pgvector-rag

What problem does it solve?

This Skill provides a comprehensive toolkit for building vector search and Retrieval-Augmented Generation (RAG) applications directly within PostgreSQL, leveraging Ollama for local LLM inference.

Core Features & Use Cases

• Vector Database Setup: Configure PostgreSQL with pgvector for efficient vector storage.
• Embedding Generation: Generate embeddings for text using Ollama models.
• RAG Pipelines: Implement end-to-end RAG workflows, from document ingestion to LLM-powered responses.
• Use Case: Build an AI-powered Q&A system over your company's internal documentation. When a user asks a question, the Skill finds relevant document chunks using semantic search in PostgreSQL and then uses Ollama to generate a concise answer based on that context.

Quick Start

Use the pgvector-rag skill to set up your vector database and generate embeddings for your documents.