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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.