From 3a686e4bb759a6d736ffac659fb1827e06194ee7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=9A=D0=B8=D1=80=D0=B8=D0=BB=D0=BB=20=D0=9A=D1=83=D1=82?= =?UTF-8?q?=D0=BB=D0=B0=D1=85=D0=BC=D0=B5=D1=82=D0=BE=D0=B2?= Date: Thu, 28 May 2026 13:13:09 +0000 Subject: [PATCH] add README.md --- README.md | 35 +++++++++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) create mode 100644 README.md diff --git a/README.md b/README.md new file mode 100644 index 0000000..4c99bfc --- /dev/null +++ b/README.md @@ -0,0 +1,35 @@ +# 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 ` – add a new passage to the knowledge base. +- `/search ` – 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.