# Agent with RAG Memory

This project implements a retrieval‑augmented generation (RAG) agent that uses **Qdrant** as the vector store and **Ollama** for local LLM inference.  
The agent follows the latest LangChain API and is fully configurable via environment variables.

## Features

- **Qdrant** vector store (via `langchain-qdrant`)
- **Ollama** local LLM integration (via `langchain-ollama`)
- Retrieval‑augmented generation with conversation memory
- Simple CLI interface for quick testing

## 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
```

## Configuration

Create a `.env` file in the project root (or set environment variables directly):

```dotenv
# Qdrant
QDRANT_HOST=localhost
QDRANT_PORT=6333
QDRANT_API_KEY=  # leave empty if no key

# Ollama
OLLAMA_HOST=localhost
OLLAMA_PORT=11434
OLLAMA_MODEL=llama3

# Optional: collection name
QDRANT_COLLECTION=documents
```

> **Note**: The Qdrant instance must be running and accessible at the specified host/port.  
> The Ollama server must be running locally and expose the chosen model.

## Usage

### Adding Documents

```python
from src.agent import Agent
from langchain_core.documents import Document

agent = Agent()

docs = [
    Document(page_content="Python is a programming language.", metadata={"source": "python.txt"}),
    Document(page_content="LangChain is a framework for LLM applications.", metadata={"source": "langchain.txt"}),
]

agent.add_documents(docs)
```

### Querying the Agent

```bash
python -m src.agent "What is LangChain?"
```

or from Python:

```python
response = agent.run("What is LangChain?")
print(response["answer"])
```

The response will include the answer and the source documents used.

## Project Structure

```
agent-s-rag-pamyatyu/
├── src/
│   ├── agent.py
│   ├── config.py
│   └── vector_store.py
├── requirements.txt
├── pyproject.toml
└── README.md
```

## License

MIT License