Langgraph RAG Agent with Ollama and Llama3.2

This project implements a Retrieval-Augmented Generation (RAG) agent using Langgraph, Ollama, and Llama3.2. The agent can perform document retrieval, web searches, and generate answers based on the retrieved information.

Prerequisites

• Python 3.8+
• Ollama (for running Llama3.2 locally)
• Pinecone (for vector storage and retrieval)
• Tavily API key (for web search functionality)

Installation

1. Clone this repository:

   git clone https://github.com/yourusername/langgraph_rag_agent.git
   cd langgraph_rag_agent
   

2. Create a virtual environment and activate it:

   python -m venv .venv
   source .venv/bin/activate # On Windows, use `.venv\Scripts\activate`
   

3. Install the required packages:

   pip install -r requirements.txt
   

4. Install Ollama:

   curl -fsSL https://ollama.com/install.sh | sh
   

5. Pull the Llama3.2 model:

   ollama pull llama3.2:3b
   

Configuration

1. Create a .env file in the project root directory.

2. Set the following environment variables in the .env file:

   • PINECONE_API_KEY: Your Pinecone API key
   • OLLAMA_MODEL: llama3.2:3b
   • PINECONE_INDEX_NAME: Name of your Pinecone index
   • COHERE_API_KEY: Your Cohere API key for embeddings
   • TAVILY_API_KEY: Your Tavily API key for web search functionality
   • EMBEDDING_MODEL: embed-multilingual-v3.0
   • RETRIEVER_K: set the amount of docs u want to retrieve per query
   • WEB_SEARCH_K: set the amount of web searches for one query

Usage

To run the Langgraph RAG agent, execute the following command:

python main.py 

The agent will prompt you to enter a question and a namespace for the Pinecone index.

Project Structure

• main.py: Entry point of the application
• agent.py: Implements the Langgraph agent logic
• utils/:
  • config.py: Configuration management
  • llm.py: Ollama LLM integration
  • retriever.py: Document retrieval using Pinecone
  • tools.py: Web search tool implementation
  • state.py: State management for Langgraph

Key Components

• Retriever: Uses Pinecone for vector storage and retrieval, with Cohere embeddings.
• Web Search: Utilizes Tavily for web search functionality.
• LLM: Integrates Ollama to run Llama3.2 locally for answer generation.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License.