RAG Chat API Starter Kit

A RAG (Retrieval Augmented Generation) system that uses Google's Gemini models to provide contextual responses based on your documentation.

Overview

RAG Chat API enables you to create a chatbot that answers questions based on your technical documentation. It uses:

• Google Gemini models for advanced embeddings and text generation
• ChromaDB for vector storage and similarity search
• LangChain for document loading, chunking, and prompt management

This system creates embeddings of your documentation, stores them in a vector database, and then retrieves relevant context when answering user queries.

Features

• ✅ Load and process documents (.txt, .md, .pdf (expected to be introduced in v2.0.0))
• ✅ Split documents into manageable chunks with overlap
• ✅ Generate and store embeddings with Google's text-embedding-004 model
• ✅ Semantic search for relevant document chunks
• ✅ Contextual answers using Gemini 1.5 Pro
• ✅ Customizable chunk size and overlap
• ✅ Organized response storage

Installation

1. Clone this repository:

   git clone <repository-url>
   cd devdocs-chat-api

2. Create and activate a virtual environment:

   python -m venv .chat-env
   source .chat-env/bin/activate  # On Windows: .chat-env\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Create a .env file with your configuration:

   CHROMA_PATH="db/chroma"
   GOOGLE_API_KEY="your-google-api-key-here"
   DATA_STORE_PATH="db/knowledge_base"
   

Usage

1. Add your documentation

Place your documentation files in the directory specified by DATA_STORE_PATH (default: knowledge_base):

mkdir -p db/knowledge_base
cp your-docs/*.md db/knowledge_base/

2. Generate embeddings

Run the embeddings script to process your documentation and create vector embeddings:

python embeddings.py

This will:

• Load all documents from your knowledge base directory
• Split them into chunks of text
• Generate embeddings using Google's text-embedding-004 model
• Store the embeddings in ChromaDB

3. Query your documentation

Run the main script to interact with your documentation:

python main.py

By default, this will:

• Ensure your knowledge base and ChromaDB directories exist
• Check if there are documents to process
• Generate embeddings if needed
• Run a sample query ("Why do we need to use embeddings?")
• Find relevant document chunks based on the query
• Generate a contextual response using Gemini 1.5 Pro
• Save the response to the responses directory

Customization

You can customize the behavior by modifying:

• Query: Change the example query in main.py
• Chunking parameters: Adjust CHUNK_SIZE and CHUNK_OVERLAP in embeddings.py
• Model: Change the embedding or LLM model in the respective files
• Prompt template: Modify the PROMPT_TEMPLATE in main.py

Project Structure

• main.py: Main script for querying the documentation
• embeddings.py: Script for generating and storing embeddings
• .env: Environment variables
• db: Directory for databases
  • chroma/: ChromaDB vector database
  • knowledge_base/: Your documentation files
• responses: Generated responses

Requirements

• Python 3.9+
• Google API key with access to Gemini models
• Dependencies listed in requirements.txt

Troubleshooting

• Empty ChromaDB: Make sure you have documents in your knowledge base directory and run embeddings.py
• No responses: Check that your Google API key is valid and has access to the required models
• Missing directories: The system will create required directories automatically

License

[Your License Here]

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Contributing

Contributions are welcome! Please fork the repository and submit a pull request with your changes. Make sure to follow the code style and include tests for new features.