RAG Chatbot

This directory contains the RAG (Retrieval-Augmented Generation) Chatbot example for the Atomic Agents project. This example demonstrates how to build an intelligent chatbot that uses document retrieval to provide context-aware responses using the Atomic Agents framework.

Features

1. Document Chunking: Automatically splits documents into manageable chunks with configurable overlap
2. Vector Storage: Supports both ChromaDB and Qdrant for efficient storage and retrieval of document chunks
3. Semantic Search: Generates and executes semantic search queries to find relevant context
4. Context-Aware Responses: Provides detailed answers based on retrieved document chunks
5. Interactive UI: Rich console interface with progress indicators and formatted output

Getting Started

To get started with the RAG Chatbot:

1. Clone the main Atomic Agents repository:

   git clone https://github.com/eigenwise/atomic-agents
   

2. Navigate to the RAG Chatbot directory:

   cd atomic-agents/atomic-examples/rag-chatbot
   

3. Install the dependencies using uv:

   uv sync
   

4. Set up environment variables: Create a .env file in the rag-chatbot directory with the following content:

   OPENAI_API_KEY=your_openai_api_key
   VECTOR_DB_TYPE=chroma  # or 'qdrant'
   

   Replace your_openai_api_key with your actual OpenAI API key.

5. Run the RAG Chatbot:

   uv run python rag_chatbot/main.py
   

Vector Database Configuration

The RAG Chatbot supports two vector databases:

ChromaDB (Default)

• Local storage: Data is stored locally in the chroma_db/ directory
• Configuration: Set VECTOR_DB_TYPE=chroma in your .env file

Qdrant

• Local storage: Data is stored locally in the qdrant_db/ directory
• Configuration: Set VECTOR_DB_TYPE=qdrant in your .env file

Usage

Using ChromaDB (Default)

export VECTOR_DB_TYPE=chroma
uv run python rag_chatbot/main.py

Using Qdrant (Local)

export VECTOR_DB_TYPE=qdrant
uv run python rag_chatbot/main.py

Components

1. Query Agent (agents/query_agent.py)

Generates semantic search queries based on user questions to find relevant document chunks.

2. QA Agent (agents/qa_agent.py)

Analyzes retrieved chunks and generates comprehensive answers to user questions.

3. Vector Database Services (services/)

• Base Service (services/base.py): Abstract interface for vector database operations
• ChromaDB Service (services/chroma_db.py): ChromaDB implementation
• Qdrant Service (services/qdrant_db.py): Qdrant implementation
• Factory (services/factory.py): Creates the appropriate service based on configuration

4. Context Provider (context_providers.py)

Provides retrieved document chunks as context to the agents.

5. Main Script (main.py)

Orchestrates the entire process, from document processing to user interaction.

How It Works

1. The system initializes by:

   • Downloading a sample document (State of the Union address)
   • Splitting it into chunks with configurable overlap
   • Storing chunks in the selected vector database with vector embeddings

2. For each user question:

   • The Query Agent generates an optimized semantic search query
   • Relevant chunks are retrieved from the vector database
   • The QA Agent analyzes the chunks and generates a detailed answer
   • The system displays the thought process and final answer

Customization

You can customize the RAG Chatbot by:

• Modifying chunk size and overlap in config.py
• Adjusting the number of chunks to retrieve for each query
• Using different documents as the knowledge base
• Customizing the system prompts for both agents
• Switching between ChromaDB and Qdrant by changing the VECTOR_DB_TYPE environment variable

Example Usage

The chatbot can answer questions about the loaded document, such as:

• "What were the main points about the economy?"
• "What did the president say about healthcare?"
• "How did he address foreign policy?"

Contributing

Contributions are welcome! Please fork the repository and submit a pull request with your enhancements or bug fixes.

License

This project is licensed under the MIT License. See the LICENSE file for details.