# RAG‑Agent with Qdrant & Ollama

This repository contains a minimal but functional implementation of a **RAG (Retrieval‑Augmented Generation) agent** that:

* Stores embeddings in a **Qdrant** vector database.
* Generates embeddings with **Ollama** (`nomic-embed-text`).
* Uses **LangChain** (v1+) for the agent, tools and prompt‑engineering.

The agent can:

* Search the knowledge base (`/search`).
* Add new documents (`/add`).
* Interact through a simple CLI.

## Installation

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

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

# 3. Run the CLI
python -m src.cli
```

> **Note**: Qdrant must be running locally on port 6333. You can start it using Docker:
>
> ```bash
> docker run -p 6333:6333 qdrant/qdrant
> ```

## Directory structure

```
workspace/task-6a02e23da6fe2e4ac16acf65/
├─ src/
│  ├─ vector_store.py   # Qdrant wrapper
│  ├─ tools.py          # LangChain tools
│  ├─ agent.py          # Agent implementation
│  ├─ loader.py         # Utility for bulk loading
│  └─ cli.py            # Interactive CLI
├─ requirements.txt
└─ README.md
```

## Usage

### Load documents from a folder

```bash
python -m src.loader /path/to/text/files
```

### Start the interactive CLI

```bash
python -m src.cli
```

- `/add` – add a new document.
- `/search` – perform a semantic search.
- `/quit` – exit.

Any other input is treated as a user message and processed by the agent.

## How it works

1. **Vector store** – `KnowledgeBase` wraps `QdrantVectorStore`. It splits documents into chunks using `RecursiveCharacterTextSplitter`, embeds them with `OllamaEmbeddings`, and stores the vectors.
2. **Tools** – Two tools (`search_knowledge_base`, `add_to_knowledge_base`) are exposed to the agent via LangChain's `@tool` decorator.
3. **Agent** – Built with `create_tool_calling_agent` and `AgentExecutor`. The system prompt encourages the assistant to use the tools.
4. **CLI** – Provides a simple REPL for adding documents, searching, and chatting with the agent.

## Extending

* Replace the embedding model with any Ollama model.
* Swap Qdrant for another vector store supported by LangChain.
* Add more tools (e.g., delete, update) following the same pattern.

---

Happy experimenting!