# RAG‑Agent with ChromaDB and Web Search

This repository implements a simple RAG (Retrieval‑Augmented Generation) agent that can answer questions using a local knowledge base stored in **ChromaDB** or by searching the web via **Tavily**.  The agent automatically chooses the appropriate source and reports it in the answer.

## Features

* **Local semantic search** – Uses a ChromaDB vector store backed by Ollama embeddings.
* **Web search** – Uses Tavily to fetch up‑to‑date information.
* **Automatic source selection** – The agent decides whether to query the local KB or the web.
* **Persisted vector store** – Data is stored on disk and reused across runs.
* **Simple CLI** – Chat loop with `exit` to quit.

## Setup

```bash
# 1. Clone the repo
git clone <repo-url>
cd <repo-dir>

# 2. (Optional) Create a virtual environment
python -m venv venv
source venv/bin/activate   # Windows: venv\Scripts\activate

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

# 4. Pull required Ollama models
ollama pull llama3
ollama pull nomic-embed-text

# 5. Set Tavily API key
export TAVILY_API_KEY=your_api_key   # Windows: set TAVILY_API_KEY=your_api_key

# 6. Prepare documents
# Place any .txt or .md files you want to index in the ./documents folder.
# They will be automatically loaded into ChromaDB on first run.

# 7. Run the agent
python main.py
```

## Usage

```text
Запрос: Какие последние новости про AI-агентов?
[Web Search]
1. AI Agents: The Future of Automation: ...
2. ...
Source: tavily

Запрос: Что в наших конспектах про LangGraph?
[Local KB]
1. LangGraph is a ...
2. ...
Source: chromadb
```

## Project Structure

```
├── agent.py          # Core agent logic and tools
├── vectorstore.py    # ChromaDB creation and document loading
├── rag_tools.py      # Web search tool
├── main.py           # CLI entry point
├── requirements.txt
└── README.md
```

## Extending

* **Add more tools** – Define new functions decorated with `@tool` and add them to the `tools` list.
* **Change LLM** – Swap `ChatOllama` for another provider (e.g., OpenAI) by adjusting the import and model name.
* **Custom prompt** – Edit `agent_prompt` in `agent.py` to modify the agent’s instruction.

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Happy querying!