RAG Agent with ChromaDB and Tavily

This repository contains a simple RAG agent that uses ChromaDB as a local vector store and Tavily for web search. The agent can decide whether to answer from the local knowledge base or fetch fresh information from the internet.

Project structure

• vectorstore.py – utilities for creating/loading ChromaDB and adding documents.
• tools.py – two LangChain tools: local KB search and Tavily web search.
• agent.py – agent creation with routing logic.
• main.py – CLI chat loop.
• documents/ – folder with sample .txt or .md files for the knowledge base.
• requirements.txt – dependencies (LangChain >1.0, chromadb, tavily-python, etc.).

Usage

# 1. Install dependencies
pip install -r requirements.txt

# 2. Set Tavily API key
export TAVILY_API_KEY=your_key_here

# 3. Load documents into ChromaDB (once)
python load_docs.py

# 4. Run the chat interface
python main.py