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

# 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

# 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