Semantic Search

A comprehensive guide for selecting and optimizing embedding models for vector search and RAG applications. Provides code templates for Voyage AI, OpenAI, and local embedding models, along with chunking strategies, domain-specific pipelines, and quality evaluation methods.

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.

Vexor CLI is a semantic file discovery skill that helps locate files by intent rather than exact text matching. It wraps the vexor command-line tool to enable natural language queries like 'find authentication logic' across codebases, using embedding providers (OpenAI, Gemini, or local models) for semantic search.

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.