# RAG Agent with Ollama Embeddings

This project demonstrates a simple Retrieval-Augmented Generation (RAG) agent that uses **OllamaEmbeddings** for vector similarity search and a local in‑memory knowledge base.  
The agent is built with **LangChain** and exposes two tools:

- `search_knowledge_base`: Search the knowledge base for relevant documents.
- `add_to_knowledge_base`: Add new content to the knowledge base.

## Prerequisites

- Python 3.10+
- An Ollama server running locally (e.g., `ollama serve`).
- The Ollama model you want to use (default is `mistral`).

## 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
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

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

`requirements.txt` contains:

```text
langchain
langchain-community
openai
```

## Configuration

Set the Ollama model via environment variable (optional):

```bash
export OLLAMA_MODEL=mistral   # or any other model available in Ollama
```

If you run the Ollama server on a non‑default host/port, set:

```bash
export OLLAMA_HOST=http://localhost:11434
```

## Running the Agent

```bash
python src/agent.py
```

You will see a prompt:

```
Welcome to the RAG Agent. Type 'exit' to quit.
User:
```

- **Add knowledge**:  
  `add_to_knowledge_base This is a new piece of information.`

- **Search knowledge**:  
  `search_knowledge_base information`

The agent will automatically decide which tool to use based on the user query.

## Example Session

```
User: add_to_knowledge_base Python is a versatile programming language.
Agent: Document added. Total documents: 1.
User: search_knowledge_base programming language
Agent: Python is a versatile programming language.
```

## Notes

- The knowledge base is **in‑memory**; data will be lost when the program exits.
- For persistent storage, replace the in‑memory implementation with a vector database such as Chroma or FAISS.
- The LLM used for generation is OpenAI’s GPT‑3.5 via the `openai` package. Adjust the `OpenAI` initialization if you prefer another model.

## License

MIT License