RAG Agent with Qdrant and Ollama

This repository contains a simple RAG (Retrieval‑Augmented Generation) agent built with LangChain, Qdrant as the vector store, and Ollama for embeddings and LLMs.

Features

• Semantic search in a local vector database.
• Add new documents to the knowledge base.
• Recursive text splitting for chunking.
• Interactive CLI to query the agent.

Setup

# Pull required models
ollama pull llama3
ollama pull nomic-embed-text

# Install Python dependencies
pip install -r requirements.txt

Usage

# Load documents from the knowledge folder (default: ./knowledge)
python agent.py

You can type any question. The agent will automatically search the knowledge base and answer.

Adding Documents

Place any .txt files in the knowledge directory before running the agent, or use the add_to_knowledge_base tool via the agent.

Project Structure

• agent.py – Main entry point.
• qdrant_store.py – Wrapper around Qdrant for adding/searching.
• rag_tools.py – LangChain tools for the agent.
• requirements.txt – Python dependencies.
• README.md – Documentation.