# RAG Agent with ChromaDB & Tavily

This project implements a Retrieval-Augmented Generation (RAG) agent that uses **ChromaDB** for vector storage and **Tavily** for web search.  
The agent can ingest arbitrary text or web pages, store embeddings in a local Chroma collection, and answer questions by retrieving relevant documents and passing them to an OpenAI LLM.

> **Important**  
> The original repository used Qdrant. All references to Qdrant have been removed.  
> Only ChromaDB and Tavily are used.

## Prerequisites

| Component | Version | Notes |
|-----------|---------|-------|
| Python | 3.9+ | Tested on 3.10 |
| OpenAI API | Any key | Required for embeddings and LLM |
| Tavily API | Any key | Required for web search |

Set the following environment variables before running:

```bash
export OPENAI_API_KEY="your-openai-key"
export TAVILY_API_KEY="your-tavily-key"
```

## Installation

```bash
# Clone the repository
git clone https://git.brojs.ru/kuzakhmetovartur/ekzamen-rag-agent-s-chromadb-i-veb-poisk.git
cd ekzamen-rag-agent-s-chromadb-i-veb-poisk

# Create a virtual environment (optional but recommended)
python -m venv .venv
source .venv/bin/activate   # On Windows: .venv\Scripts\activate

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

`requirements.txt` contains:

```
chromadb>=0.4
tavily>=0.1
langchain>=0.0.350
openai>=1.0
```

> **Note**: The exact versions may vary; the above are the minimal compatible versions.

## Usage

The agent is a single script `src/index.py`. It supports two commands:

### 1. Ingest

```bash
python src/index.py ingest <url_or_text>
```

- If `<url_or_text>` starts with `http://` or `https://`, the script treats it as a URL, fetches the content via Tavily, and stores it.
- Otherwise, it treats the argument as raw text and stores it directly.

Example:

```bash
python src/index.py ingest https://en.wikipedia.org/wiki/OpenAI
```

### 2. Query

```bash
python src/index.py query "<your question>"
```

The script retrieves relevant documents from the Chroma collection and asks OpenAI to generate an answer.

Example:

```bash
python src/index.py query "What is OpenAI?"
```

## Project Structure

```
.
├── src
│   └── index.py          # Main script
├── README.md
└── requirements.txt
```

## How It Works

1. **Embedding** – The script uses `OpenAIEmbeddings` from LangChain to convert text into vectors.
2. **Vector Store** – `Chroma` stores these vectors locally in `~/.rag_agent/chromadb`.
3. **Retrieval** – When a query is made, the nearest vectors are fetched.
4. **Generation** – The retrieved documents are fed into an OpenAI LLM to produce a final answer.

## Troubleshooting

- **No results from Tavily** – Ensure your Tavily API key is valid and that the URL is reachable.
- **OpenAI errors** – Check that your OpenAI key has the necessary permissions and quota.
- **Chroma storage issues** – The data directory is `~/.rag_agent/chromadb`. Delete it to reset the collection.

## License

This project is released under the MIT License.