ai-rag-vectors

What problem does it solve?

This Skill enables sophisticated semantic search capabilities within your application by setting up a robust vector embedding layer for efficient retrieval of information from documents.

Core Features & Use Cases

• Vector Embeddings: Generates vector representations of text chunks for semantic understanding.
• pgvector Integration: Stores embeddings in PostgreSQL using the pgvector extension for efficient similarity search.
• Recursive Chunking: Breaks down documents into manageable, overlapping chunks to maintain context.
• Use Case: Integrate this skill to allow users to ask natural language questions about a collection of documents, and receive answers based on the most semantically relevant information.

Quick Start

Use the ai-rag-vectors skill to set up the vector embedding layer for your project.