# Agent with RAG Memory using Qdrant and Ollama

This project demonstrates a simple Retrieval-Augmented Generation (RAG) agent built with LangChain, Qdrant, and Ollama. The agent uses Ollama embeddings for vector representation and Qdrant as the vector store.

## Prerequisites

- Python 3.10+
- Qdrant server running locally or accessible remotely
- Ollama server running locally or accessible remotely

## Installation

```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 (optional but recommended)
python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

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

## Configuration

Edit `config.py` to match your environment:

```python
# Qdrant settings
QDRANT_HOST = "localhost"
QDRANT_PORT = 6333
QDRANT_API_KEY = None
QDRANT_COLLECTION = "rag_collection"

# Ollama settings
OLLAMA_MODEL = "llama3"
```

## Running the Agent

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

The script will:

1. Connect to Qdrant.
2. Create an Ollama embeddings instance.
3. Add sample documents to the collection if it is empty.
4. Build a RetrievalQA chain using the Ollama LLM.
5. Execute a sample query and print the answer.

## Extending

- Replace the sample documents with your own corpus.
- Adjust the `chain_type` in `src/agent.py` if you need a different retrieval strategy.
- Use environment variables or a `.env` file to store sensitive information like `QDRANT_API_KEY`.

## License

MIT License
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