RAG Agent with Local Qdrant and Ollama

This repository implements a simple AI agent that can store, search, and retrieve information from a local vector store using Qdrant and Ollama embeddings. The agent is built with LangChain v1 and supports an interactive CLI with the following commands:

• /add – add a new document to the knowledge base.
• /search – perform a semantic search in the knowledge base.
• /quit – exit the program.

Features

• RAG – Retrieval-Augmented Generation using a local vector store.
• Qdrant – Vector similarity search engine.
• Ollama – Local LLM (llama3) and embeddings (nomic-embed-text).
• LangChain v1 – Modern agent framework.
• Recursive text splitter – Chunk documents before embedding.

Setup

# Install Ollama models
ollama pull llama3
ollama pull nomic-embed-text

# Install Python dependencies
pip install -r requirements.txt

Usage

# Load documents from the `docs` folder and start the CLI
python -m src.cli --docs docs

You can then interact with the agent using the commands described above.