RAG Agent with Qdrant and Ollama
What the project does
This repository contains a lightweight Retrieval‑Augmented Generation (RAG) agent that can:
- Store arbitrary text snippets in an embedded vector store backed by Qdrant.
- Search those snippets using semantic similarity.
- 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.