# RAG Agent with ChromaDB and Tavily

This repository contains a lightweight RAG (Retrieval‑Augmented Generation) agent that:

1. Stores local knowledge in **ChromaDB** using **Ollama** embeddings.
2. Performs semantic search over the local store.
3. Falls back to **Tavily** web search for up‑to‑date information.
4. Decides automatically which source to use and indicates the source in the answer.

## Prerequisites

* Python 3.10+ (recommended via `pyenv` or `conda`).
* [Ollama](https://ollama.ai/) installed locally.
* A Tavily API key – set it in a `.env` file.

```bash
# Pull the required models
ollama pull llama3
ollama pull nomic-embed-text
```

## Installation

```bash
pip install -r requirements.txt
```

## Usage

```bash
# Create a .env file with your Tavily key
# TAVILY_API_KEY=YOUR_KEY

# Populate the vector store from the documents folder
python main.py
```

You will be presented with a prompt.  Type your question and press **Enter**.
Type `exit` to quit.

## Project Structure

```
├── agent.py          # Agent definition
├── main.py           # CLI entry point
├── tools.py          # Local KB and web search tools
├── vectorstore.py    # ChromaDB helpers
├── requirements.txt
├── README.md
└── documents/        # Folder with .txt/.md files to ingest
```

## How It Works

1. **Vector Store** – `vectorstore.py` creates a ChromaDB instance backed by
   `OllamaEmbeddings`.  Documents from `documents/` are chunked with
   `RecursiveCharacterTextSplitter` and added to the store.

2. **Tools** – `tools.py` exposes two LangChain tools:
   * `search_local_kb` – semantic search in ChromaDB.
   * `web_search` – web search via Tavily.

3. **Agent** – `agent.py` builds an OpenAI‑functions‑style agent that
   chooses between the two tools based on the user’s query.  The system prompt
   instructs the LLM to use `search_local_kb` for knowledge‑base queries and
   `web_search` for recent facts.  The answer always contains a source tag.

4. **CLI** – `main.py` ties everything together: it loads the vector store,
   creates the agent and runs an interactive chat loop.

## Extending

* Replace the LLM with any other LangChain‑compatible model.
* Add more tools (e.g., database queries, file system access).
* Persist the vector store across runs – it already does this via `persist_directory`.

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Happy experimenting!