# RAG Agent with LangChain, Qdrant, and Ollama

This repository contains a minimal example of a Retrieval-Augmented Generation (RAG) agent built with **LangChain**, **Qdrant**, and **Ollama**. The agent retrieves relevant documents from a local Qdrant vector store and generates answers using an Ollama language model.

## Prerequisites

- **Python 3.10+**  
- **Qdrant** server running locally (default port `6333`).  
  - Create a collection named `rag_collection` and populate it with embeddings.  
- **Ollama** server running locally (default port `11434`).  
  - Ensure the model `llama3.1` (or any other supported model) is available.

## 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: .venv\\Scripts\\activate

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

## Usage

```bash
python -m src.main
```

You will be prompted to enter a question. The agent will retrieve relevant documents from Qdrant and generate an answer using Ollama. Type `exit` or `quit` to terminate the program.

## Project Structure

```
agent-s-rag-pamyatyu/
├── requirements.txt
├── src/
│   └── main.py
└── README.md
```

- `requirements.txt` – lists all Python dependencies, including `langchain-qdrant` and `langchain-ollama`.
- `src/main.py` – contains the RAG agent implementation.
- `README.md` – this documentation file.

## Troubleshooting

- **Missing dependencies**: Ensure you ran `pip install -r requirements.txt`.  
- **Qdrant connection errors**: Verify Qdrant is running and the collection name matches `rag_collection`.  
- **Ollama connection errors**: Verify Ollama is running and the model name is correct.

## License

This project is provided as-is for educational purposes. Feel free to modify and extend it.

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