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README.md

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-# 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.