# RAG Agent with Qdrant and Tavily

This repository implements an AI agent that can answer questions using a local knowledge base stored in **Qdrant** and up‑to‑date information fetched from the web via **Tavily**.  The agent is built with **LangChain 1.x** and **Ollama** for local LLM and embeddings.

## Features

* **Local RAG** – Semantic search in Qdrant using Ollama embeddings.
* **Web search** – Tavily integration for real‑time information.
* **Automatic source selection** – The LLM decides whether to use the local KB or the web.
* **Persistent vector store** – Data is saved in `./qdrant_db` and reused across runs.
* **Interactive CLI** – Add documents, ask questions, and see the source.

## 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 Qdrant (Docker recommended)
#    If you prefer a local binary, download from https://qdrant.tech
#    Docker command:
#    docker run -p 6333:6333 qdrant/qdrant
```

## Usage

```bash
# Start the CLI
python -m workspace.task-6a1864f78a94f887e50d46da.cli
```

Commands:

* `/add <directory>` – Load all `.txt` and `.md` files from the directory into Qdrant.
* `/search <question>` – Ask the agent a question.
* `/quit` – Exit.

Example:

```
> /add ./documents
Loaded 12 chunks into Qdrant.
Documents added.
> /search What is LangGraph?
Answer:
LangGraph is a framework for building ...
Source: chromadb
```

## Environment Variables

* `TAVILY_API_KEY` – Your Tavily API key.

Create a `.env` file in the project root:

```
TAVILY_API_KEY=your_api_key_here
```

## Project Structure

```
workspace/
├─ task-6a1864f78a94f887e50d46da/
│  ├─ vector_store.py          # Qdrant vector store helpers
│  ├─ tools.py                 # Local KB and web search tools
│  ├─ agent.py                 # Agent definition
│  ├─ cli.py                   # Interactive command line
│  ├─ requirements.txt
│  └─ README.md
```

## License

MIT