# 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
```bash
# Install Python dependencies
pip install -r requirements.txt

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

## Running the CLI
```bash
python cli.py
```
You will see a prompt. Use `/add`, `/search`, or `/quit` as described above.