Vector Database

This skill provides comprehensive documentation and code examples for building RAG (Retrieval-Augmented Generation) systems. It covers vector database setup (Pinecone, Weaviate, Chroma, pgvector), embedding strategies, retrieval patterns (hybrid search, HyDE, multi-query), chunking strategies, and evaluation metrics using LangChain and LangGraph.

A comprehensive plugin for building production-ready LLM applications, including RAG systems with vector databases, AI agents using LangGraph, advanced prompt engineering patterns, and embedding strategies. It provides templates and best practices for integrating with Pinecone, Qdrant, pgvector, and other vector stores.

AI Partner Chat provides personalized conversations by integrating user/AI personas and vectorized personal notes. It uses a local ChromaDB database with BAAI/bge-m3 embeddings to retrieve relevant context from your markdown notes during conversations.

A comprehensive Pinecone integration plugin that enables document-based Q&A with citations through Pinecone Assistant. Users can create assistants, upload documentation files, sync repositories, and chat with their knowledge base. Also includes MCP server tools for vector index management, semantic search, and RAG application development.