vectordb

What problem does it solve?

This Skill provides comprehensive capabilities for implementing semantic search and Retrieval Augmented Generation (RAG) by efficiently storing and retrieving high-dimensional vector embeddings.

Core Features & Use Cases

• Embedding Generation: Create vector embeddings from text using models like OpenAI's.
• Vector Store Integration: Interact with popular vector databases such as Pinecone, Chroma, and pgvector (PostgreSQL).
• RAG Implementation: Build RAG pipelines for context-aware AI responses, including document chunking and retrieval.
• Use Case: Store document embeddings in a vector database, then use RAG to answer complex questions based on retrieved relevant documents, providing more accurate and contextual AI responses.

Quick Start

Generate an OpenAI embedding for the text "What is the capital of France?".