refactor: configs

ReadMe.md

@@ -1,6 +1,6 @@
 # Nous: Your Personal Knowledge Assistant 🧠💬
 
-Nous is an open-source personal knowledge assistant that allows you to interact with your personal documents and text files using natural language. It's designed with a focus on privacy, security, and local processing. 
+Nous is an open-source personal knowledge assistant that allows you to interact with your personal documents and text files using natural language. It's designed with a focus on privacy, security, and local processing.
 
 ## Demo 🎥
 
@@ -179,6 +179,124 @@ URLS = [
 
 After adding your links, restart the Flask app to ingest the new documents into the vector database.
 
+## 🔧 Configuration
+
+Nous uses two main configuration files: one for the Golang HTTP server and another for the Python Flask LLM service.
+
+### Golang HTTP Server Configuration
+
+Located at: `app/internal/config/config.go`
+
+The Golang server configuration manages various aspects of the web server, including paths, addresses, and connections.
+
+#### Configuration Structure
+
+```go
+type Config struct {
+    StaticPath    string
+    TemplatesPath string
+    ServerAddr    string
+    DatabasePath  string
+    LLMBaseURL    string
+    RedisAddr     string
+}
+```
+
+#### Configuration Options
+
+- `StaticPath`: Path to static files (default: `"../static"` relative to the executable)
+- `TemplatesPath`: Path to HTML templates (default: `"../templates/*"` relative to the executable)
+- `ServerAddr`: Address and port for the HTTP server (default: `:8080`)
+- `DatabasePath`: Path to the SQLite database file (default: `"./nous.db"`)
+- `LLMBaseURL`: URL of the LLM service (default: `"http://localhost:5000"`)
+- `RedisAddr`: Address of the Redis server (default: `"localhost:6379"`)
+
+#### Customizing the Configuration
+
+You can customize these settings using environment variables:
+
+1. `STATIC_PATH`: Set the path to static files
+2. `TEMPLATES_PATH`: Set the path to HTML templates
+3. `SERVER_ADDR`: Set the server address and port
+4. `DATABASE_PATH`: Set the path to the SQLite database file
+5. `LLM_BASE_URL`: Set the URL of the LLM service
+6. `REDIS_ADDR`: Set the address of the Redis server
+
+Example:
+```bash
+export SERVER_ADDR=:8081
+```
+
+### LLM Service Configuration
+
+Located at: `llm_api/config.py`
+
+The LLM service configuration controls the behavior of the Flask app that manages LLM interactions.
+
+#### Configuration Options
+
+1. **API Keys**
+   ```python
+   TAVILY_API_KEY = os.getenv("TAVILY_API_KEY")
+   ```
+   - Used for web search capabilities. Set this in your `.env` file.
+
+2. **Vectorstore Paths**
+   ```python
+   DATA_INDEX_PATH = os.getenv('DATA_INDEX_PATH', "data/faiss_index.bin")
+   DATA_VECTOR_PATH = os.getenv('DATA_VECTOR_PATH', "data/vectors.npy")
+   DATA_INDEX_TO_ID_PATH = os.getenv('DATA_INDEX_TO_ID_PATH', "data/index_to_id.npy")
+   ```
+   - Determine where vectorstore data is saved and loaded from.
+
+3. **Ollama Configuration**
+   ```python
+   class OllamaConfig:
+       HOST = os.getenv('OLLAMA_HOST', 'http://localhost:11434')
+       EMBEDDINGS_MODEL = os.getenv('OLLAMA_EMBEDDINGS_MODEL', "nomic-embed-text")
+       MODEL_RAG = os.getenv('OLLAMA_MODEL_RAG', "phi3:mini")
+       MODEL_RETRIEVER_GRADER = os.getenv('OLLAMA_MODEL_RETRIEVER_GRADER', "thinyllama:mini")
+   ```
+   - `HOST`: URL where Ollama is running
+   - `EMBEDDINGS_MODEL`: Model for generating embeddings
+   - `MODEL_RAG`: Model for Retrieval-Augmented Generation
+   - `MODEL_RETRIEVER_GRADER`: Model for grading retrieval results
+
+4. **Personal Knowledge Base Resources**
+   ```python
+   KNOWLEDGE_BASE_URLS = [
+       "https://tip.golang.org/tour/concurrency.article",
+       "https://tip.golang.org/doc/effective_go",
+       "https://gosafir.com/mag/wp-content/uploads/2019/12/Tolkien-J.-The-lord-of-the-rings-HarperCollins-ebooks-2010.pdf",
+       "https://gist.github.com/silver-xu/1dcceaa14c4f0253d9637d4811948437",
+   ]
+   ```
+   - List of URLs for documents to be ingested into the knowledge base.
+
+#### Customizing the Configuration
+
+To modify LLM service settings:
+
+1. Open `llm_api/config.py` in a text editor.
+2. Adjust values as needed.
+3. For environment variables, either:
+   - Set them in your system environment, or
+   - Create a `.env` file in the `llm_api` directory with the required key-value pairs.
+4. Save the file and restart the LLM service.
+
+### Applying Configuration Changes
+
+After modifying any configuration:
+
+1. For the Golang server, rebuild and restart the server.
+2. For the LLM service, restart the Flask app.
+3. If using Docker, rebuild the containers:
+   ```bash
+   docker-compose up --build
+   ```
+
+This ensures that all configuration changes are properly incorporated into the running services.
+
 ## Docker Compose Configuration
 
 The `docker-compose.yml` file in the root directory contains the following services:

app/internal/config/config.go

@@ -5,35 +5,47 @@ import (
 	"path/filepath"
 )
 
+// Config holds all configuration for the application
 type Config struct {
-	StaticPath    string
-	TemplatesPath string
-	ServerAddr    string
-	DatabasePath  string
-	LLMBaseURL    string
-	RedisAddr     string
+    StaticPath    string
+    TemplatesPath string
+    ServerAddr    string
+    DatabasePath  string
+    LLMBaseURL    string
+    RedisAddr     string
 }
 
+// Load returns a new Config struct populated with values from environment variables or defaults
 func Load() (*Config, error) {
-	ex, err := os.Executable()
-	if err != nil {
-		return nil, err
-	}
-	exPath := filepath.Dir(ex)
+    exPath, err := getExecutablePath()
+    if err != nil {
+        return nil, err
+    }
 
-	return &Config{
-		StaticPath:    getEnv("STATIC_PATH", filepath.Join(exPath, "..", "static")),
-		TemplatesPath: getEnv("TEMPLATES_PATH", filepath.Join(exPath, "..", "templates", "*")),
-		ServerAddr:    getEnv("SERVER_ADDR", ":8080"),
-		DatabasePath:  getEnv("DATABASE_PATH", "./nous.db"),
-		LLMBaseURL:    getEnv("LLM_BASE_URL", "http://localhost:5000"),
-		RedisAddr:     getEnv("REDIS_ADDR", "localhost:6379"),
-	}, nil
+    return &Config{
+        StaticPath:    getEnv("STATIC_PATH", filepath.Join(exPath, "..", "static")),
+        TemplatesPath: getEnv("TEMPLATES_PATH", filepath.Join(exPath, "..", "templates", "*")),
+        ServerAddr:    getEnv("SERVER_ADDR", ":8080"),
+        DatabasePath:  getEnv("DATABASE_PATH", "./nous.db"),
+        LLMBaseURL:    getEnv("LLM_BASE_URL", "http://localhost:5000"),
+        RedisAddr:     getEnv("REDIS_ADDR", "localhost:6379"),
+    }, nil
 }
 
+// getExecutablePath returns the directory of the current executable
+func getExecutablePath() (string, error) {
+    ex, err := os.Executable()
+    if err != nil {
+        return "", err
+    }
+    return filepath.Dir(ex), nil
+}
+
+// getEnv retrieves the value of the environment variable named by the key
+// If the variable is not present, it returns the fallback value
 func getEnv(key, fallback string) string {
-	if value, exists := os.LookupEnv(key); exists {
-		return value
-	}
-	return fallback
+    if value, exists := os.LookupEnv(key); exists {
+        return value
+    }
+    return fallback
 }

llm_api/config.py

@@ -1,14 +1,37 @@
 import os
 from dotenv import load_dotenv
 
+# Load environment variables
 load_dotenv()
 
+# API Keys
 TAVILY_API_KEY = os.getenv("TAVILY_API_KEY")
 
-URLS = [
+# Vectorstore paths
+DATA_INDEX_PATH = os.getenv('DATA_INDEX_PATH', "data/faiss_index.bin")
+DATA_VECTOR_PATH = os.getenv('DATA_VECTOR_PATH', "data/vectors.npy")
+DATA_INDEX_TO_ID_PATH = os.getenv('DATA_INDEX_TO_ID_PATH', "data/index_to_id.npy")
+
+# Ollama configuration
+class OllamaConfig:
+    HOST = os.getenv('OLLAMA_HOST', 'http://localhost:11434')
+    EMBEDDINGS_MODEL = os.getenv('OLLAMA_EMBEDDINGS_MODEL', "nomic-embed-text")
+    MODEL_RAG = os.getenv('OLLAMA_MODEL_RAG', "phi3:mini")
+    MODEL_RETRIEVER_GRADER = os.getenv('OLLAMA_MODEL_RETRIEVER_GRADER', "thinyllama:mini")
+
+# Personal Knowledge base resources
+KNOWLEDGE_BASE_URLS = [
     "https://tip.golang.org/tour/concurrency.article",
     "https://tip.golang.org/doc/effective_go",
-    # "https://socrates.acadiau.ca/courses/engl/rcunningham/resources/Shpe/Hamlet.pdf",
-    "https://gosafir.com/mag/wp-content/uploads/2019/12/Tolkien-J.-The-lord-of-the-rings-HarperCollins-ebooks-2010.pdf"
+    "https://gosafir.com/mag/wp-content/uploads/2019/12/Tolkien-J.-The-lord-of-the-rings-HarperCollins-ebooks-2010.pdf",
     "https://gist.github.com/silver-xu/1dcceaa14c4f0253d9637d4811948437",
 ]
+
+# Ensure all required environment variables are set
+def validate_env_vars():
+    required_vars = ["TAVILY_API_KEY"]
+    for var in required_vars:
+        if not os.getenv(var):
+            raise EnvironmentError(f"Missing required environment variable: {var}")
+
+validate_env_vars()

llm_api/embeddings.py

@@ -2,26 +2,26 @@
 from langchain_community.embeddings import OllamaEmbeddings
 from langchain_community.docstore.in_memory import InMemoryDocstore
 import faiss
+from llm_api.config import DATA_INDEX_PATH, DATA_INDEX_TO_ID_PATH, DATA_VECTOR_PATH, OllamaConfig
 import numpy as np
 import os
 from typing import TypedDict, List
 
 def create_vectorstore(documents: List[str], force_rebuild: bool = False) -> FAISS:
-    index_path = "data/faiss_index.bin"
-    vectors_path = "data/vectors.npy"
-    index_to_id_path = "data/index_to_id.npy"
-    
+    index_path = DATA_INDEX_PATH
+    vectors_path = DATA_VECTOR_PATH
+    index_to_id_path = DATA_INDEX_TO_ID_PATH
+
     if not force_rebuild and os.path.exists(index_path) and os.path.exists(vectors_path) and os.path.exists(index_to_id_path):
         # Load existing index and vectors
         index = faiss.read_index(index_path)
         docstore_dict = np.load(vectors_path, allow_pickle=True).item()
         index_to_id = np.load(index_to_id_path, allow_pickle=True).item()
-        
+
         # Create InMemoryDocstore from the loaded dictionary
         docstore = InMemoryDocstore(docstore_dict)
-
-        ollama_host = os.getenv('OLLAMA_HOST', 'http://localhost:11434')
-        ollama_embeddings = OllamaEmbeddings(model="nomic-embed-text", base_url=ollama_host)
+
+        ollama_embeddings = OllamaEmbeddings(model=OllamaConfig.EMBEDDINGS_MODEL, base_url=OllamaConfig.HOST)
         vectorstore = FAISS(
             embedding_function=ollama_embeddings,
             index=index,
@@ -31,14 +31,13 @@ def create_vectorstore(documents: List[str], force_rebuild: bool = False) -> FAI
         print("Loaded existing vectorstore from disk.")
     else:
         # Create new vectorstore
-        ollama_host = os.getenv('OLLAMA_HOST', 'http://localhost:11434')
-        ollama_embeddings = OllamaEmbeddings(model="nomic-embed-text", base_url=ollama_host)
+        ollama_embeddings = OllamaEmbeddings(model=OllamaConfig.EMBEDDINGS_MODEL, base_url=OllamaConfig.HOST)
         vectorstore = FAISS.from_documents(documents, ollama_embeddings)
-        
+
         # Save the index, vectors, and index_to_docstore_id separately
         faiss.write_index(vectorstore.index, index_path)
         np.save(vectors_path, vectorstore.docstore._dict)
         np.save(index_to_id_path, vectorstore.index_to_docstore_id)
         print("Created new vectorstore and saved to disk.")
-    
-    return vectorstore
+
+    return vectorstore

llm_api/graph.py

@@ -7,7 +7,7 @@
 import logging
 import concurrent.futures
 
-from config import TAVILY_API_KEY, URLS
+from config import OllamaConfig, TAVILY_API_KEY, KNOWLEDGE_BASE_URLS
 from document_processor import load_and_split_documents
 from embeddings import create_vectorstore
 from retriever import create_retriever
@@ -21,13 +21,13 @@
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-doc_splits = load_and_split_documents(URLS)
+doc_splits = load_and_split_documents(KNOWLEDGE_BASE_URLS)
 vectorstore = create_vectorstore(doc_splits, force_rebuild=False)
 retriever = create_retriever(vectorstore)
 
-llm = create_llm(model="phi3:mini")
+llm = create_llm(model=OllamaConfig.MODEL_RAG)
 rag_chain = rag_prompt | llm | StrOutputParser()
-retrieval_grader = grading_prompt | create_llm(model="tinyllama", format="json") | JsonOutputParser()
+retrieval_grader = grading_prompt | create_llm(model=OllamaConfig.MODEL_RETRIEVER_GRADER, format="json") | JsonOutputParser()
 
 web_search_tool = TavilySearchResults(api_key=TAVILY_API_KEY)
 

llm_api/llm.py

@@ -2,12 +2,13 @@
 from langchain_ollama import ChatOllama
 from functools import lru_cache
 
+from llm_api.config import OLLAMA_HOST
+
 @lru_cache(maxsize=2)
 def create_llm(model="llama3.1", temperature=0, format=''):
-    ollama_host = os.getenv('OLLAMA_HOST', 'http://localhost:11434')
     return ChatOllama(
-        model=model, 
-        temperature=temperature, 
+        model=model,
+        temperature=temperature,
         format=format,
-        base_url=ollama_host
-    )
+        base_url=OLLAMA_HOST
+    )