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README.md

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+# 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
+
+```bash
+# 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
+
+```bash
+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