RAG Agent

Overview

This project implements a Retrieval‑Augmented Generation (RAG) agent that can search a local knowledge base stored in ChromaDB and the web via Tavily. The agent automatically chooses the appropriate source and indicates it in the response.

Features

Local vector store with Ollama embeddings (nomic-embed-text)
Web search powered by Tavily
Two tools: Local KB Search and Web Search
Automatic source selection
Persistent vector store between runs
CLI chat loop with exit command

Installation

# Pull required models
ollama pull llama3
ollama pull nomic-embed-text

# Install Python dependencies
pip install langchain langchain-chroma langchain-tavily langchain-ollama tavily-python chromadb python-dotenv

Setup

Create a .env file in the project root with your Tavily API key:

TAVILY_API_KEY=your_api_key_here
CHAT_BASE_URL=http://localhost:11434/v1
CHAT_API_KEY=ollama
CHAT_MODEL=llama3

Usage

python main.py --docs_dir path/to/documents

--docs_dir (optional) – Directory containing .txt or .md files to index into the vector store. If omitted, the agent will use the existing persisted store.

Example

You: What are the latest news about AI agents?
Agent: 1. AI Agents in 2024 (https://example.com) ...
Source: tavily

You: Tell me about LangGraph in my notes.
Agent: LangGraph is a framework for building ...
Source: chromadb

Exiting

Type exit or quit to exit the chat loop.

License

MIT

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