RAG Agent with Qdrant and Ollama

What the project does

This repository contains a lightweight Retrieval‑Augmented Generation (RAG) agent that can:

1. Store arbitrary text snippets in an embedded vector store backed by Qdrant.
2. Search those snippets using semantic similarity.
3. Answer user questions by combining retrieved passages with the LLM from Ollama.

The CLI (cli.py) exposes three explicit commands:

• /add <text> – add a new passage to the knowledge base.
• /search <query> – perform a semantic search and list matching passages.
• /quit – exit the program. Any other input is forwarded to the agent as a normal question.

Technology stack

• LLM – Ollama llama3 (or any compatible model).
• Embeddings – Ollama nomic-embed-text.
• Vector store – Qdrant in‑memory collection.
• LangChain – orchestration of tools and agent logic.

Installation

# Install Python dependencies
pip install -r requirements.txt

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

Running the CLI

python cli.py

You will see a prompt. Use /add, /search, or /quit as described above.