# RAG Agent with Ollama

This project implements a simple Retrieval-Augmented Generation (RAG) agent that uses **Ollama** for both embeddings and LLM inference.  
The agent stores documents in memory, retrieves the most relevant ones for a query, and generates an answer using the retrieved context.

## Features

- **Embeddings** – Uses Ollama’s embedding endpoint (`ollama.embeddings`) with caching.
- **LLM** – Uses Ollama’s chat endpoint (`ollama.chat`) for generation.
- **RAG** – Cosine similarity based retrieval of top‑k documents.
- **FastAPI** – Exposes a REST API for adding documents and asking questions.
- **Docker** – Containerized with Ollama and FastAPI.

## Setup

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

# Build Docker image
docker build -t rag-agent .

# Run container
docker run -p 8000:8000 rag-agent
```

The API will be available at `http://localhost:8000`.

## API Endpoints

| Method | Path      | Description                     |
|--------|-----------|---------------------------------|
| POST   | /documents | Add a document to the agent.    |
| POST   | /ask       | Ask a question; returns answer. |

### Example

```bash
# Add a document
curl -X POST http://localhost:8000/documents \
     -H "Content-Type: application/json" \
     -d '{"text":"Python is a programming language."}'

# Ask a question
curl -X POST http://localhost:8000/ask \
     -H "Content-Type: application/json" \
     -d '{"query":"What is Python?"}'
```

## Dependencies

- `ollama` – Ollama client for embeddings and chat.
- `fastapi` – Web framework.
- `uvicorn` – ASGI server.
- `numpy` – Numerical operations.
- `pydantic` – Data validation.

All dependencies are listed in `requirements.txt`.

## License

MIT License
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This implementation follows the assignment constraints: **only Ollama** is used for embeddings and LLM, no OpenAI services are involved.