# RAG Agent with Qdrant and Ollama

This repository contains a minimal but fully‑functional example of an AI agent that uses **Qdrant** as a local vector store, **Ollama** for embeddings and a local LLM, and **LangChain** for the agent logic.

## Features

* **Semantic search** – `search_knowledge_base` tool queries the vector store.
* **Document ingestion** – `add_to_knowledge_base` tool splits text into chunks and stores them.
* **Interactive CLI** – simple command line interface for adding documents, searching and chatting with the agent.
* **Modular design** – vector store, tools and agent logic are separated into distinct modules.

## Setup

```bash
# 1. Install Ollama models
ollama pull llama3
ollama pull nomic-embed-text

# 2. Install Python dependencies
pip install -r requirements.txt

# 3. Start Qdrant (Docker recommended)
#    docker run -p 6333:6333 qdrant/qdrant
```

## Usage

```bash
python -m workspace.task-6a02e23da6fe2e4ac16acf65.cli
```

The CLI accepts the following commands:

* `/add <title>` – add a new document. After the title you will be prompted to paste the content; finish with a line containing only `END`.
* `/search <query>` – perform a semantic search.
* `/quit` – exit.

Anything else is forwarded to the agent.

## Project structure

```
workspace/
├── task-6a02e23da6fe2e4ac16acf65/
│   ├── agent.py          # Agent and tool definitions
│   ├── cli.py            # Interactive command line interface
│   ├── vector_store.py   # Qdrant + Ollama wrapper
│   ├── requirements.txt
│   └── README.md
```