```markdown
# RAG Agent with Qdrant and Ollama

This project implements an AI agent that can search and add documents to a local knowledge base using **Qdrant** for vector storage and **Ollama** for embeddings and LLM inference. The agent is built with **LangChain** and exposes two tools:

- `search_knowledge_base(query, max_results)` – semantic search in the knowledge base.
- `add_to_knowledge_base(content, title)` – add a new document to the knowledge base.

## Features

- **Vector store**: Qdrant with Ollama embeddings (`nomic-embed-text`).
- **Chunking**: Recursive character splitter with overlap.
- **Agent**: Zero-shot React agent that uses the two tools.
- **CLI**: Interactive command line interface to add documents and query the agent.
- **Batch loading**: Script to load all text files from a directory into the knowledge base.

## Prerequisites

- Python 3.10+
- Docker (for Qdrant) or a running Qdrant instance.
- Ollama installed locally with the following models:
  ```bash
  ollama pull llama3
  ollama pull nomic-embed-text
  ```

## Setup

```bash
# Clone the repository
git clone https://github.com/your-username/rag-agent.git
cd rag-agent

# Create a virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Start Qdrant (Docker example)
docker run -p 6333:6333 qdrant/qdrant
```

## Usage

### 1. Load documents into the knowledge base

```bash
python src/main.py /path/to/documents
```

Supported file types: `.txt`, `.md`. (PDF support can be added with an additional parser.)

### 2. Start the interactive CLI

```bash
python src/cli.py
```

Commands:

- `/add <file_path>` – Add a single document.
- `/search <query>` – Query the agent.
- `/quit` – Exit.

### 3. Example

```bash
> /add example.txt
Document 'example' added to knowledge base with 3 chunks.
> /search What is the capital of France?
1. The capital of France is Paris. (Title: example)
```

## Project Structure

```
rag-agent/
├── src/
│   ├── agent.py
│   ├── cli.py
│   ├── main.py
│   ├── tools.py
│   └── vector_store.py
├── requirements.txt
└── README.md
```

## License

MIT License
```