From b2b988f3ef5ed4cebeaf5c10057df7f8aeb32843 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 13:32:50 +0000 Subject: [PATCH] =?UTF-8?q?=D0=A3=D0=B4=D0=B0=D0=BB=D0=B8=D1=82=D1=8C=20RE?= =?UTF-8?q?ADME.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- README.md | 35 ----------------------------------- 1 file changed, 35 deletions(-) delete mode 100644 README.md diff --git a/README.md b/README.md deleted file mode 100644 index 4c99bfc..0000000 --- a/README.md +++ /dev/null @@ -1,35 +0,0 @@ -# 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.