RAG Agent with Qdrant and Ollama

Overview

This repository contains a minimal implementation of an AI agent that uses RAG (Retrieval‑Augmented Generation) with a local vector store powered by Qdrant and embeddings from Ollama. The agent can:

1. Add documents to the knowledge base.
2. Search the knowledge base semantically.
3. Answer arbitrary user queries using the stored information.

The project is structured into three main files:

• main.py – entry point with an interactive CLI and examples.
• tools.py – LangChain tools for adding/searching documents.
• requirements.txt – Python dependencies.

Installation

# Pull required Ollama models (run once)
ollama pull llama3
ollama pull nomic-embed-text

# Install Python packages
pip install -r requirements.txt

Usage

Run the interactive client:

python main.py

You can use the following commands:

• /add – add a new document.
• /search <query> – perform a semantic search.
• /quit – exit.
• Any other text is treated as a question for the agent.

Architecture

The agent uses LangChain’s create_agent with two custom tools:

1. add_to_knowledge_base – splits input into chunks, embeds them via Ollama, and stores in Qdrant.
2. search_knowledge_base – performs a similarity search on the vector store.

The LLM is an Ollama llama3 model accessed through LangChain’s ChatOllama. The embeddings are provided by OllamaEmbeddings with the nomic-embed-text model.

Extending

Feel free to add more tools or integrate a persistent Qdrant instance instead of an in‑memory one. The code is intentionally simple for educational purposes.