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

# 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

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