# RAG Agent with Ollama Embeddings and Qdrant

This project implements a Retrieval-Augmented Generation (RAG) agent that uses:

- **OllamaEmbeddings** from `langchain-community` for local embeddings.
- **Qdrant** as the vector store for efficient similarity search.
- **OpenAI LLM** for generating responses.

## Prerequisites

- Python 3.10+
- A running local Ollama instance (default: `http://localhost:11434`).
- A running local Qdrant instance (default: `http://localhost:6333`).
- An OpenAI API key for the LLM.

## Setup

```bash
# Clone the repository
git clone https://git.brojs.ru/kuzakhmetovartur/agent-s-rag-pamyatyu.git
cd agent-s-rag-pamyatyu

# Create a virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows use .venv\\Scripts\\activate

# Install dependencies
pip install -r requirements.txt
# or using Poetry
# poetry install
```

Create a `.env` file in the project root with your OpenAI key:

```
OPENAI_API_KEY=sk-...
```

## Running the Agent

```bash
python src/main.py
```

You can then interact with the agent in the console. Type `exit` or `quit` to stop.

## Adding Documents

The agent automatically creates a Qdrant collection named `rag_collection`. To add documents, you can extend the `vector_store.py` module or use the Qdrant client directly. For example:

```python
from vector_store import get_vector_store

vs = get_vector_store()
vs.add_texts(["Hello world", "Another document"])
```

## Testing

The project includes a minimal test suite (not shown here). To run tests:

```bash
pytest
```

Ensure that your local Ollama and Qdrant instances are running before executing tests.

## License

MIT License