Style by class ======= */
-
-  .hidden {
-    display: none;
-  }
-
-  .disable {
-    display: none;
-  }
-
-  .header-wrapper {
-    display: flex;
-  }
-
-  .loading {
-    font: 15px arial, sans-serif;
-    width: 100%;
-    margin-left: 12px;
-    color: #505050;
-    padding: 2px;
-  }
-
-  .notselected {
-    background-color: #303936e6;
-    padding-top: 3px;
-    padding-bottom: 5px;
-  }
-
-  .notselected:hover {
-    background-color: #121a17e6;
-    cursor:pointer;
-  }
-
-  #like-button.selected {
-    background-color: #1e6a9c;
-    padding-top: 7px;
-    padding-bottom: 7px;
-  }
-
-  #dislike-button.selected {
-    background-color: #CF5C3F;
-    padding-top: 7px;
-    padding-bottom: 7px;
-  }
-
-  .selected:hover {
-    background-color: #0a619a;
-    cursor:pointer;
-  }
-
-  .rewrite {
-    padding: 15px;
-    border: 2px solid #000;
-    margin-top: 6px;
-    border-radius: 15px;
-  }
-
-  .feedback {
-    padding: 15px;
-    border: 2px solid #000;
-    margin-top: 6px;
-    border-radius: 15px;
-  }
-
-  .question, .response, .response-text, .fact-checked-text {
-    max-width: 700px;
-    margin-left: 3px;
-  }
-
-  .full-response {
-    max-width: 700px;
-    margin-left: 10px;
-  }
-
-  .related-questions {
-    margin-bottom: 20px;
-    font-size: 0.9em;
-    line-height: 140%;
-  }
-
-  .relevant-sources {
-    margin-bottom: 20px;
-    font-size: 0.9em;
-    line-height: 140%;
-  }
-
-  /* ======= Style buttons by ID ======= */
-
-  #rewrite-button {
-    border: 0;
-    background-color: #cf633ff2;
-    color: #fff;
-    padding: 7px;
-    border-radius: 5px;
-    cursor:pointer;
-    margin-top: 0.3em;
-  }
-
-  #rewrite-button:hover {
-    background: #ce3705f2;
-    cursor:pointer;
-  }
-
-  #feedback-button {
-    border: 0;
-    background-color: #cf633ff2;
-    color: #fff;
-    padding: 7px;
-    border-radius: 5px;
-    cursor:pointer;
-    margin-top: 0.3em;
-  }
-
-  #feedback-button:hover {
-    background: #ce3705f2;
-    cursor:pointer;
-  }
-
-  #like-button {
-    border: 0;
-    color: #fff;
-    padding-left: 7px;
-    padding-right: 7px;
-    border-radius: 5px;
-    cursor:pointer;
-  }
-
-  #dislike-button {
-    border: 0;
-    color: #fff;
-    padding-left: 7px;
-    padding-right: 7px;
-    border-radius: 5px;
-    cursor:pointer;
-  }
-
-  #submit-button {
-    border: 0;
-    background: none;
-    background-color: #CF5C3F;
-    color: #fff;
-    padding: 7px;
-    border-radius: 5px;
-    cursor:pointer;
-  }
-
-  #submit-button:hover {
-    background: #ce3705f2;
-    cursor:pointer;
-  }
-
-  #submit-result {
-    color: #027f02d6;
-    font-family: fantasy;
-  }
-
-  #feedback-submit-button {
-    border: 0;
-    background: none;
-    background-color: #CF5C3F;
-    color: #fff;
-    padding: 7px;
-    border-radius: 5px;
-    cursor:pointer;
-  }
-
-  #feedback-submit-button:hover {
-    background: #ce3705f2;
-    cursor:pointer;
-  }
-
-  #feedback-submit-result {
-    color: #027f02d6;
-    font-family: fantasy;
-  }
-
-  #edit-text-area {
-    font: 13px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
-    max-height: 500px;
-    max-width: -webkit-fill-available;
-    height: 300px;
-    width: 580px;
-    padding: 8px;
-  }
-
-  #feedback-text-area {
-    font: 13px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
-    max-height: 500px;
-    max-width: -webkit-fill-available;
-    height: 300px;
-    width: 580px;
-    padding: 8px;
-  }
-
-  #rewrite-question-header {
-    margin: 0;
-    margin-bottom: 5px;
-  }
-
-  #rewrite-response-header {
-    margin: 0;
-    margin-top: 10px;
-    margin-bottom: 5px;
-  }
-
-  #user-id {
-    margin: 0;
-    margin-top: 10px;
-    margin-bottom: 15px;
-  }
-
-  #fact-check-url {
-    margin: 0 0 0.7em;
-  }
-
-  #source-para {
-    margin: 0 0 0.7em;
-  }
-
-  #distance-para {
-    margin: 0 0 0.7em;
-    font: 11px/1.5em Overpass, "Open Sans", Helvetica, sans-serif;
-  }
-  /* ======= Search Box ======= */
-
-  .search {
-    border: 2px solid #CF5C3F;
-    overflow: auto;
-    max-width: 600px;
-    margin-top: 15px;
-    margin-left: 10px;
-    margin-bottom: 10px;
-    border-radius: 5px;
-  }
-
-  .search input[type="text"] {
-    border: 0;
-    width: calc(100% - 65px);
-    padding: 10px;
-  }
-
-  .search input[type="text"]:focus {
-    outline: 0;
-  }
-
-  .search input[type="submit"] {
-    border: 0;
-    background: none;
-    background-color: #CF5C3F;
-    color: #fff;
-    float: right;
-    padding: 10px;
-    -moz-border-radius-top-right: 5px;
-    -webkit-border-radius-top-right: 5px;
-    -moz-border-radius-bottom-right: 5px;
-    -webkit-border-radius-bottom-right: 5px;
-    cursor:pointer;
-  }
-
-  /* ======= Accordion ======= */
-
-  .accordion {
-    max-width: 65em;
-    #margin-bottom: 1em;
-  }
-
-  .accordion > input[type="checkbox"] {
-    position: absolute;
-    left: -100vw;
-  }
-
-  .accordion .content {
-    overflow-y: hidden;
-    height: 0;
-    transition: height 0.3s ease;
-  }
-
-  .accordion .reference-content {
-    font-size: 15px;
-    font-family: serif;
-  }
-
-  .accordion > input[type="checkbox"]:checked ~ .content {
-    height: auto;
-    overflow: visible;
-    padding: 15px;
-    border: 2px solid #000;
-    margin-top: 6px;
-    border-radius: 15px;
-  }
-
-  .accordion .handle {
-    margin: 0;
-    font-size: 1.0em;
-  }
-
-  .accordion label {
-    display: block;
-    font-weight: normal;
-    border: 2px solid #000;
-    #padding: 12px;
-    background: #4490b8ab;
-    #border-radius: 15px;
-    padding: 5px;
-    #background: #027f023b;
-    border-radius: 10px;
-  }
-
-  .accordion label:hover,
-  .accordion label:focus {
-    background: #d9d9d9;
-    cursor:pointer;
-  }
-
-  .accordion .handle label::before {
-    font-family: fontawesome, sans-serif;
-    display: inline-block;
-    content: "\2964";
-    margin-right: 10px;
-    font-size: .58em;
-    line-height: 1.556em;
-    vertical-align: middle;
-  }
-
-  .accordion > input[type="checkbox"]:checked ~ .handle label::before {
-    content: "\2965";
-  }
-
-  .accordion p:last-child {
-    margin-bottom: 0;
-  }
-
-  /* ======= Accordion Source ======= */
-
-  .accordion-source {
-    max-width: 65em;
-    margin-bottom: 1em;
-  }
-
-  .accordion-source > input[type="checkbox"] {
-    position: absolute;
-    left: -100vw;
-  }
-
-  .accordion-source .content {
-    overflow-y: hidden;
-    height: 0;
-    transition: height 0.3s ease;
-  }
-
-  .accordion-source .content{
-    font-size: 13px;
-  }
-
-  .accordion-source > input[type="checkbox"]:checked ~ .content {
-    height: auto;
-    overflow: visible;
-    padding: 15px;
-    border: 2px solid #000;
-    margin-top: 6px;
-    border-radius: 15px;
-  }
-
-  .accordion-source .handle {
-    margin: 0;
-    font-size: 1em;
-    line-height: 1.2em;
-  }
-
-  .accordion-source label {
-    display: block;
-    font-weight: normal;
-    border: 1px solid #000;
-    padding: 6px;
-    background: #4490b8ab;
-    border-radius: 15px;
-  }
-
-  .accordion-source label:hover,
-  .accordion-source label:focus {
-    background: #d9d9d9;
-    cursor:pointer;
-  }
-
-  .accordion-source .handle label::before {
-    font-family: fontawesome, sans-serif;
-    display: inline-block;
-    content: "\2964";
-    margin-right: 10px;
-    font-size: .58em;
-    line-height: .556em;
-    vertical-align: middle;
-  }
-
-  .accordion-source > input[type="checkbox"]:checked ~ .handle label::before {
-    content: "\2965";
-  }
-
-  .accordion-source p:last-child {
-    margin-bottom: 0;
-  }
-
-/* Loader animation */
-/* Source: https://css-loaders.com/classic/ */
-.loader {
-  width: fit-content;
-  font-family: monospace;
-  font-size: 14px;
-  margin-left: 13px;
-  clip-path: inset(0 3ch 0 0);
-  animation: animation 1s steps(4) infinite;
-}
-.loader:before {
-  content:"Generating a response..."
-}
-@keyframes animation {to{clip-path: inset(0 -1ch 0 0)}}
-

From 2b9dd6127b197732659a15aae5e88a1c06ad19de Mon Sep 17 00:00:00 2001
From: Kyo Lee <kyol@google.com>
Date: Fri, 20 Sep 2024 19:33:22 +0000
Subject: [PATCH 12/12] [Docs Agent] Release of Docs Agent v 0.3.6.

What's changed:

- Runtask enhancement: The `runtask` command allows specifying
  a custom input string using the `--custom_input` flag.
- ExtractWorkflows task: This new task extracts workflows and
  commandlines from a source document.
- HelpPolishPrompts task: This task helps users polish prompts
  in a task file.
- IndexPageGenerator task: This task generates an index page
  describing files in a given directory.
- Chat app bug fix: Fixed an issue where + characters were
  removed from the page title.
---
 examples/gemini/python/docs-agent/README.md   |  31 ++-
 examples/gemini/python/docs-agent/config.yaml |   4 +-
 .../python/docs-agent/docs/cli-reference.md   |  23 ++-
 .../docs_agent/agents/docs_agent.py           |  28 +--
 .../docs_agent/interfaces/README.md           | 178 +++++++++++-------
 .../chatbot/templates/chatui-full/result.html |  10 +-
 .../templates/chatui-widget-pro/result.html   |  10 +-
 .../templates/chatui-widget/result.html       |  10 +-
 .../chatbot/templates/chatui/result.html      |  10 +-
 .../docs_agent/interfaces/cli/cli_runtask.py  | 121 ++++++++++--
 .../docs_agent/interfaces/run_console.py      |   5 +-
 .../docs_agent/models/google_genai.py         |  33 +++-
 .../docs-agent/docs_agent/utilities/tasks.py  |  32 ++--
 .../gemini/python/docs-agent/pyproject.toml   |   2 +-
 .../tasks/extract-workflows-task.yaml         |  12 ++
 .../tasks/help-polish-prompts-task.yaml       |  13 ++
 .../tasks/index-page-generator-task.yaml      |  15 ++
 .../docs-agent/tasks/release-notes-task.yaml  |   2 +-
 18 files changed, 398 insertions(+), 141 deletions(-)
 create mode 100644 examples/gemini/python/docs-agent/tasks/extract-workflows-task.yaml
 create mode 100644 examples/gemini/python/docs-agent/tasks/help-polish-prompts-task.yaml
 create mode 100644 examples/gemini/python/docs-agent/tasks/index-page-generator-task.yaml

diff --git a/examples/gemini/python/docs-agent/README.md b/examples/gemini/python/docs-agent/README.md
index d3d368eb9..c788c4f0e 100644
--- a/examples/gemini/python/docs-agent/README.md
+++ b/examples/gemini/python/docs-agent/README.md
@@ -7,9 +7,9 @@ Docs Agent provides a set of easy-to-use self-service tools designed to give you
 your team access to Google's [Gemini API][genai-doc-site] for learning, experimentation,
 and project deployment.
 
-## Overview
+## Docs Agent web app
 
-Docs Agent apps use a technique known as Retrieval Augmented Generation (RAG), which
+Docs Agent uses a technique known as **Retrieval Augmented Generation (RAG)**, which
 allows you to bring your own documents as knowledge sources to AI language models.
 This approach helps the AI language models to generate relevant and accurate responses
 that are grounded in the information that you provide and control.
@@ -18,13 +18,24 @@ that are grounded in the information that you provide and control.
 
 **Figure 1**. Docs Agent uses a vector database to retrieve context for augmenting prompts.
 
-Docs Agent apps are designed to be easily set up and configured in a Linux environment.
-If you want to set up and launch the Docs Agent chat app on your host machine, check out
-the [Set up Docs Agent][set-up-docs-agent] section below.
+The Docs Agent chatbot web app is designed to be easily set up and configured in a Linux
+environment. If you want to set up and launch the Docs Agent chat app on your host machine,
+check out the [Set up Docs Agent][set-up-docs-agent] section below.
 
-### Summary of features
+## Docs Agent tasks
 
-The following list summarizes the tasks and features supported by Docs Agent:
+Docs Agent's `agent runtask` command allows you to run pre-defined chains of prompts,
+which are referred to as **tasks**. These tasks simplify complex interactions by defining
+a series of steps that the Docs Agent will execute. The tasks are defined in `.yaml` files
+stored in the [`tasks`][tasks-dir] directory of your Docs Agent project. The tasks are
+designed to be reusable and can be used to automate common workflows, such as generating
+release notes, updating documentation, or analyzing complex information.
+
+To set up and run the `agent runtask` command, see [Set up Docs Agent CLI][cli-readme].
+
+## Summary of features
+
+The list below summarizes the tasks and features supported by Docs Agent:
 
 - **Process Markdown**: Split Markdown files into small plain text chunks. (See
   [Docs Agent chunking process][chunking-process].)
@@ -60,9 +71,9 @@ The following list summarizes the tasks and features supported by Docs Agent:
   [Set up the Docs Agent CLI][cli-readme] to make requests to the Gemini models
   from anywhere in a terminal.
 - **Support the Gemini 1.5 models**: Docs Agent works with the Gemini 1.5 models,
-  `gemini-1.5-pro-latest` and `text-embedding-004`. The new ["1.5"][new-15-mode] web app
-  mode uses all three Gemini models to their strength: AQA (`aqa`), Gemini 1.0 Pro
-  (`gemini-pro`), and Gemini 1.5 Pro (`gemini-1.5-pro-latest`).
+  `gemini-1.5-pro`, `gemini-1.5-flash`, and `text-embedding-004`. The new
+  [`full`][new-15-mode] web app mode uses all three Gemini models to their strength:
+  AQA (`aqa`), Gemini 1.0 Pro (`gemini-pro`), and Gemini 1.5 Pro (`gemini-1.5-pro`).
 - **Complete a task using the Docs Agent CLI**: The `agent runtask` command allows you
   to run pre-defined chains of prompts, which are referred to as tasks. These tasks
   simplify complex interactions by defining a series of steps that the Docs Agent will
diff --git a/examples/gemini/python/docs-agent/config.yaml b/examples/gemini/python/docs-agent/config.yaml
index 8d7fdc9df..d104ab5f4 100644
--- a/examples/gemini/python/docs-agent/config.yaml
+++ b/examples/gemini/python/docs-agent/config.yaml
@@ -17,7 +17,7 @@
 configs:
   - product_name: "Fuchsia"
     models:
-      - language_model: "models/aqa"
+      - language_model: "models/gemini-1.5-flash-latest"
         embedding_model: "models/embedding-001"
         api_endpoint: "generativelanguage.googleapis.com"
         embedding_api_call_limit: 1400
@@ -25,7 +25,7 @@ configs:
     docs_agent_config: "normal"
     markdown_splitter: "token_splitter"
     log_level: "NORMAL"
-    db_type: "google_semantic_retriever"
+    db_type: "chroma"
     db_configs:
       - db_type: "chroma"
         vector_db_dir: "vector_stores/chroma"
diff --git a/examples/gemini/python/docs-agent/docs/cli-reference.md b/examples/gemini/python/docs-agent/docs/cli-reference.md
index 5967b65b9..b6b945740 100644
--- a/examples/gemini/python/docs-agent/docs/cli-reference.md
+++ b/examples/gemini/python/docs-agent/docs/cli-reference.md
@@ -273,8 +273,29 @@ For example:
 agent runtask --task DraftReleaseNotes
 ```
 
+### View the list of available Docs Agent tasks
+
 To see the list of all tasks available in your project, run
-`agent runtask` without any arguments.
+`agent runtask` without any arguments:
+
+```sh
+agent runtask
+```
+
+### Ask the model to run a task using custom input
+
+If a task script has a `<INPUT>` placeholder, you can provide
+a custom input string to the task:
+
+```sh
+agent runtask --task <TASK> --custom_input <INPUT_STRING>
+```
+
+For example:
+
+```sh
+agent runtask --task IndexPageGenerator --custom_input ~/my_example/docs/development/
+```
 
 ## Managing online corpora
 
diff --git a/examples/gemini/python/docs-agent/docs_agent/agents/docs_agent.py b/examples/gemini/python/docs-agent/docs_agent/agents/docs_agent.py
index 992a46c6f..5eb83c459 100644
--- a/examples/gemini/python/docs-agent/docs_agent/agents/docs_agent.py
+++ b/examples/gemini/python/docs-agent/docs_agent/agents/docs_agent.py
@@ -359,7 +359,11 @@ def add_custom_instruction_to_context(self, condition, context):
 
     # Return true if the aqa model used in this Docs Agent setup
     def check_if_aqa_is_used(self):
-        if self.config.models.language_model == "models/aqa":
+        if (
+            self.config.models.language_model == "models/aqa"
+            or self.config.app_mode == "full"
+            or self.config.app_mode == "widget-pro"
+        ):
             return True
         else:
             return False
@@ -525,16 +529,21 @@ def ask_content_model_with_context_prompt(
         try:
             response = ""
             if model == "gemini-pro":
-                response = self.gemini_pro.generate_content(contents=new_prompt)
+                response = self.gemini_pro.generate_content(
+                    contents=new_prompt, log_level=self.config.log_level
+                )
             elif model == "gemini-1.5":
-                response = self.gemini_15.generate_content(contents=new_prompt)
+                response = self.gemini_15.generate_content(
+                    contents=new_prompt, log_level=self.config.log_level
+                )
             else:
-                response = self.gemini.generate_content(contents=new_prompt)
-        except:
+                response = self.gemini.generate_content(
+                    contents=new_prompt, log_level=self.config.log_level
+                )
+        except Exception as e:
+            print("Error in generate_content()")
+            print(e)
             return self.config.conditions.model_error_message, new_prompt
-        # for chunk in response:
-        #     if str(chunk.candidates[0].content) == "":
-        #         return self.config.conditions.model_error_message, new_prompt
         return response, new_prompt
 
     # Use this method for talking to a Gemini content model
@@ -549,9 +558,6 @@ def ask_content_model_to_use_file(self, prompt: str, file: str):
             response = self.gemini.generate_content(contents=new_prompt)
         except google.api_core.exceptions.InvalidArgument:
             return self.config.conditions.model_error_message
-        # for chunk in response:
-        #     if str(chunk.candidates[0].content) == "":
-        #         return self.config.conditions.model_error_message
         return response
 
     # Use this method for asking a Gemini content model for fact-checking.
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/README.md b/examples/gemini/python/docs-agent/docs_agent/interfaces/README.md
index 7a3d83b13..56a3292ea 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/README.md
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/README.md
@@ -1,35 +1,22 @@
 # Set up Docs Agent CLI
 
 This guide provides instructions on setting up Docs Agent's command-line
-interface (CLI) that allows you to ask questions from anywhere in a terminal.
+interface (CLI) on your host machine for running Docs Agent tasks.
 
-Using Docs Agent, you can configure your host machine's environment to make
-the `gemini` command run from anywhere in your terminal. The `gemini` command
-(which is an `alias` to Docs Agent's `agent tellme` command) reads a question
-from the arguments, asks the [Gemini AQA][gemini-aqa] model, and prints its
-response in the terminal.
-
-The example below shows that a user can run the `gemini` command directly
-from a terminal:
-
-```
-user@user01:~$ gemini does Flutter support material design 3?
-
-As of the Flutter 3.16 release, Material 3 is enabled by default.
-
-To verify this information, see:
+Docs Agent's `agent runtask` command allows you to run pre-defined chains of
+prompts, which are referred to as **tasks**. These tasks simplify complex
+interactions by defining a series of steps that the Docs Agent will execute.
+The tasks are defined in `.yaml` files stored in the [`tasks`][docs-agent-tasks]
+directory of your Docs Agent project. The tasks are designed to be reusable and
+can be used to automate common workflows, such as generating release notes,
+updating documentation, or analyzing complex information.
 
- • https://docs.flutter.dev/ui/design/material/index#more-information
+To set up the Docs Agent CLI, the steps are:
 
-user@user01:~$
-```
-
-In this setup guide, Docs Agent's AQA model is configured to use an example
-online corpus. However, using the tools available in the Docs Agent project,
-you can [create and populate a new corpus][populate-corpus] with your own
-documents and adjust your Docs Agent configuration to use that corpus
-instead – you can also [share the corpus][share-corpus] with other members
-in your team.
+1. [Prerequisites](#1-prerequisites)
+2. [Update your host machine's environment](#2-update-your-host-machines-environment)
+3. [Clone the Docs Agent project repository](#3-clone-the-docs-agent-project-repository)
+4. [Try the Docs Agent CLI](#4-try-the-docs-agent-cli)
 
 ## 1. Prerequisites
 
@@ -37,15 +24,10 @@ Setting up Docs Agent requires the following prerequisite items:
 
 - A Linux host machine
 
-- A [Google Cloud][google-cloud] project with the setup below:
-
-  - An API key enabled with the Generative Language API (that is,
-    the [Gemini API][genai-doc-site])
+- A [Google Cloud][google-cloud] project with an API key enabled with the
+  Generative Language API (that is, the [Gemini API][genai-doc-site])
 
-  - (**Optional**) [Authenticated OAuth client credentials][oauth-client]
-    stored on the host machine
-
-## 2 Update your host machine's environment
+## 2. Update your host machine's environment
 
 1. Update the Linux package repositories on the host machine:
 
@@ -88,14 +70,80 @@ Setting up Docs Agent requires the following prerequisite items:
    source ~/.bashrc
    ```
 
-## 3. Authorize credentials for Docs Agent
+## 3. Clone the Docs Agent project
+
+**Note**: This guide assumes that you're creating a new project directory
+from your `$HOME` directory.
+
+1. Clone the following repo:
+
+   ```
+   git clone https://github.com/google/generative-ai-docs.git
+   ```
+
+2. Go to the Docs Agent project directory:
+
+   ```
+   cd generative-ai-docs/examples/gemini/python/docs-agent
+   ```
+
+3. Install dependencies using `poetry`:
+
+   ```
+   poetry install
+   ```
+
+## 4. Try the Docs Agent CLI
+
+1. Enter the `poetry shell` environment:
+
+   ```
+   poetry shell
+   ```
+
+   Entering the `poetry shell` environment is **required** for
+   running the `agent` command.
+
+2. Run the `agent helpme` command, for example:
+
+   ```
+   agent helpme how do I cook pasta?
+   ```
+
+   This command returns the Gemini model's response of your input prompt
+   `how do I cook pasta?`.
+
+3. View the list of Docs Agent tasks available in your setup:
+
+   ```
+   agent runtask
+   ```
+
+   This command prints a list of Docs Agent tasks that you can run.
+   (See the `tasks` directory in your local Docs Agent setup.)
+
+4. Run the `agent runtask` command, for example:
+
+   ```
+   agent runtask --task IndexPageGenerator
+   ```
+
+For more details on these commands, see the
+[Interacting with language models][cli-reference-helpme] section in
+the CLI reference page.
+
+## Appendices
+
+### Authorize credentials for Docs Agent
 
 **Note**: This step may not be necessary if you already have OAuth client
 credentials (via `gcloud`) stored on your host machine.
 
-**Note**: This step is **only necessary** if you plan on using the
+This step is **only necessary** if you plan on using the
 `agent tellme` command to interact with your online corpora on Google Cloud.
 
+Do the following:
+
 1. Download the `client_secret.json` file from your
    [Google Cloud project][authorize-credentials].
 
@@ -122,45 +170,45 @@ credentials (via `gcloud`) stored on your host machine.
    (`application_default_credentials.json`) in the `$HOME/.config/gcloud/`
    directory of your host machine.
 
-## 4. Clone the Docs Agent project
+### Set up an alias to the gemini command
 
-**Note**: This guide assumes that you're creating a new project directory
-from your `$HOME` directory.
+This section provides instructions on setting up the Docs Agent CLI to enable
+you to ask questions from anywhere in a terminal.
 
-1. Clone the following repo:
+Using Docs Agent, you can configure your host machine's environment to make
+the `gemini` command run from anywhere in your terminal. The `gemini` command
+(which is an `alias` to Docs Agent's `agent tellme` command) reads a question
+from the arguments, asks the [Gemini AQA][gemini-aqa] model, and prints its
+response in the terminal.
 
-   ```
-   git clone https://github.com/google/generative-ai-docs.git
-   ```
+The example below shows that a user can run the `gemini` command directly
+from a terminal:
 
-2. Go to the Docs Agent project directory:
+```
+user@user01:~$ gemini does Flutter support material design 3?
 
-   ```
-   cd generative-ai-docs/examples/gemini/python/docs-agent
-   ```
+As of the Flutter 3.16 release, Material 3 is enabled by default.
 
-3. Install dependencies using `poetry`:
+To verify this information, see:
 
-   ```
-   poetry install
-   ```
+ • https://docs.flutter.dev/ui/design/material/index#more-information
 
-At this point, you can start using the `agent helpme`, `agent tellme`,
-and `agent runtask` commands to interact with the Gemini models from
-your terminal. For more information on these commands, see the
-[Interacting with language models][cli-reference-helpme] section in
-the CLI reference page.
+user@user01:~$
+```
 
-Proceed to the next section if you want to set up an alias for the
-`agent tellme` command.
+In this setup, Docs Agent's AQA model is configured to use an example
+online corpus. However, using the tools available in the Docs Agent project,
+you can [create and populate a new corpus][populate-corpus] with your own
+documents and adjust your Docs Agent configuration to use that corpus
+instead – you can also [share the corpus][share-corpus] with other members
+in your team.
 
-## 5. Set up an alias to the gemini command
+To update your shell environment so that the `gemini` command can be run
+from anywhere in the terminal, do the following:
 
 **Note**: If your Docs Agent project is not cloned in the `$HOME` directory,
-you need to edit the `scripts/tellme.sh` script in your `docs-agent` project directory.
-
-Update your shell environment so that the `gemini` command can be run
-from anywhere in the terminal:
+you need to edit the `scripts/tellme.sh` script in your `docs-agent` project
+directory.
 
 1. (**Optional**) Open the `scripts/tellme.sh` file using a text editor,
    for example:
@@ -207,8 +255,6 @@ from anywhere in the terminal:
    user@user01:~/temp$ gemini does flutter support material design 3?
    ```
 
-## Appendices
-
 ### Set up your terminal to run the helpme command
 
 **Note**: This is an experimental setup.
@@ -230,6 +276,7 @@ These tasks include, but not limited to:
 - Rewrite `README` file to be instructional and linear.
 - Rewrite `README` file to be more concise and better structured.
 - Format `README` to collect reference links at the bottom.
+- Write a protocol description.
 - Write comments for a C++ source file.
 
 **Note**: Since this setup uses the Gemini Pro model, setting up OAuth on your
@@ -318,3 +365,4 @@ To set up this `helpme` command in your terminal, do the following:
 [authorize-credentials]: https://ai.google.dev/docs/oauth_quickstart#authorize-credentials
 [genai-doc-site]: https://ai.google.dev/docs/gemini_api_overview
 [cli-reference-helpme]: ../../docs/cli-reference.md#interacting-with-language-models
+[docs-agent-tasks]: ../../tasks
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-full/result.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-full/result.html
index 3fca7dea5..7c72dd00b 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-full/result.html
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-full/result.html
@@ -10,7 +10,7 @@
 {% if aqa_response_in_html != "" %}
   <!--Gemini AQA template-->
   <div class="question">
-    <h2>{{ question | replace("+", " ") | replace("%3F", "?")}}</h2>
+    <h2>{{ question }}</h2>
   </div>
   {% if probability|float > 0.7 %}
   <div class="important-box" id="important-box">
@@ -86,7 +86,7 @@ <h2 class="handle">
 {% else %}
   <!--Gemini Pro template-->
   <div class="question">
-    <h2>{{ question | replace("+", " ") | replace("%3F", "?")}}</h2>
+    <h2>{{ question }}</h2>
   </div>
   <div class="important-box" id="important-box">
     <!--Uses the URL from the first result-->
@@ -139,7 +139,7 @@ <h2 class="handle">
 <div class="rewrite hidden" id="rewrite-box">
   <h4 id="rewrite-question-header">Question:</h4>
   <span id="rewrite-question-span">
-    <p>{{ question | replace("+", " ") | replace("%3F", "?")}}</p>
+    <p>{{ question }}</p>
   </span>
   <h4 id="rewrite-response-header">Gemini's response:</h4>
   <span id="rewrite-original-response-span">
@@ -174,7 +174,7 @@ <h4>Rewrite:</h4>
   {{ uuid | safe }}
 </div>
 <div class="hidden" id="feedback-question-span">
-  {{ question | replace("+", " ") | replace("%3F", "?")}}
+  {{ question }}
 </div>
 <div class="hidden" id="feedback-response-span">
   {{ md_to_html(response) | safe }}
@@ -206,7 +206,7 @@ <h4>Feedback:</h4>
   {{ uuid | safe }}
 </div>
 <div class="hidden" id="feedback-question-span">
-  {{ question | replace("+", " ") | replace("%3F", "?")}}
+  {{ question }}
 </div>
 <div class="hidden" id="feedback-response-span">
   {{ md_to_html(response) | safe }}
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget-pro/result.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget-pro/result.html
index e2c7ed471..77ef339de 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget-pro/result.html
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget-pro/result.html
@@ -10,7 +10,7 @@
 {% if aqa_response_in_html != "" %}
   <!--Gemini AQA template-->
   <div class="question">
-    <h2>{{ question | replace("+", " ") | replace("%3F", "?")}}</h2>
+    <h2>{{ question }}</h2>
   </div>
   {% if probability|float > 0.7 %}
   <div class="important-box" id="important-box">
@@ -88,7 +88,7 @@ <h2 class="handle">
 {% else %}
   <!--Gemini Pro template-->
   <div class="question">
-    <h2>{{ question | replace("+", " ") | replace("%3F", "?")}}</h2>
+    <h2>{{ question }}</h2>
   </div>
   <div class="important-box" id="important-box">
     <!--Uses the URL from the first result-->
@@ -143,7 +143,7 @@ <h2 class="handle">
 <div class="rewrite hidden" id="rewrite-box">
   <h4 id="rewrite-question-header">Question:</h4>
   <span id="rewrite-question-span">
-    <p>{{ question | replace("+", " ") | replace("%3F", "?")}}</p>
+    <p>{{ question }}</p>
   </span>
   <h4 id="rewrite-response-header">Gemini's response:</h4>
   <span id="rewrite-original-response-span">
@@ -178,7 +178,7 @@ <h4>Rewrite:</h4>
   {{ uuid | safe }}
 </div>
 <div class="hidden" id="feedback-question-span">
-  {{ question | replace("+", " ") | replace("%3F", "?")}}
+  {{ question }}
 </div>
 <div class="hidden" id="feedback-response-span">
   {{ md_to_html(response) | safe }}
@@ -210,7 +210,7 @@ <h4>Feedback:</h4>
   {{ uuid | safe }}
 </div>
 <div class="hidden" id="feedback-question-span">
-  {{ question | replace("+", " ") | replace("%3F", "?")}}
+  {{ question }}
 </div>
 <div class="hidden" id="feedback-response-span">
   {{ md_to_html(response) | safe }}
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget/result.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget/result.html
index c0c4e9856..ae02baac5 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget/result.html
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui-widget/result.html
@@ -10,7 +10,7 @@
 {% if aqa_response_in_html != "" %}
   <!--Gemini AQA template-->
   <div class="question">
-    <h2>{{ question | replace("+", " ") | replace("%3F", "?")}}</h2>
+    <h2>{{ question }}</h2>
   </div>
   {% if probability|float > 0.7 %}
   <div class="important-box" id="important-box">
@@ -93,7 +93,7 @@ <h2 class="handle">
 {% else %}
   <!--Gemini Pro template-->
   <div class="question">
-    <h2>{{ question | replace("+", " ") | replace("%3F", "?")}}</h2>
+    <h2>{{ question }}</h2>
   </div>
   <div class="important-box" id="important-box">
     <!--Uses the URL from the first result-->
@@ -146,7 +146,7 @@ <h2 class="handle">
 <div class="rewrite hidden" id="rewrite-box">
   <h4 id="rewrite-question-header">Question:</h4>
   <span id="rewrite-question-span">
-    <p>{{ question | replace("+", " ") | replace("%3F", "?")}}</p>
+    <p>{{ question }}</p>
   </span>
   <h4 id="rewrite-response-header">Gemini's response:</h4>
   <span id="rewrite-original-response-span">
@@ -181,7 +181,7 @@ <h4>Rewrite:</h4>
   {{ uuid | safe }}
 </div>
 <div class="hidden" id="feedback-question-span">
-  {{ question | replace("+", " ") | replace("%3F", "?")}}
+  {{ question }}
 </div>
 <div class="hidden" id="feedback-response-span">
   {{ md_to_html(response) | safe }}
@@ -213,7 +213,7 @@ <h4>Feedback:</h4>
   {{ uuid | safe }}
 </div>
 <div class="hidden" id="feedback-question-span">
-  {{ question | replace("+", " ") | replace("%3F", "?")}}
+  {{ question }}
 </div>
 <div class="hidden" id="feedback-response-span">
   {{ md_to_html(response) | safe }}
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui/result.html b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui/result.html
index 0999c7a55..5c5bcdcaa 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui/result.html
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/chatbot/templates/chatui/result.html
@@ -10,7 +10,7 @@
 {% if aqa_response_in_html != "" %}
   <!--Gemini AQA template-->
   <div class="question">
-    <h2>{{ question | replace("+", " ") | replace("%3F", "?")}}</h2>
+    <h2>{{ question }}</h2>
   </div>
   <div class="important-box" id="important-box">
     <b>Important:</b> The answer below is generated by the Gemini AQA model.
@@ -63,7 +63,7 @@ <h2 class="handle">
 {% else %}
   <!--Gemini Pro template-->
   <div class="question">
-    <h2>{{ question | replace("+", " ") | replace("%3F", "?")}}</h2>
+    <h2>{{ question }}</h2>
   </div>
   <div class="important-box" id="important-box">
     <!--Uses the URL from the first result-->
@@ -116,7 +116,7 @@ <h2 class="handle">
 <div class="rewrite hidden" id="rewrite-box">
   <h4 id="rewrite-question-header">Question:</h4>
   <span id="rewrite-question-span">
-    <p>{{ question | replace("+", " ") | replace("%3F", "?")}}</p>
+    <p>{{ question }}</p>
   </span>
   <h4 id="rewrite-response-header">Gemini's response:</h4>
   <span id="rewrite-original-response-span">
@@ -151,7 +151,7 @@ <h4>Rewrite:</h4>
   {{ uuid | safe }}
 </div>
 <div class="hidden" id="feedback-question-span">
-  {{ question | replace("+", " ") | replace("%3F", "?")}}
+  {{ question }}
 </div>
 <div class="hidden" id="feedback-response-span">
   {{ md_to_html(response) | safe }}
@@ -183,7 +183,7 @@ <h4>Feedback:</h4>
   {{ uuid | safe }}
 </div>
 <div class="hidden" id="feedback-question-span">
-  {{ question | replace("+", " ") | replace("%3F", "?")}}
+  {{ question }}
 </div>
 <div class="hidden" id="feedback-response-span">
   {{ md_to_html(response) | safe }}
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_runtask.py b/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_runtask.py
index 7deea6f0b..3892f8093 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_runtask.py
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/cli/cli_runtask.py
@@ -78,6 +78,11 @@ def cli_runtask(ctx, config_file, product):
     default=None,
     multiple=True,
 )
+@click.option(
+    "--custom_input",
+    help="Specify an input string for the task.",
+    default=None,
+)
 @click.pass_context
 def runtask(
     # Words can be used to try to find an agent or match to helpme/tellme
@@ -85,6 +90,7 @@ def runtask(
     words,
     task_config: typing.Optional[str],
     task: typing.Optional[str],
+    custom_input: typing.Optional[str] = None,
     model: typing.Optional[str] = None,
     force: bool = False,
     # task: typing.Optional[str] = None,
@@ -150,7 +156,15 @@ def runtask(
             exit(1)
     # Get the words before any flags.
     user_query = ""
-    if words == () and (task == () or task == None) and (task_config == "" or task_config == None or task_config == os.path.join(get_project_path(), "tasks")):
+    if (
+        words == ()
+        and (task == () or task == None)
+        and (
+            task_config == ""
+            or task_config == None
+            or task_config == os.path.join(get_project_path(), "tasks")
+        )
+    ):
         print()
         print("Please specify a task. These are the available tasks:")
         for item in tasks_config.tasks:
@@ -177,11 +191,15 @@ def runtask(
         try:
             result = re.match(md_cb_regex, output)
             if result is None:
-                print(f"The LLM did not return a valid response for {user_query}. Try again.")
+                print(
+                    f"The LLM did not return a valid response for {user_query}. Try again."
+                )
                 exit(1)
             found = re.search("^name=(.*)$", result.group(1))
             if found is None:
-                print(f"The LLM did not return a valid response for {user_query}. Try again.")
+                print(
+                    f"The LLM did not return a valid response for {user_query}. Try again."
+                )
                 exit(1)
             # Prints the remaining response from model
             print()
@@ -204,10 +222,10 @@ def runtask(
                 )
                 exit(1)
             elif found.group(1) == "helpme":
-            #     and click.confirm(
-            #     f"Should I try to help you with {user_query}?",
-            #     abort=True,
-            # ):
+                #     and click.confirm(
+                #     f"Should I try to help you with {user_query}?",
+                #     abort=True,
+                # ):
                 print()
                 ctx.invoke(
                     helpme,
@@ -216,13 +234,15 @@ def runtask(
                     new=True,
                     model="models/gemini-1.5-flash-latest",
                 )
-                print("Follow-up usage: agent helpme <more text> --cont --models/gemini-1.5-flash-latest")
+                print(
+                    "Follow-up usage: agent helpme <more text> --cont --models/gemini-1.5-flash-latest"
+                )
                 exit(1)
             elif found.group(1) == "tellme":
-            # and click.confirm(
-            #     f"Should I tell you more about your question - {user_query}?",
-            #     abort=True,
-            # ):
+                # and click.confirm(
+                #     f"Should I tell you more about your question - {user_query}?",
+                #     abort=True,
+                # ):
                 print()
                 # Call helpme instead of tellme until rag is properly hooked in
                 ctx.invoke(
@@ -265,6 +285,14 @@ def runtask(
         ):
             this_preamble = curr_task.preamble
 
+        # Check if a custom input string is provided.
+        if custom_input is not None:
+            print()
+            print(f"Custom input: {custom_input}")
+            print(
+                f"This input string will replace the <INPUT> placeholder in this task run."
+            )
+
         # Ask the user to confirm.
         if force or click.confirm(
             f"\nPreparing to launch task:\n\n"
@@ -278,6 +306,7 @@ def runtask(
             else:
                 print(f"Starting task: {curr_task.name}")
                 # print(f"{curr_task}")
+            list_of_output_files = ""
             this_step = 0
             for task in curr_task.steps:
                 this_step += 1
@@ -328,6 +357,7 @@ def runtask(
                 this_yaml = None
                 this_rag = None
                 this_terminal = None
+                this_default_input = None
                 if hasattr(task, "flags"):
                     if hasattr(task.flags, "file"):
                         this_file = task.flags.file
@@ -345,6 +375,8 @@ def runtask(
                         this_rag = task.flags.rag
                     if hasattr(task.flags, "terminal"):
                         this_terminal = task.flags.terminal
+                    if hasattr(task.flags, "default_input"):
+                        this_default_input = task.flags.default_input
 
                 # Set the out filename to the default name.
                 if this_out is None or this_out == "":
@@ -356,6 +388,53 @@ def runtask(
                         + ".md"
                     )
 
+                list_of_output_files += (
+                    "* Step {:d}:".format(this_step) + " agent_out/" + this_out + "\n"
+                )
+
+                # Update the file-related fields if they are set to <INPUT> in the task file.
+                if custom_input is not None:
+                    # First try to replace them with the custom input value provided by
+                    # the --custom_input flag at runtime
+                    if this_file == "<INPUT>":
+                        this_file = custom_input
+                    if this_perfile == "<INPUT>":
+                        this_perfile = custom_input
+                    if this_allfiles == "<INPUT>":
+                        this_allfiles = custom_input
+                elif this_default_input is not None:
+                    # If no custom_input value is provided at runtime,
+                    # try to replace them with the default input value provided in the task file.
+                    if this_file == "<INPUT>":
+                        this_file = this_default_input
+                    if this_perfile == "<INPUT>":
+                        this_perfile = this_default_input
+                    if this_allfiles == "<INPUT>":
+                        this_allfiles = this_default_input
+                else:
+                    # Error and exit if there is still <INPUT> in any fields.
+                    if (
+                        this_file == "<INPUT>"
+                        or this_perfile == "<INPUT>"
+                        or this_allfiles == "<INPUT>"
+                    ):
+                        print()
+                        print(
+                            f"Error: Detected <INPUT> in the task fields. You must use the --custom_input flag to specify an input string for the task."
+                        )
+                        print(
+                            f"Usage: agent runtask --task <TASK_NAME> --custom_input <CUSTOM_INPUT_STRING>"
+                        )
+                        exit(1)
+
+                # If not specified, set the function field to "helpme".
+                if task.function is None:
+                    task.function = "helpme"
+
+                # If not specified, set the name field to an empty string.
+                if task.name is None:
+                    task.name = ""
+
                 # Select the command type: helpme, tellme, posix
                 if task.function == "helpme":
                     # helpme Task
@@ -379,7 +458,12 @@ def runtask(
                         print(f"Prompt: {task.prompt}")
                         print(f"===================")
                         print()
-                    overwrite_words = this_preamble + "\n" + task.prompt
+                    overwrite_words = (
+                        "FOLLOW THESE RULES FOR THE USER PROMPT: "
+                        + this_preamble
+                        + "\n\nUSER PROMPT: "
+                        + task.prompt
+                    )
                     overwrite_words = overwrite_words.split()
                     ctx.invoke(
                         helpme,
@@ -422,7 +506,12 @@ def runtask(
                         print(f"Prompt: {task.prompt}")
                         print(f"===================")
                         print()
-                    overwrite_words = this_preamble + "\n" + task.prompt
+                    overwrite_words = (
+                        "FOLLOW THESE RULES FOR THE USER PROMPT: "
+                        + this_preamble
+                        + "\n\nUSER PROMPT: "
+                        + task.prompt
+                    )
                     overwrite_words = overwrite_words.split()
                     ctx.invoke(
                         tellme,
@@ -465,6 +554,10 @@ def runtask(
                     exit(1)
                 time.sleep(3)
 
+        print()
+        print("[Output files]\n")
+        print(list_of_output_files)
+
 
 cli = click.CommandCollection(
     sources=[cli_runtask],
diff --git a/examples/gemini/python/docs-agent/docs_agent/interfaces/run_console.py b/examples/gemini/python/docs-agent/docs_agent/interfaces/run_console.py
index 3374441cd..99a216497 100644
--- a/examples/gemini/python/docs-agent/docs_agent/interfaces/run_console.py
+++ b/examples/gemini/python/docs-agent/docs_agent/interfaces/run_console.py
@@ -395,8 +395,9 @@ def ask_model_with_file(
                 total=None,
             )
             final_context = file_content
-        # print(f"Context:\n{final_context}")
-        # print(f"Question:\n{question}")
+
+        #print(f"Context:\n{final_context}")
+        #print(f"Question:\n{question}")
         # Ask Gemini with the question and final context.
         (
             response,
diff --git a/examples/gemini/python/docs-agent/docs_agent/models/google_genai.py b/examples/gemini/python/docs-agent/docs_agent/models/google_genai.py
index da9e82058..ab37ac297 100644
--- a/examples/gemini/python/docs-agent/docs_agent/models/google_genai.py
+++ b/examples/gemini/python/docs-agent/docs_agent/models/google_genai.py
@@ -125,12 +125,25 @@ def embed(
     # TODO: bring in limit values from config files
     @sleep_and_retry
     @limits(calls=max_text_per_minute, period=minute)
-    def generate_content(self, contents):
+    def generate_content(self, contents, log_level: typing.Optional[str] = "NORMAL"):
         if self.language_model is None:
             raise GoogleUnsupportedModelError(self.language_model, self.api_endpoint)
         model = google.generativeai.GenerativeModel(model_name=self.language_model)
-        response = model.generate_content(contents)
+        try:
+            response = model.generate_content(contents)
+        except google.api_core.exceptions.InvalidArgument:
+            return self.model_error_message
+        if log_level == "VERBOSE" or log_level == "DEBUG":
+            print("[Response JSON]")
+            print(response)
+            print()
         for chunk in response:
+            if not hasattr(chunk, "candidates"):
+                return self.model_error_message
+            if len(chunk.candidates) == 0:
+                return self.model_error_message
+            if not hasattr(chunk.candidates[0], "content"):
+                return self.model_error_message
             if str(chunk.candidates[0].content) == "":
                 return self.model_error_message
         return response.text
@@ -138,7 +151,11 @@ def generate_content(self, contents):
     # Use this method for talking to a Gemini content model
     # Optionally provide a prompt, if not use the one from config.yaml
     def ask_content_model_with_context_prompt(
-        self, context: str, question: str, prompt: typing.Optional[str] = None
+        self,
+        context: str,
+        question: str,
+        prompt: typing.Optional[str] = None,
+        log_level: typing.Optional[str] = "NORMAL",
     ):
         if prompt == None:
             prompt = self.prompt_condition
@@ -152,7 +169,17 @@ def ask_content_model_with_context_prompt(
             response = self.generate_content(new_prompt)
         except google.api_core.exceptions.InvalidArgument:
             return self.model_error_message
+        if log_level == "VERBOSE" or log_level == "DEBUG":
+            print("[Response JSON]")
+            print(response)
+            print()
         for chunk in response:
+            if not hasattr(chunk, "candidates"):
+                return self.model_error_message
+            if len(chunk.candidates) == 0:
+                return self.model_error_message
+            if not hasattr(chunk.candidates[0], "content"):
+                return self.model_error_message
             if str(chunk.candidates[0].content) == "":
                 return self.model_error_message
         return response.text, new_prompt
diff --git a/examples/gemini/python/docs-agent/docs_agent/utilities/tasks.py b/examples/gemini/python/docs-agent/docs_agent/utilities/tasks.py
index ad6b85ef2..5b846bd80 100644
--- a/examples/gemini/python/docs-agent/docs_agent/utilities/tasks.py
+++ b/examples/gemini/python/docs-agent/docs_agent/utilities/tasks.py
@@ -39,6 +39,7 @@ def __init__(
         new: typing.Optional[bool] = None,
         cont: typing.Optional[str] = None,
         terminal: typing.Optional[str] = None,
+        default_input: typing.Optional[str] = None,
     ):
         self.model = model
         self.file = file
@@ -51,6 +52,7 @@ def __init__(
         self.new = new
         self.cont = cont
         self.terminal = terminal
+        self.default_input = default_input
 
     def __str__(self):
         help_str = ""
@@ -62,9 +64,11 @@ def __str__(self):
             help_str += f"Per file: {self.perfile}\n"
         if self.allfiles is not None and self.allfiles != "":
             help_str += f"All files: {self.allfiles}\n"
+        if self.default_input is not None and self.default_input != "":
+            help_str += f"Default input: {self.default_input}\n"
         if self.file_ext is not None and self.file_ext != "":
             help_str += f"File ext: {self.file_ext}\n"
-        if self.rag is not None and self.rag!= False:
+        if self.rag is not None and self.rag != False:
             help_str += f"RAG: {str(self.rag)}\n"
         if self.yaml is not None and self.yaml != "":
             help_str += f"YAML: {self.yaml}\n"
@@ -124,6 +128,10 @@ def dictionaryToFlags(flags: dict) -> Flags:
         terminal = str(flags["terminal"])
     else:
         terminal = ""
+    if "default_input" in flags:
+        default_input = str(flags["default_input"])
+    else:
+        default_input = ""
     flags = Flags(
         model=model,
         file=file,
@@ -136,6 +144,7 @@ def dictionaryToFlags(flags: dict) -> Flags:
         new=new,
         cont=cont,
         terminal=terminal,
+        default_input=default_input,
     )
     return flags
 
@@ -143,15 +152,15 @@ def dictionaryToFlags(flags: dict) -> Flags:
 class Step:
     def __init__(
         self,
-        name: str,
-        function: typing.Optional[str],
         prompt: typing.Optional[str],
+        name: typing.Optional[str] = None,
+        function: typing.Optional[str] = None,
         flags: typing.Optional[Flags] = None,
         description: typing.Optional[str] = None,
     ):
+        self.prompt = prompt
         self.name = name
         self.function = function
-        self.prompt = prompt
         self.flags = flags
         self.description = description
 
@@ -186,9 +195,9 @@ def returnSteps(self) -> list[Step]:
                     flags = None
                 # Using .get let's you specify optional keys
                 step_item = Step(
-                    name=item["name"],
-                    function=item["function"],
                     prompt=item["prompt"],
+                    name=item.get("name", None),
+                    function=item.get("function", None),
                     flags=flags,
                     description=item.get("description", None),
                 )
@@ -251,7 +260,7 @@ def __init__(
         self.tasks = tasks
 
     def __str__(self):
-        output =  []
+        output = []
         for item in self.tasks:
             output.append(str(item))
         return str(output)
@@ -273,7 +282,10 @@ def return_task(self, task: str):
 class ReadTaskConfig:
     # Tries to ingest a task configuration file and validate its keys.
     def __init__(
-        self, yaml_path: str = os.path.join(get_project_path(), "tasks/release-notes-task.yaml")
+        self,
+        yaml_path: str = os.path.join(
+            get_project_path(), "tasks/release-notes-task.yaml"
+        ),
     ):
         self.yaml_path = yaml_path
         try:
@@ -328,9 +340,7 @@ def returnTasks(self, task: typing.Optional[str] = None) -> TaskConfigFile:
                         break
                 # Exits if product is not found
                 if not match:
-                    logging.error(
-                        f"The task {task} does not exist in {self.yaml_path}"
-                    )
+                    logging.error(f"The task {task} does not exist in {self.yaml_path}")
                     return sys.exit(1)
             tasks_file = TaskConfigFile(tasks=tasks)
             return tasks_file
diff --git a/examples/gemini/python/docs-agent/pyproject.toml b/examples/gemini/python/docs-agent/pyproject.toml
index 095ec4904..c39b7fbbb 100644
--- a/examples/gemini/python/docs-agent/pyproject.toml
+++ b/examples/gemini/python/docs-agent/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "docs-agent"
-version = "0.3.5"
+version = "0.3.6"
 description = ""
 authors = ["Docs Agent contributors"]
 readme = "README.md"
diff --git a/examples/gemini/python/docs-agent/tasks/extract-workflows-task.yaml b/examples/gemini/python/docs-agent/tasks/extract-workflows-task.yaml
new file mode 100644
index 000000000..52748de2c
--- /dev/null
+++ b/examples/gemini/python/docs-agent/tasks/extract-workflows-task.yaml
@@ -0,0 +1,12 @@
+tasks:
+  - name: "ExtractWorkflows"
+    model: "models/gemini-1.5-flash-latest"
+    description: "An agent that extracts workflows from a source doc."
+    steps:
+      - prompt: "Summarize the contents of this document in a concise and informative manner. Focus on the key procedures, steps, or workflows described."
+        flags:
+          file: "<INPUT>"
+          default_input: "./README.md"
+      - prompt: "Identify and list all key workflows described in the document. Provide a brief description for each workflow, highlighting its purpose and key steps."
+      - prompt: "Identify all command lines used in the workflows described in the document.  Focus on command lines that are essential for executing the workflow steps."
+      - prompt: "For each identified command line, provide a detailed description of its function and purpose.  Include specific examples of its usage, showcasing how it is integrated within the workflows."
diff --git a/examples/gemini/python/docs-agent/tasks/help-polish-prompts-task.yaml b/examples/gemini/python/docs-agent/tasks/help-polish-prompts-task.yaml
new file mode 100644
index 000000000..a1283abed
--- /dev/null
+++ b/examples/gemini/python/docs-agent/tasks/help-polish-prompts-task.yaml
@@ -0,0 +1,13 @@
+tasks:
+  - name: "HelpPolishPrompts"
+    model: "models/gemini-1.5-flash-latest"
+    description: "An agent that helps polish prompts in a task file."
+    preamble: "When writing a prompt, always be direct and concise."
+    steps:
+      - prompt: "Revise the prompts in this task YAML file to improve their effectiveness in generating responses."
+        flags:
+          file: "<INPUT>"
+          default_input: "./tasks/index-page-generator-task.yaml"
+          file_ext: "yaml"
+      - prompt: "Replace the original prompts in the task YAML file with the revised prompts."
+      - prompt: "Suggest additional prompts relevant to the main task in this task YAML file."
diff --git a/examples/gemini/python/docs-agent/tasks/index-page-generator-task.yaml b/examples/gemini/python/docs-agent/tasks/index-page-generator-task.yaml
new file mode 100644
index 000000000..dfed4c666
--- /dev/null
+++ b/examples/gemini/python/docs-agent/tasks/index-page-generator-task.yaml
@@ -0,0 +1,15 @@
+tasks:
+  - name: "IndexPageGenerator"
+    model: "models/gemini-1.5-flash-latest"
+    description: "An agent that generates a draft of an index page from source docs."
+    preamble: "When generating a Markdown file, limit the number of characters per line to 80 or less, and always include a brief description to each page in the list."
+    steps:
+      - prompt: "Explain the files in this directory."
+        flags:
+          allfiles: "<INPUT>"
+          default_input: "./docs_agent"
+          file_ext: "md"
+      - prompt: "Generate a draft of an index page that lists all the pages above so that the pages are organized by related topics for new developers."
+      - prompt: "Update the introduction paragraph of the draft above to be more helpful and descriptive (and keep the rest of the draft intact on the page)."
+      - prompt: "Add Markdown reference-style links that maps certain key words to the files found above (and add a list of the reference-style links at the bottom of the page)."
+
diff --git a/examples/gemini/python/docs-agent/tasks/release-notes-task.yaml b/examples/gemini/python/docs-agent/tasks/release-notes-task.yaml
index 27a47b5b8..0b35cd565 100644
--- a/examples/gemini/python/docs-agent/tasks/release-notes-task.yaml
+++ b/examples/gemini/python/docs-agent/tasks/release-notes-task.yaml
@@ -9,7 +9,7 @@ tasks:
         prompt: "git --no-pager log --since=2024-05-26"
       - name: "Create an initial release notes draft"
         function: "helpme"
-        prompt: "Based on the commit messages above, create an intial release notes draft that introduces new features and brings attention to important messages."
+        prompt: "Based on the commit messages above, create an initial release notes draft that introduces new features and brings attention to important messages."
       - name: "Revise the release notes draft"
         function: "helpme"
         prompt: "Revise the release notes draft above to be more concise and better structured, and if appropriate, include practical examples."