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We also recommend that a - file or class name and description of purpose be included on the - same "printed page" as the copyright notice for easier - identification within third-party archives. - - Copyright [yyyy] [name of copyright owner] - - 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. \ No newline at end of file diff --git a/examples/gemini/python/docs-agent/README.md b/examples/gemini/python/docs-agent/README.md deleted file mode 100644 index d3d368eb9..000000000 --- a/examples/gemini/python/docs-agent/README.md +++ /dev/null @@ -1,432 +0,0 @@ -# Docs Agent - -The Docs Agent project explores applications and use cases that involve using a large -corpus of documentation as a knowledge source for AI language models. - -Docs Agent provides a set of easy-to-use self-service tools designed to give you and -your team access to Google's [Gemini API][genai-doc-site] for learning, experimentation, -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 architecture](docs/images/docs-agent-architecture-01.png) - -**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. - -### Summary of features - -The following list 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].) -- **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. -- **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][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`). -- **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. - -![Docs Agent chat app](docs/images/docs-agent-chat-app-screenshot-01.png) - -**Figure 2**. A screenshot of the Docs Agent chat app launched using Flutter docs. - -## Set up Docs Agent - -**Note**: For instructions on the Docs Agent CLI setup, see the -[`README.md`][cli-readme] file in the `docs_agent/interfaces` directory. - -This section provides instructions on how to set up and launch the Docs Agent -chatbot web app on a Linux host machine. - -### 1. Prerequisites - -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]) - - - (**Optional**) [Authenticated OAuth client credentials][oauth-client] - stored on the host machine - -### 2 Update your host machine's environment - -Update your host machine's environment to prepare for the Docs Agent setup: - -1. Update the Linux package repositories on the host machine: - - ``` - sudo apt update - ``` - -2. Install the following dependencies: - - ``` - sudo apt install git pipx python3-venv - ``` - -3. Install `poetry`: - - ``` - pipx install poetry - ``` - -4. To add `$HOME/.local/bin` to your `PATH` variable, run the following - command: - - ``` - pipx ensurepath - ``` - -5. To set the Google API key as a environment variable, add the following - line to your `$HOME/.bashrc` file: - - ``` - export GOOGLE_API_KEY= - ``` - - Replace `` with the API key to the - [Gemini API][genai-doc-site]. - -6. Update your environment: - - ``` - source ~/.bashrc - ``` - -### 3. (Optional) Authorize credentials for Docs Agent - -**This step is needed only if you plan to use [Gemini's AQA model][aqa-model-concept].** - -Authorize Google Cloud credentials on your host machine: - -1. Download the `client_secret.json` file from your - [Google Cloud project][authorize-credentials]. - -2. Copy the `client_secret.json` file to your host machine. - -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: - - ``` - gcloud auth application-default login --client-id-file=client_secret.json --scopes='https://www.googleapis.com/auth/cloud-platform,https://www.googleapis.com/auth/generative-language.retriever' - ``` - - This command opens a browser and asks to log in using your Google account. - -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 `$HOME/.config/gcloud/` - directory of your host machine. - -### 4. Clone the Docs Agent project - -**Note**: This guide assumes that you're creating a new project directory -from your `$HOME` directory. - -Clone the Docs Agent project and install dependencies: - -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. Enter the `poetry` shell environment: - - ``` - poetry shell - ``` - - **Important**: From this point, all `agent` command lines below need to - run in this `poetry shell` environment. - -### 5. Edit the Docs Agent configuration file - -This guide uses the [open source Flutter documents][flutter-docs-src] as an example dataset, -which are the source Markdown files for the [Flutter website][flutter-docs-site]. - -To complete this setup walkthrough, run the command below to download the open source -Flutter documents somewhere on your host machine (for instance, in your `$HOME` directory): - -``` -git clone --recurse-submodules https://github.com/flutter/website.git -``` - -Update settings in the Docs Agent project to use your custom dataset: - -1. Go to the Docs Agent project home directory, for example: - - ``` - cd $HOME/generative-ai-docs/examples/gemini/python/docs-agent - ``` - -2. Open the [`config.yaml`][config-yaml] file using a text editor, for example: - - ``` - nano config.yaml - ``` - -3. Edit the file to update the `product_name` field, for example: - - ``` - product_name: "Flutter" - ``` - - This product name is displayed on the Docs Agent chat app UI. - -4. Under the `inputs` field, define the following entries to specify the directories - that contain your source Markdown files. - - - `path`: The directory where the source Markdown files are stored. - - `url_prefix`: The prefix used to create URLs for the source Markdown files. - - **Important**: If URLs do not exist for your Markdown files, you still need to - provide a placeholder string in the `url_prefix` field. - - The example below shows the entries for the Flutter documents downloaded in the - `$HOME/website` directory): - - ``` - inputs: - - path: "/usr/local/home/user01/website/src/content" - url_prefix: "https://docs.flutter.dev" - ``` - - You can also provide multiple input directories (`path` and `url_prefix` sets) under - the `inputs` field, for example: - - ``` - inputs: - - path: "/usr/local/home/user01/website/src/content/ui" - url_prefix: "https://docs.flutter.dev/ui" - - path: "/usr/local/home/user01/website/src/content/tools" - url_prefix: "https://docs.flutter.dev/tools" - ``` - -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/gemini-pro" - ... - db_type: "chroma" - ``` - - (**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 (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. - - -### 6. Populate a new vector database - -The Docs Agent CLI can help you chunk documents, generate embeddings extract metadata, -and populate a vector database from Markdown files and more. - -**Note**: The `agent` commands below need to run within the `poetry shell` environment. - -To populate a new vector database: - -1. Go to the Docs Agent project home directory, for example: - - ``` - cd $HOME/generative-ai-docs/examples/gemini/python/docs-agent - ``` - -2. Process Markdown files into small text chunks: - - ``` - agent chunk - ``` - - The command takes documents under the `inputs` fields (specified in your - `config.yaml` file), splits the documents into small text chunk files, and - stores them in the `output_path` direcoty. - -3. Create and populate a new vector database: - - ``` - agent populate - ``` - - This command takes the plain text files in the `output_path` directory - and creates a new Chroma collection in the `vector_stores/` directory. - -### 7. Launch the Docs Agent chat app - -Docs Agent's Flask-based chat app lets users interact with the Docs Agent service through -a web browser. - -**Note**: The `agent chatbot` command needs to run within the `poetry shell` environment. - -To start the Docs Agent chat app: - -1. Go to the Docs Agent project home directory, for example: - - ``` - cd $HOME/generative-ai-docs/examples/gemini/python/docs-agent - ``` - -2. Launch the Docs Agent chat app: - - ``` - agent chatbot - ``` - - The Docs Agent chat app runs on port 5000 by default. If you have an application - already running on port 5000 on your host machine, you can use the `--port` flag to - specify a different port (for example, `agent chatbot --port 5050`). - - **Note**: If this `agent chatbot` command fails to run, check the `HOSTNAME` environment - variable on your host machine (for example, `echo $HOSTNAME`). If this variable is unset, - try setting it to `localhost` by running `export HOSTNAME=localhost` - - Once the app starts running, this command prints output similar to the following: - - ``` - $ agent chatbot - Launching the chatbot UI. - * Serving Flask app 'docs_agent.interfaces.chatbot' - * Debug mode: on - INFO:werkzeug:WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead. - * Running on http://example.com:5000 - INFO:werkzeug:Press CTRL+C to quit - INFO:werkzeug: * Restarting with stat - Launching the chatbot UI. - WARNING:werkzeug: * Debugger is active! - INFO:werkzeug: * Debugger PIN: 391-260-142 - ``` - - Notice the line that shows the URL of this server (`http://example.com:5000` - in the example above). - -3. Open the URL above on a browser. - - Now, users can start asking questions related to your product. - -**The Docs Agent chat app is all set!** - -## Contributors - -Nick Van der Auwermeulen (`@nickvander`), Rundong Du (`@rundong08`), -Meggin Kearney (`@Meggin`), and Kyo Lee (`@kyolee415`). - - - -[contribute-to-docs-agent]: #contribute-to-docs-agent -[set-up-docs-agent]: #set-up-docs-agent -[preprocess-dir]: ./docs_agent/preprocess/ -[populate-vector-database]: ./docs_agent/preprocess/populate_vector_database.py -[fact-check-section]: ./docs/concepts.md#using-a-language-model-to-fact_check-its-own-response -[related-questions-section]: ./docs/concepts.md#using-a-language-model-to-suggest-related-questions -[submit-a-rewrite]: ./docs/concepts.md#enabling-users-to-submit-a-rewrite-of-a-generated-response -[like-generated-responses]: ./docs/concepts.md#enabling-users-to-like-generated-responses -[populate-db-steps]: #populate-a-new-vector-database-from-markdown-files -[start-the-app-steps]: #start-the-docs-agent-chat-app -[genai-doc-site]: https://ai.google.dev/docs/gemini_api_overview -[chroma-docs]: https://docs.trychroma.com/ -[flutter-docs-src]: https://github.com/flutter/website/tree/main/src -[flutter-docs-site]: https://docs.flutter.dev/ -[apps-script-readme]: ./apps_script/README.md -[scripts-readme]: ./docs_agent/preprocess/README.md -[config-yaml]: config.yaml -[benchmark-test]: ./docs_agent/benchmarks/README.md -[semantic-api]: https://ai.google.dev/docs/semantic_retriever -[aqa-model]: https://ai.google.dev/models/gemini#model_variations -[authorize-credentials]: https://ai.google.dev/docs/oauth_quickstart#authorize-credentials -[aqa-model-concept]: ./docs/concepts.md#using-the-semantic-retrieval-api-and-aqa-model -[prompt-structure]: ./docs/concepts.md#structure-of-a-prompt-to-a-language-model -[docs-agent-concepts]: ./docs/concepts.md -[google-cloud]: https://console.cloud.google.com/ -[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 -[tasks-dir]: tasks/ diff --git a/examples/gemini/python/docs-agent/apps_script/README.md b/examples/gemini/python/docs-agent/apps_script/README.md deleted file mode 100644 index d333f36a8..000000000 --- a/examples/gemini/python/docs-agent/apps_script/README.md +++ /dev/null @@ -1,165 +0,0 @@ -# Convert Google Docs, PDF, and Gmail to Markdown files - -The collection of scripts in this `apps_script` directory allows you to convert -the contents of Google Drive folders and Gmail to Markdown files that are -compatible with Docs Agent. - -The steps are: - -1. [Prepare a Google Drive folder](#1_prepare-a-google-driver-folder). -2. [Mount Google Drive on your host machine](#2_mount-google-drive-on-your-host-machine). -3. [Create an Apps Script project](#3_create-an-apps-script-project). -4. [Edit and run main.gs on Apps Script](#4_edit-and-run-main_gs-on-apps-script). -5. [Update config.yaml to include the mounted directory](#5_update-config_yaml-to-include-the-mounted-directory). - -## 1. Prepare a Google Drive folder - -First, create a new folder in Google Drive and add your Google Docs (which will be -used as source documents to Docs Agent) to the folder. - -Do the following: - -1. Browser to https://drive.google.com/. -1. Click **+ New** on the top left corner. -1. Click **New folder**. -1. Name your new folder (for example, `my source Google Docs`). -1. To enter the newly created folder, double click the folder. -1. Add (or move) your source Google Docs to this new folder. - -## 2. Mount Google Drive on your host machine - -Mount your Google Drive to your host machine, so that it becomes easy to access the -folders in Google Drive from your host machine (later in step 5). - -There are a variety of methods and tools available online that enable this setup -(for example, see [`google-drive-ocamlfuse`][google-drive-ocamlfuse] for Linux machines). - -## 3. Create an Apps Script project - -Create a new Apps Script project and copy all the `.gs` scripts in this -`apps_script` directory to your new Apps Script project. - -Do the following: - -1. Browse to https://script.google.com/. -1. Click **New Project**. -1. At the top of the page, click **Untitled Project** and enter a meaningful - title (for example, `gDocs to Docs Agent`). -1. Click the **+** icon next to **Files**. -1. Click **Script**. -1. Name the new script to be one of the `.gs` files in this `apps_script` directory - (for example, `drive_to_markdown`). -1. Copy the content of the `.gs` file to the new script on your Apps Script project. -1. To save, click the "Save project" icon in the toolbar. -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 -in Google Drive and export emails from Gmail. - -![Apps Script project](../docs/images/apps-script-screenshot-01.png) - -**Figure 1**. A screenshot of an example Apps Script project. - -## 4. Edit and run main.gs on Apps Script - -Edit the `main.gs` file on your Apps Script project to select which functions -(features) you want to run. - -Do the following: - -1. Browse to your project on https://script.google.com/. - -1. Open the `main.gs` file. - -1. In the `main` function, comment out any functions that you don't want to run - (see Figure 1): - - * `convertDriveFolderToMDForDocsAgent(folderInput)`: This function converts - the contents of a Google Drive folder to Markdown files (currently only Google - Docs and PDF). Make sure to specify a valid Google Drive folder in the `folderInput` - variable. Use the name of the folder created in **step 1** above, for example: - - ``` - var folderInput = "my source Google Docs" - function main() { - convertDriveFolderToMDForDocsAgent(folderInput); - //exportEmailsToMarkdown(SEARCH_QUERY, folderOutput); - } - ``` - - * `exportEmailsToMarkdown(SEARCH_QUERY, folderOutput)`: This function converts - the emails returned from a Gmail search query into Markdown files. Make sure to - specify a search query in the `SEARCH_QUERY` variable. You can test this search - query directly in the Gmail search bar. Also, specify an output directory for the - resulting emails. - -1. To save, click the "Save project" icon in the toolbar. - -1. Click the "Run" icon in the toolbar. - - When this script runs successfully, the Execution log panel prints output similar - to the following: - - ``` - 9:55:59 PM Notice Execution completed - ``` - - Also, the script creates a new folder in your Google Drive and stores the converted - Markdown files in this folder. The name of this new folder has `-output` as a postfix. - For example, with the folder name `my source Google Docs`, the name of the new folder - is `my source Google Docs-output`. - - With Google Drive mounted on your host machine in step 2, you can now directly access - this folder from the host machine, for example: - - ``` - user@hostname:~/DriveFileStream/My Drive/my source Google Docs-output$ ls - Copy_of_My_Google_Docs_To_Be_Converted.md - ``` - -## 5. Update config.yaml to include the mounted directory - -Once you have your Google Drive mounted on the host machine, you can now -specify one of its folders as an input source directory for Docs Agent. - -Do the following: - -1. In the Docs Agent project, open the [`config.yaml`][config-yaml] file - with a text editor. - -1. Specify your mounted Google Drive folder as an `input` group, for example: - - ``` - input: - - path: "/usr/local/home/user01/DriveFileStream/My Drive/my source Google Docs-output" - url_prefix: "docs.google.com" - ``` - - You **must** specify a value to the `url_prefix` field, such as `docs.google.com`. - Currently this value is used to generate hashes for the content. - -1. (**Optional**) Add an additional Google Drive folder for your exported emails, - for example: - - ``` - input: - - path: "/usr/local/home/user01/DriveFileStream/My Drive/my source Google Docs-output" - url_prefix: "docs.google.com" - - path: "/usr/local/home/user01/DriveFileStream/My Drive/psa-output" - url_prefix: "mail.google.com" - ``` - -1. Save the changes in the `config.yaml` file. - -You're all set with a new documentation source for Docs Agent. You can now follow the -instructions in the project's main [`README`][main-readme] file to launch the Docs Agent app. - - - -[config-yaml]: ../config.yaml -[main-readme]: ../README.md -[google-drive-ocamlfuse]: https://github.com/astrada/google-drive-ocamlfuse 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 deleted file mode 100644 index b19d33d84..000000000 --- a/examples/gemini/python/docs-agent/apps_script/drive_to_markdown.gs +++ /dev/null @@ -1,240 +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. - */ -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="") { - - //Checks if input folder exists or exits - if(folderExistsInput(folderName)){ - var file_count = 0; - var folders = DriveApp.getFoldersByName(folderName); - 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(); - } - 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; - var unchanged_file_count = 0; - var updated_file_count = 0; - var gdoc_count = 0; - var pdf_count = 0; - var start_data_row = 2; - var status = "New content"; - - 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(); - } - 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(); - var fcreate = myfile.getDateCreated(); - - //Function returns an array, assign each array value to seperate variables - var backup_results = returnBackupHash(sheet, "Backup", fid, start_data_row, 1, 9, 3); - if (backup_results != undefined && backup_results[0] != "no_results") { - var backup_fid = backup_results[0]; - 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); - let gdoc_blob = gdoc.getBody().getText(); - var md5_hash = Utilities.computeDigest(Utilities.DigestAlgorithm.MD5,gdoc_blob, - Utilities.Charset.US_ASCII); - var hash_str = byteToStr(md5_hash); - if (backup_fid == fid && hash_str == md5_backup) { - Logger.log("File is unchanged. Skipping conversion."); - if (mdoutput_backup_id){ - var saved_file = DriveApp.getFileById(mdoutput_backup_id); - var saved_file_id = saved_file.getId(); - } - status = "Unchanged content"; - unchanged_file_count += 1; - var convert_file = false; - } - else if (backup_fid == fid && hash_str != md5_backup){ - status = "Updated content"; - updated_file_count += 1; - var convert_file = true; - } - else { - status = "New content"; - new_file_count += 1; - var convert_file = true; - } - if (convert_file){ - var frontmatter = "---" + "\n"; - frontmatter += "title: \"" + fname + "\"\n"; - frontmatter += "type: \"" + ftype + "\"\n"; - frontmatter += "id: \"" + fid + "\"\n"; - frontmatter += "created: \"" + fcreate + "\"\n"; - frontmatter += "updated: \"" + fdate + "\"\n"; - frontmatter += "URL: \"" + furl + "\"\n"; - frontmatter += "---" + "\n\n"; - var saved_file = convertDocumentToMarkdown(gdoc, folderOutputName, frontmatter); - var saved_file_id = saved_file.getId(); - Logger.log("Finished converting file: " + fname + " to markdown."); - Logger.log("Markdown file: " + saved_file); - status = "New content"; - gdoc_count += 1; - } - file_count += 1; - } - if (ftype == "application/pdf") { - // Converts PDFs - First to a temporary Google Doc and then use convertDocumentToMarkdown to convert to markdown with frontmatter - Logger.log("File: " + fname + " is a PDF."); - let pdfBlob = DriveApp.getFileById(fid).getBlob(); - let pdfblobText = pdfBlob.getDataAsString(); - var md5_hash = Utilities.computeDigest(Utilities.DigestAlgorithm.MD5,pdfblobText, - Utilities.Charset.US_ASCII); - var hash_str = byteToStr(md5_hash); - if (backup_fid == fid && hash_str == md5_backup) { - Logger.log("File is unchanged. Skipping conversion."); - if (mdoutput_backup_id){ - var saved_file = DriveApp.getFileById(mdoutput_backup_id); - var saved_file_id = saved_file.getId(); - } - status = "Unchanged content"; - unchanged_file_count += 1; - var convert_file = false; - } - else if (backup_fid == fid && hash_str != md5_backup){ - status = "Updated content"; - updated_file_count += 1; - var convert_file = true; - } - else { - status = "New content"; - new_file_count += 1; - var convert_file = true; - } - if (convert_file){ - let temp_doc_name = pdfBlob.getName() + "-temp"; - let temp_doc = {title: temp_doc_name, mimeType: pdfBlob.getContentType(), parents: [{id: folderOutputName.getId()}]} - let options = {ocr: true}; - let output = Drive.Files.insert(temp_doc, pdfBlob, options); - let output_id = output.getId(); - let gdoc = DocumentApp.openById(output_id); - var frontmatter = "---" + "\n"; - frontmatter += "title: \"" + fname + "\"\n"; - frontmatter += "type: \"" + ftype + "\"\n"; - frontmatter += "id: \"" + fid + "\"\n"; - frontmatter += "created: \"" + fcreate + "\"\n"; - frontmatter += "updated: \"" + fdate + "\"\n"; - frontmatter += "URL: \"" + furl + "\"\n"; - frontmatter += "---" + "\n\n"; - var saved_file = convertDocumentToMarkdown(gdoc, folderOutputName, frontmatter); - 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" ); - let output_file = DriveApp.getFileById(output_id); - output_file.setTrashed(true); - status = "New content"; - pdf_count += 1; - } - file_count += 1; - } - let md_chip = createRichText(saved_file); - let original_chip = createRichText(myfile); - let folder_chip = createRichText(foldersnext); - metadata = [ - fname, - fid, - "original_chip", - saved_file_id, - "md_chip", - fcreate, - fdate, - ftype, - "folder_chip", - hash_str, - status, - ]; - 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); - } - } - return gdoc_count, pdf_count, new_file_count, updated_file_count, unchanged_file_count -} 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 100644 index 40a4461ff..000000000 --- a/examples/gemini/python/docs-agent/apps_script/exportmd.gs +++ /dev/null @@ -1,1310 +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. - */ - -/* 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 = ''; - - // The above was written with js since doesn't work: - // https://gist.github.com/lmmx/ec084fc351528395f2bb - - var mdstring = stringMiddleMan(); - - var htmlstring = - '' - + css_style - + '
'; - - 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 
 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+="
\n";
-      } else if (result.sourceGlossary==="end" && inSrc) {
-        inSrc=false;
-        text+="
\n\n";
-      } else if (result.sourceFigCap==="start" && !inSrc) {
-        inSrc=true;
-        text+="
\n";
-      } else if (result.sourceFigCap==="end" && inSrc) {
-        inSrc=false;
-        text+="
\n\n";
-      } else if (result.source==="start" && !inSrc) {
-        inSrc=true;
-        text+="
\n";
-      } else if (result.source==="end" && inSrc) {
-        inSrc=false;
-        text+="
\n\n";
-      } else if (result.inClass==="start" && !inClass) {
-        inClass=true;
-        text+="
\n";
-      } else if (result.inClass==="end" && inClass) {
-        inClass=false;
-        text+="
\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/g, '>');
-}
-
-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("\n");
-    var nCols = element.getChild(0).getNumCells();
-    for (var i = 0; i < element.getNumChildren(); i++) {
-      textElements.push("  \n");
-      // process this row
-      for (var j = 0; j < nCols; j++) {
-        textElements.push("    \n");
-      }
-      textElements.push("  \n");
-    }
-    textElements.push("
" + element.getChild(i).getChild(j).getText() + "
\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.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) {
-      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('  ');
-    } else {
-      Logger.log("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 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 = '';
-  } else {
-
-    prefix = findPrefix(inSrc, element, listCounters);
-
-    var pOut = "";
-    for (var i=0; i):
-      if (gt === DocumentApp.GlyphType.BULLET
-          || gt === DocumentApp.GlyphType.HOLLOW_BULLET
-          || gt === DocumentApp.GlyphType.SQUARE_BULLET) {
-        prefix += "* ";
-      } else {
-        // Ordered list (
    ):
-        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 = "" + d2;
-    }
-    if (mark_sup) {
-      d2 = '' + d2;
-    }
-    if (mark_sub) {
-      d2 = '' + 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 + '';
-    }
-
-    if (mark_sub) {
-      d1 = d1 + '';
-    }
-
-    if (mark_strike) {
-      d1 = d1 + '';
-    }
-
-    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$4");
-      mOut =  new_text + mOut;
-    }
-
-    lastOff=off;
-//    Logger.log("Modified String: " + mOut)
-  }
-
-  mOut = pOut.substring(0, off) + mOut;
-  return mOut;
-}
\ No newline at end of file
diff --git a/examples/gemini/python/docs-agent/apps_script/gmail_to_markdown.gs b/examples/gemini/python/docs-agent/apps_script/gmail_to_markdown.gs
deleted file mode 100644
index 3263ef100..000000000
--- a/examples/gemini/python/docs-agent/apps_script/gmail_to_markdown.gs
+++ /dev/null
@@ -1,137 +0,0 @@
-/**
- * Copyright 2023 Google LLC
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-function exportEmailsToMarkdown(search, folderName) {
-  //Checks if input folder exists or exits
-  if(folderExistsOrCreate(folderName)){
-    var output_file_name = folderName + "-index";
-    var folderOutputObj = DriveApp.getFoldersByName(folderName);
-    if (folderOutputObj.hasNext()){
-      var folderOutputName = folderOutputObj.next();
-    }
-    var sheet = checkIndexOutputOrCreate(output_file_name, folderOutputName);
-    console.log(`Searching for: "${search}"`);
-    var start = 0;
-    var max = 500;
-    var threads = GmailApp.search(search, start, max);
-    var threadMax = threads.length;
-    if (threads!=null){
-      console.log(threadMax + " threads found.");
-    } else {
-      console.warn("No threads found with the search criteria");
-      return;
-    }
-    let timeZone = Session.getScriptTimeZone();
-    let created_date = Utilities.formatDate(new Date(), timeZone, "MM-dd-yyyy HH:mm:ss z");
-    sheet.appendRow(["Created: ", created_date])
-    sheet.appendRow(["Date", "From", "Subject", "To", "Markdown ID", "Markdown URL", "Full date", "MD5 hash", "Status"]);
-    var start_data_row = 2;
-    var status = "New content";
-    var newEmails = 0;
-    var unchangedEmails = 0;
-    for (var threadCount in threads) {
-      var msgs = threads[threadCount].getMessages();
-      Logger.log("Processing thread " + threadCount + " of " + threadMax);
-      for (var msgCount in msgs) {
-        var msg = msgs[msgCount];
-        var subject = msg.getSubject().replace(/"/g, "\\\"");;
-        // Removes replies and forwards - Can mostly be noise.
-        if(!subject.toLowerCase().includes("re:") &&
-            !subject.toLowerCase().includes("fwd:") &&
-            !subject.toLowerCase().includes("forwarded message")){
-          // Values to get and store messages
-          var date = msg.getDate();
-          let from_author = msg.getFrom().replace(/"/g, "\\\"");
-          var hash_content = from_author + date + subject;
-          let sanitized_subject = sanitizeString(subject);
-          let date_format = Utilities.formatDate(date, "PST", "MM-dd-yyyy");
-          let to = msg.getTo();
-          let to_array = to.split(", ");
-          for (i in to_array) {
-            to_array[i] = "\"" + to_array[i].replace(/^" "/, "").replace(/"/g, "\\\"") + "\"";
-          }
-          let md5_hash = Utilities.computeDigest(Utilities.DigestAlgorithm.MD5,hash_content,
-                          Utilities.Charset.US_ASCII);
-          let hash_str = byteToStr(md5_hash);
-          //Function returns an array, assign each array value to seperate variables. For emails, only need to retrieve
-          // backup markdown ids
-          var backup_results = returnBackupHash(sheet, "Backup", hash_str, start_data_row, 7, 4, 5);
-          if (backup_results != undefined && backup_results[0] != "no_results") {
-            Logger.log("Email is already in markdown format. Skipping conversion.");
-            var status = "Unchanged content";
-            var markdown_id = backup_results[1];
-            if (markdown_id){
-              var md_file = DriveApp.getFileById(markdown_id);
-            }
-            unchangedEmails += 1;
-          }
-          else {
-            var status = "New content";
-            let message = msg.getPlainBody();
-            let filename = sanitizeFileName(date_format + subject + ".md");
-            // Initialize blank text since file will get updated with URL
-            let email_md = "";
-            // Add count here for emails
-            newEmails += 1;
-            Logger.log("Email number: " + newEmails + "| Saving email to: " + filename);
-            var body = "# " + subject + "\n";
-            // Cleans the reply part of emails
-            body += regexToCleanCharsMD(sanitizeBody(message.replace(/^>/g,""))) + "\n";
-            var destinationFolder = DriveApp.getFoldersByName(folderOutputName).next();
-            // Initialize blank file to retrieve URL which is then added to the frontmatter
-            var destinationFile = destinationFolder.createFile(filename, email_md , MimeType.PLAIN_TEXT);
-            // Create metadata for the object
-            var markdown_id = destinationFile.getId();
-            var md_file = DriveApp.getFileById(markdown_id);
-            let md_url = md_file.getUrl();
-            let frontmatter = "---" + "\n";
-            frontmatter += "title: \"" + sanitized_subject + "\"\n";
-            frontmatter += "type: \"" + "email" + "\"\n";
-            frontmatter += "URL: \"" + md_url + "\"\n";
-            frontmatter += "created: \"" + date + "\"\n";
-            frontmatter += "from: \"" + from_author + "\"\n";
-            frontmatter += "to: \[" + to_array + "\]\n";
-            frontmatter += "---" + "\n\n";
-            email_md = frontmatter + body;
-            var encoded = Utilities.base64Encode(email_md);
-            var byteDataArray = Utilities.base64Decode(encoded);
-            var textAsBlob = Utilities.newBlob(byteDataArray);
-            Drive.Files.update(null,markdown_id, textAsBlob);
-          }
-          let md_chip = createRichText(md_file);
-          metadata = [
-            date_format,
-            from_author,
-            sanitized_subject,
-            to,
-            markdown_id,
-            "md_chip",
-            date,
-            hash_str,
-            status,
-          ];
-          sheet.appendRow(metadata);
-          var emailTotal = newEmails + unchangedEmails;
-          let row_number = emailTotal + start_data_row;
-          insertRichText(sheet, md_chip, "F", row_number);
-        }
-      }
-    }
-    Logger.log("Saved a total of " + newEmails + " new emails.");
-    Logger.log("There is a total of " + unchangedEmails + " unchanged emails.");
-    Logger.log("Grand total of " + emailTotal + " emails.");
-  }
-}
\ No newline at end of file
diff --git a/examples/gemini/python/docs-agent/apps_script/helper_functions.gs b/examples/gemini/python/docs-agent/apps_script/helper_functions.gs
deleted file mode 100644
index 3fbf96866..000000000
--- a/examples/gemini/python/docs-agent/apps_script/helper_functions.gs
+++ /dev/null
@@ -1,214 +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.
- */
-
-// Checks to see if a folder already exists in the drive
-function folderExists(folderName) {
-  const folderIterator = DriveApp.getRootFolder().getFoldersByName(folderName);
-  if(folderIterator.hasNext()) {
-    return true;
-  }
-  else {
-    return false;
-  }
-}
-
-// 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)) {
-    Logger.log("Folder exists: "+ folderName);
-    return true;
-  }
-  else {
-    Logger.log("Folder does not exist: "+ folderName + ". Please make sure the directory exists.");
-    return false;
-  }
-}
-
-// Checks to see if folder exists or creates it. Useful for output directories
-function folderExistsOrCreate(folderName){
-  if(folderExists(folderName)) {
-    Logger.log("Folder exists: "+ folderName);
-    return true;
-  }
-  else {
-    Logger.log("Folder does not exist: "+ folderName + ". Creating the directory.");
-    DriveApp.createFolder(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);
-  if(!folder.hasNext()){
-  }
-  else{
-    var file = folder.next().getFilesByName(fileName);
-    if(!file.hasNext()){
-       return true;
-    }
-    else{
-       return false;
-    }
-  }
-}
-
-// 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, 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()) {
-    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");
-    // Checks to see if this is a sub directory
-    if (sheet.getSheetByName("Backup")) {
-      var sheet_backup = sheet.getSheetByName("Backup");
-      sheet.deleteSheet(sheet_backup);
-    }
-    var sheet_backup = sheet.insertSheet("Backup", 1);
-    var sheet_backup_open = sheet.getSheetByName("Backup");
-    sheet_index.getDataRange().copyTo(sheet_backup_open.getRange(1,1));
-    if (sheet_index != null){
-      sheet.deleteSheet(sheet_index);
-    }
-    sheet.insertSheet("Index", 0);
-    sheet_index = sheet.getSheetByName("Index");
-    sheet_index.addDeveloperMetadata("Date", date);
-    }
-  else {
-    Logger.log("File index: " + fileName + " does not exist.");
-    let output = Drive.Files.insert(file).id;
-    var sheet = SpreadsheetApp.openById(output);
-    var sheet_1 = sheet.getSheetByName("Sheet1");
-    sheet.insertSheet("Index", 0);
-    var sheet_index = sheet.getSheetByName("Index")
-    sheet_index.addDeveloperMetadata("Date", date);
-    sheet.deleteSheet(sheet_1);
-  }
-  return sheet;
-}
-
-// Function to convert byte array into a string
-function byteToStr(byteInput){
-  let signatureStr = '';
-  for (i = 0; i < byteInput.length; i++) {
-    let byte = byteInput[i];
-    if (byte < 0)
-      byte += 256;
-    let byteStr = byte.toString(16);
-    if (byteStr.length == 1) byteStr = '0' + byteStr;
-      signatureStr += byteStr;
-    }
-return signatureStr;
-}
-
-// Function to remove special characters for file names
-function sanitizeFileName(fileName){
-  let clean_filename = fileName.replace(/\[/g, "_").replace(/\]/g, "_").replace(/\(/g, "_").replace(/\)/g, "_").replace(/^_/g, "").replace(/,/g, "_").replace(/ /g, "_").replace(/:/g, "").replace(/`/g, "").replace(/\'/g, "").replace(/&/g, "and").replace(//g, "").replace(/’/g, "");
-return clean_filename;
-}
-
-// Function to remove special characters for file names
-function sanitizeString(string){
-  let clean_string = string.replace(/\[/g, "").replace(/\]/g, "").replace(/\(/g, "").replace(/\)/g, "").replace(/^_/g, "").replace(/,/g, " ").replace(/:/g, "").replace(/`/g, "").replace(/\'/g, "").replace(/&/g, "and").replace(//g, "");
-return clean_string;
-}
-
-function sanitizeBody(string){
-  let clean_body = string.replace(/’/g, "'").replace(/^M/g, "");
-return clean_body;
-}
-
-function regexToCleanCharsMD(string){
-  let clean_string = string.replace(/(\*+)(\s*\b)([^\*]+)(\b\s*)(\*+)/g, "$2$1$3$5$4");
-return clean_string;
-}
-
-// Function to check if a backup sheet exists and return a hash if the file exists
-// Specify the sheet name where the backup is saved, default is "Backup"
-// From your backup sheet specify the column that contains the MD5 hash
-// and the columns for which you return values
-function returnBackupHash(sheet, sheet_name, fid, start_data_row, pos_id, pos_1_col, pos_2_col){
-  if (sheet.getSheetByName(sheet_name)){
-    let backup_sheet = sheet.getSheetByName(sheet_name);
-    if(backup_sheet.getLastRow()> start_data_row){
-      let backup_values = backup_sheet.getDataRange().getValues();
-      for (let row_count = start_data_row; row_count < backup_sheet.getLastRow(); row_count++) {
-        let row_id = backup_values[row_count][pos_id];
-        let pos_1_value = backup_values[row_count][pos_1_col];
-        //Retrieve id of existing markdown conversion
-        let pos_2_value = backup_values[row_count][pos_2_col];
-        if (row_id == fid){
-          var results = [row_id, pos_1_value, pos_2_value];
-          break;
-        }
-        else {
-          var results = ["no_results"];
-        }
-      }
-      return results;
-    }
-  }
-}
-
-// Creates a richText item with item.
-function createRichText (item){
-  let title = item.getName();
-  let url = item.getUrl();
-  let richText = SpreadsheetApp.newRichTextValue().setText(title).setLinkUrl(url).build();
-  return richText;
-}
-
-// Insert a richText item in a specific cell
-function insertRichText (sheetItem, item, column, row){
-  let range = sheetItem.getRange(column + row);
-  range.setRichTextValue(item);
-}
diff --git a/examples/gemini/python/docs-agent/apps_script/main.gs b/examples/gemini/python/docs-agent/apps_script/main.gs
deleted file mode 100644
index 2fe88de33..000000000
--- a/examples/gemini/python/docs-agent/apps_script/main.gs
+++ /dev/null
@@ -1,27 +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.
- */
-
-// Defines the gmail search query for saving emails to markdown
-var SEARCH_QUERY = 'subject: psa to:my-mailing-list@example.com';
-// Defines the directory to output the emails in markdown format
-var folderOutput = "PSA-output"
-// Defines the directory that has your docs content
-var folderInput = "input-folder"
-
-function main() {
-  convertDriveFolderToMDForDocsAgent(folderInput);
-  exportEmailsToMarkdown(SEARCH_QUERY, folderOutput);
-}
\ No newline at end of file
diff --git a/examples/gemini/python/docs-agent/config.yaml b/examples/gemini/python/docs-agent/config.yaml
deleted file mode 100644
index 8d7fdc9df..000000000
--- a/examples/gemini/python/docs-agent/config.yaml
+++ /dev/null
@@ -1,50 +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.
-#
-
-configs:
-  - product_name: "Fuchsia"
-    models:
-      - language_model: "models/aqa"
-        embedding_model: "models/embedding-001"
-        api_endpoint: "generativelanguage.googleapis.com"
-        embedding_api_call_limit: 1400
-        embedding_api_call_period: 60
-    docs_agent_config: "normal"
-    markdown_splitter: "token_splitter"
-    log_level: "NORMAL"
-    db_type: "google_semantic_retriever"
-    db_configs:
-      - db_type: "chroma"
-        vector_db_dir: "vector_stores/chroma"
-        collection_name: "docs_collection"
-      - db_type: "google_semantic_retriever"
-        corpus_name: "corpora/fuchsia-dev"
-    output_path: "data/plain_docs"
-    inputs:
-      - path: "/usr/local/home/user01/website/src"
-        url_prefix: "https://docs.flutter.dev/"
-    conditions:
-      - condition_text: "You are a helpful chatbot answering questions from users.
-        Read the context below first and answer the user's question at the end.
-        In your answer, provide a summary in three or five sentences. (BUT DO NOT USE
-        ANY INFORMATION YOU KNOW ABOUT THE WORLD.)"
-        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.)"
-        model_error_message: "Gemini is not able to answer this question at the moment.
-        Rephrase the question and try asking again."
diff --git a/examples/gemini/python/docs-agent/docs/chunking-process.md b/examples/gemini/python/docs-agent/docs/chunking-process.md
deleted file mode 100644
index e4c054b92..000000000
--- a/examples/gemini/python/docs-agent/docs/chunking-process.md
+++ /dev/null
@@ -1,68 +0,0 @@
-# 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.
-
-
-
-[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
deleted file mode 100644
index 5967b65b9..000000000
--- a/examples/gemini/python/docs-agent/docs/cli-reference.md
+++ /dev/null
@@ -1,337 +0,0 @@
-# Docs Agent CLI reference
-
-This page provides a list of the Docs Agent command lines and their usages
-and examples.
-
-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.
-
-**Important**: All `agent` commands need to run in the `poetry shell`
-environment.
-
-## Processing documents
-
-### Chunk Markdown files into small text chunks
-
-The command below splits Markdown files (and other source files) into small
-chunks of plain text files:
-
-```sh
-agent chunk
-```
-
-### Populate a vector database using text chunks
-
-The command below populates a vector database using plain text files (created
-by running the `agent chunk` command):
-
-```sh
-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
-[`config.yaml`][config-yaml] file:
-
-```sh
-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
-```
-
-### 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
-
-The command below launches Docs Agent's Flask-based chatbot web application:
-
-```sh
-agent chatbot
-```
-
-### Launch the Docs Agent web app using a different port
-
-The command below launches the Docs Agent web app to run on port 5005:
-
-```sh
-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 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
-a log view page (which is accessible at `/logs`):
-
-```sh
-agent chatbot --enable_show_logs
-```
-
-## Running benchmark test
-
-### Run the Docs Agent benchmark test
-
-The command below runs benchmark test using the questions and answers listed
-in the [`benchmarks.yaml`][benchmarks-yaml] file:
-
-```sh
-agent benchmark
-```
-
-## Interacting with language models
-
-### Ask a question
-
-The command below reads a question from the arguments, asks the Gemini model,
-and prints its response:
-
-```sh
-agent tellme 
-```
-
-Replace `QUESTION` with a question written in plain English, for example:
-
-```sh
-agent tellme does flutter support material design 3?
-```
-
-**Note**: This `agent tellme` command is used to set up the `gemini` command
-in the [Set up Docs Agent CLI][set-up-docs-agent-cli] guide.
-
-### Ask a question to a specific product
-
-The command below enables you to ask a question to a specific product in your
-Docs Agent setup:
-
-```sh
-agent tellme  --product 
-```
-
-The example below asks the question to the `Flutter` product in your
-Docs Agent setup:
-
-```sh
-agent tellme which modules are available? --product=Flutter
-```
-
-You may also specify multiple products, for example:
-
-```sh
-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  --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
-```
-
-### 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  --file  --rag
-```
-
-### 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  --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  --cont
-```
-
-For example:
-
-```sh
-agent helpme write a concept doc that delves into more details of these features? --cont
-```
-
-### Print the context in the current session
-
-The command below prints the questions, files, and responses that
-are being used as context in the current session:
-
-```sh
-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  --perfile 
-```
-
-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  --allfiles 
-```
-
-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 
-```
-
-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
-
-The command below prints the list of all existing online corpora created
-using the [Semantic Retrieval API][semantic-api]:
-
-```sh
-agent list-corpora
-```
-
-### Share an online corpora with a user
-
-The command below enables `user01@gmail.com` to read text chunks stored in
-`corpora/example01`:
-
-```sh
-agent share-corpus --name corpora/example01 --user user01@gmail.com --role READER
-```
-
-The command below enables `user01@gmail.com` to read and write to
-`corpora/example01`:
-
-```sh
-agent share-corpus --name corpora/example01 --user user01@gmail.com --role WRITER
-```
-
-### Share an online corpora with everyone
-
-The command below enables `EVERYONE` to read text chunks stored in
-`corpora/example01`:
-
-```sh
-agent open-corpus --name corpora/example01
-```
-
-### Remove a user permission from an online corpora
-
-The command below remove an existing user permission set in `corpora/example01`:
-
-```sh
-agent remove-corpus-permission --name corpora/example01/permissions/123456789123456789
-```
-
-### Delete an online corpora
-
-The command below deletes an online corpus:
-
-```sh
-agent delete-corpus --name corpora/example01
-```
-
-
-
-[config-yaml]: ../config.yaml
-[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/concepts.md b/examples/gemini/python/docs-agent/docs/concepts.md
deleted file mode 100644
index c8cfb53ac..000000000
--- a/examples/gemini/python/docs-agent/docs/concepts.md
+++ /dev/null
@@ -1,376 +0,0 @@
-# Docs Agent concepts
-
-**Note**: If you want to set up and launch the Docs Agent chat app on your host machine,
-see the [Set up Docs Agent][set-up-docs-agent] section in README.
-
-This page describes the architecture and features of Docs Agent.
-
-## Overview
-
-The Docs Agent chat app is designed to be easily set up and configured in a Linux environment.
-and require that you have access to Google’s [Gemini API][genai-doc-site].
-
-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.
-
-![Docs Agent architecture](./images/docs-agent-architecture-01.png)
-
-**Figure 1**. Docs Agent uses a vector database to retrieve context for augmenting prompts.
-
-## Main features
-
-The key features of the Docs Agent chat app are:
-
-- Add contextual information to user questions to augment prompts for AI language models.
-- Process documents into embeddings and store them in a vector database for semnatic retrieval.
-
-![Docs Agent flow](./images/docs-agent-architecture-02.png)
-
-**Figure 2**. A user question is augmented by the Docs Agent server and passed to an LLM.
-
-For the moment, the Docs Agent project focuses on providing Python scripts that make it
-easy to process Markdown files into embeddings. However, there is no hard requirement that the
-source documents must exist in Markdown format. What’s important is that the processed content
-is available as embeddings in the vector database.
-
-### Structure of a prompt to a language model
-
-To enable an LLM to answer questions that are not part of the public knowledge (which the LLM
-is likely trained on), the Docs Agent project applies a mixture of prompt engineering and
-embeddings techniques. That is, we process a set of documents (which contain domain specific
-knowledge) into embeddings and store them in a vector database. This vector database allows
-the Docs Agent server to perform semantic search on stored embeddings to find the most relevant
-content from the source documents given user questions.
-
-Once the most relevant content is returned, the Docs Agent server uses the prompt structure
-shown in Figure 3 to augment the user question with a preset **condition** and a list of
-**context**. (When the Docs Agent server starts, the condition value is read from the
-[`config.yaml`][config-yaml] file.) Then the Docs Agent server sends this prompt to a
-language model using the Gemini API and receives a response generated by the model.
-
-![Docs Agent prompt strcture](./images/docs-agent-prompt-structure-01.png)
-
-**Figure 3**. Prompt structure for augmenting a user question with related context
-(Context source: [eventhorizontelescope.org][context-source-01])
-
-### Processing of Markdown files into embeddings
-
-To process information into embeddings using the Python scripts in the project, the
-information needs to be stored in Markdown format. Once you have a set of Markdown files
-stored in a directory on your host machine,  you can run the
-[`files_to_plain_text.py`][files-to-plain-text] script to process those Markdown
-files into small plain text files – the script splits the content by the top three Markdown
-headers (`#`, `##`, and `###`).
-
-Once Markdown files are processed into small plain text files, you can run the
-[`populate_vector_database.py`][populate-vector-database] script to generate embeddings
-for each text file and store those embeddings into a [Chroma][chroma-docs] vector database
-running on the host machine.
-
-The embeddings in this vector database enable the Docs Agent server to perform semantic search
-and retrieve context related to user questions for augmenting prompts.
-
-For more information on the processing of Markdown files, see the [`README`][scripts-readme]
-file in the `scripts` directory.
-
-![Document to embeddings](./images/docs-agent-embeddings-01.png)
-
-**Figure 4**. A document is split into small semantic chunks, which are then used to generate
-embeddings.
-
-![Markdown to embeddings](./images/docs-agent-embeddings-02.png)
-
-**Figure 5**. A Markdown page is split by headers and processed into embeddings.
-
-## Summary of tasks and features
-
-The following list summarizes the tasks and features of the Docs Agent chat app:
-
-- **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.
-- **(Experimental) “Fact-check” responses**: This experimental feature 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].
-- **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.
-
-## Flow of events
-
-The following events take place in the Docs Agent chat app:
-
-1. The [`files_to_plain_text.py`][files-to-plain-text] script converts input
-   Markdown documents into small plain text files, split by Markdown headings
-   (`#`, `##`, and `###`).
-2. The [`populate_vector_database.py`][populate-vector-database] script generates
-   embeddings from the small plain text files and populates a vector database.
-3. When the [`agent chatbot`] command is run, it starts the Docs Agent server and
-   vector database, which loads generated embeddings and metadata (URLs and filenames)
-   stored in the `vector_store` directory.
-4. When the user asks a question, the Docs Agent server uses the vector database to
-   perform semantic search on embeddings, which represent content in the source
-   documents.
-5. Using this semantic search capability, the Docs Agent server finds a list of
-   text chunks that are most relevant to the user question.
-6. The Docs Agent server adds this list of text chunks as context (plus a condition
-   for responses) to the user question and constructs them into a prompt.
-7. The system sends the prompt to a language model via the Gemini API.
-8. The language model generates a response and the Docs Agent server renders it on
-   the chat UI.
-
-Additional events for [“fact-checking” a generated response][fact-check-section]:
-
-9. The Docs Agent server prepares another prompt that compares the generated response
-   (in step 8) to the context (in step 6) and asks the language model to look for
-   a discrepancy in the response.
-10. The language model generates a response that points out one major discrepancy
-    (if it exists) between its previous response and the context.
-11. The Docs Agent server renders this response on the chat UI as a call-out note.
-12. The Docs Agent server passes this second response to the vector database to
-    perform semantic search.
-13. The vector database returns a list of relevant content (that is closely related
-    to the second response).
-14. The Docs Agent server renders the top URL of this list on the chat UI and
-    suggests that the user checks out this URL for fact-checking.
-
-Additional events for
-[suggesting 5 questions related to the user question][related-questions-section]:
-
-15. The Docs Agent server prepares another prompt that asks the language model to
-    generate 5 questions based on the context (in step 6).
-16. The language model generates a response that contains a list of questions related
-    to the context.
-17. The Docs Agent server renders the questions on the chat UI.
-
-## Supplementary features
-
-This section describes additional features implemented on the Docs Agent chat app for
-enhancing the usability of the Q&A experience powered by generative AI.
-
-![Docs Agent UI](./images/docs-agent-ui-screenshot-01.png)
-
-**Figure 6**. A screenshot of the Docs Agent chat UI showing the sections generated by
-three distinct prompts.
-
-### Using a language model to fact-check its own response
-
-In addition to using the prompt structure above (shown in Figure 3), we‘re currently
-experimenting with the following prompt setup for “fact-checking” responses generated
-by the language model:
-
-- Condition:
-
-  ```
-  You are a helpful chatbot answering questions from users. Read the following context
-  first and answer the question at the end:
-  ```
-
-- Context:
-
-  ```
-  
-  ```
-
-- Additional condition (for fact-checking):
-
-  ```
-  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 and focus on only
-  one important item.)"
-  ```
-
-- Previously generated response
-
-  ```
-  Text: 
-  ```
-
-This "fact-checking" prompt returns a response similar to the following example:
-
-```
-The text states that Flutter chose to use Dart because it is a fast, productive, object-oriented
-language that is well-suited for building user interfaces. However, the context provided in the
-prompt states that Flutter chose Dart because it is a fast, productive language that is well-suited
-for Flutter's problem domain: creating visual user experiences. Therefore, readers should consider
-fact-checking the claim that Dart is well-suited for building user interfaces.
-```
-
-After the second response, notice that the Docs Agent chat UI also suggests a URL to visit for
-fact-checking  (see Figure 6), which looks similar to the following example:
-
-```
-To verify this information, please check out:
-
-https://docs.flutter.dev/resources/faq
-```
-
-To identify this URL, the Docs Agent server takes the second response (which is the paragraph that
-begins with “The text states that ...” in the example above) and uses it to query the vector
-database. Once the vector database returns a list of the most relevant content to this response,
-the UI only displays the top URL to the user.
-
-Keep in mind that this "fact-checking" prompt setup is currently considered **experimental**
-because we‘ve seen cases where a language model would end up adding incorrect information into its
-second response as well. However, we saw that adding this second response (which brings attention
-to the language model’s possible hallucinations) seems to improve the usability of the system since it
-serves as a reminder to the users that the language model‘s response is far from being perfect, which
-helps encourage the users to take more steps to validate generated responses for themselves.
-
-### Using a language model to suggest related questions
-
-The project‘s latest web UI includes the “Related questions” section, which displays five
-questions that are related to the user question (see Figure 6). These five questions are also
-generated by a language model (via the Gemini API). Using the list of contents returned from the vector
-database as context, the system prepares another prompt asking the language model to generate five
-questions from the included context.
-
-The following is the exact structure of this prompt:
-
-- Condition:
-
-  ```
-  Read the context below and answer the question at the end:
-  ```
-
-- Context:
-
-  ```
-  
-  ```
-
-- Question:
-
-  ```
-  What are 5 questions developers might ask after reading the context?
-  ```
-
-### Enabling users to submit a rewrite of a generated response
-
-The project‘s latest web UI includes the **Rewrite this response** button at the bottom of
-the panel (see Figure 6). When this button is clicked, a widget opens up, expanding the
-main UI panel, and reveals a textarea containing the generated response to the user's question.
-The user is then allowed to edit this response in the textarea and click the **Submit** button
-to submit the updated response to the system.
-
-The system stores the submitted response as a Markdown file in the project's local `rewrites`
-directory. The user may re-click the **Submit** button to update the submitted rewrite multiple
-times.
-
-### Enabling users to like generated responses
-
-The project's latest web UI includes the **Like this response** button at the bottom of the panel
-(see Figure 6). When this button is clicked, the server logs the event of "like" for the response.
-However, clicking the **Liked** button again will reset the button. Then the server logs this reset
-event of "like" for the response.
-
-The user may click this like button multiple times to toggle the state of the like button. But when
-examining the logs, only the final state of the like button will be considered for the response.
-
-### Using Google Docs, PDF, or Gmail as input sources
-
-The project includes Apps Script files that allow you to convert various sources of content
-(including Google Docs and PDF) from your Google Drive and Gmail into Markdown files. You can then
-use these Markdown files as additional input sources for Docs Agent. For more information, see the
-[`README`][apps-script-readme] file in the `apps_script` directory.
-
-![Docs Agent pre-processing flow](./images/docs-agent-pre-processing-01.png)
-
-**Figure 7**. Docs Agent's pre-processing flow for various doc types.
-
-### Using the Semantic Retrieval API and AQA model
-
-Docs Agent provides options to use Gemini's [Semantic Retrieval API][semantic-api] for storing text
-chunks in Google Cloud's online storage (and using this online storage for context retrieval),
-in combination with using the [AQA model][aqa-model] for question-answering.
-
-To use the Semantic Retrieval API, update the `config.yaml` file to the following settings:
-
-```
-models:
-  - language_model: "models/aqa"
-
-...
-
-db_type: "google_semantic_retriever"
-```
-
-The setup above uses both the Semantic Retrieval API and the AQA model.
-
-**Note**: At the moment, when `db_type` is set to `google_semantic_retriever`, running the
-`populate_vector_database.py` script will also create and popluate a local vector database using
-Chroma as well as creating and populating an online corpus using the Semantic Retrieval API.
-
-However, if you want to use only the AQA model without using an online corpus, update the
-`config.yaml` file to the following settings instead:
-
-```
-models:
-  - language_model: "models/aqa"
-
-...
-
-db_type: "chroma"
-```
-
-The setup above uses the AQA model with your local Chroma vector database. For more information,
-see the [More Options: AQA Using Inline Passages][inline-passages] section on the
-_Semantic Retriever Quickstart_ page.
-
-**Note**: To use the Semantic Retrieval API, you need to complete the OAuth setup for your Google
-Cloud project from your host machine. For detailed instructions, see the
-[Authentication with OAuth quickstart][oauth-quickstart] page.
-
-
-
-[set-up-docs-agent]: ../README.md#set-up-docs-agent
-[files-to-plain-text]: ../docs_agent/preprocess/files_to_plain_text.py
-[populate-vector-database]: ../docs_agent/preprocess/populate_vector_database.py
-[context-source-01]: http://eventhorizontelescope.org
-[fact-check-section]: #using-a-language-model-to-fact_check-its-own-response
-[related-questions-section]: #using-a-language-model-to-suggest-related-questions
-[submit-a-rewrite]: #enabling-users-to-submit-a-rewrite-of-a-generated-response
-[like-generated-responses]: #enabling-users-to-like-generated-responses
-[populate-db-steps]: #populate-a-new-vector-database-from-markdown-files
-[genai-doc-site]: https://ai.google.dev/docs/gemini_api_overview
-[chroma-docs]: https://docs.trychroma.com/
-[apps-script-readme]: ../apps_script/README.md
-[scripts-readme]: ../docs_agent/preprocess/README.md
-[config-yaml]: ../config.yaml
-[benchmark-test]: ../docs_agent/benchmarks/README.md
-[semantic-api]: https://ai.google.dev/docs/semantic_retriever
-[aqa-model]: https://ai.google.dev/models/gemini#model_variations
-[oauth-quickstart]: https://ai.google.dev/docs/oauth_quickstart
-[inline-passages]: https://ai.google.dev/docs/semantic_retriever#more_options_aqa_using_inline_passages
-[authorize-credentials]: https://ai.google.dev/docs/oauth_quickstart#authorize-credentials
-[preprocess-dir]: ../docs_agent/preproces/
diff --git a/examples/gemini/python/docs-agent/docs/config-reference.md b/examples/gemini/python/docs-agent/docs/config-reference.md
deleted file mode 100644
index 2cdc30629..000000000
--- a/examples/gemini/python/docs-agent/docs/config-reference.md
+++ /dev/null
@@ -1,152 +0,0 @@
-# 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"
-  ```
-
-* `full`: This special mode is designed to be used with Gemini 1.5 models.
-
-  ```
-  app_mode: "full"
-  ```
-
-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 `/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"
-```
-
-
-
-[config-yaml]: ../config.yaml
diff --git a/examples/gemini/python/docs-agent/docs/images/apps-script-screenshot-01.png b/examples/gemini/python/docs-agent/docs/images/apps-script-screenshot-01.png
deleted file mode 100644
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