# RAG Agent with ChromaDB and Tavily Web Search

## Overview

This repository contains a simple RAG (Retrieval‑Augmented Generation) agent that can answer user questions by searching a local knowledge base stored in **ChromaDB** and by performing live web searches via **Tavily**.  The agent automatically selects the appropriate source and reports it in the answer.

The project uses the following stack:

- **Python 3.10+**
- **LangChain 1.x** – modern agent framework
- **ChromaDB** – local vector store
- **Ollama** – LLM (`llama3`) and embeddings (`nomic-embed-text`)
- **Tavily** – web search API
- **LangGraph** – (not used directly, but required by LangChain 1.x)

## Folder structure

```
workspace/
├─ documents/          # .txt/.md files that will be loaded into Chroma
├─ chroma_db/          # persistent Chroma data (created on first run)
├─ main.py             # CLI entry point
├─ vectorstore.py      # Chroma store helpers
├─ tools.py            # Agent tools
├─ README.md
└─ requirements.txt
```

## Setup

1. **Install Ollama** and pull the required models:
   ```bash
   ollama pull llama3
   ollama pull nomic-embed-text
   ```

2. **Set the Tavily API key** (obtain a free key from https://tavily.com):
   ```bash
   export TAVILY_API_KEY=your_api_key_here
   ```
   On Windows use `set` instead of `export`.

3. **Install Python dependencies**:
   ```bash
   pip install -r requirements.txt
   ```

4. **Add documents** you want the agent to know about into the `documents/` folder.  Any `.txt` or `.md` files will be automatically loaded.

## Running the Agent

```bash
python main.py
```

You will see a prompt where you can type questions.  Type `exit`, `quit`, or `q` to end the session.

Example interaction:

```
User: What are the latest developments in AI agents?
Assistant: [Web Search] - ...
Source: tavily

User: What does our lab say about LangGraph?
Assistant: [Local KB] - ...
Source: chromadb
```

## How It Works

1. **Vector Store** – `vectorstore.py` creates a persistent Chroma collection using `OllamaEmbeddings`.  Documents from `documents/` are split with `RecursiveCharacterTextSplitter` and added to the store.
2. **Tools** – `tools.py` defines two tools:
   * `search_local_kb` – semantic search in the local vector store.
   * `web_search` – live web search via Tavily.
3. **Agent** – In `main.py` we create a `ChatOllama` LLM and pass the two tools to `create_agent`.  A system prompt instructs the LLM to choose the correct tool.  The agent returns the answer along with a source tag.

## Extending

- Add more documents to `documents/` and restart the CLI – the store will be updated automatically.
- Replace the LLM or embedding model by changing the `ChatOllama` and `OllamaEmbeddings` parameters.
- Add additional tools (e.g., file system access, calculator) following the same pattern.

## License

MIT License.