Rag

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 guidance for building sophisticated LLM applications using LangChain 1.x and LangGraph. It covers agent orchestration with StateGraph, RAG implementations, multi-agent systems, memory management patterns, and production deployment best practices including LangSmith observability integration.

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.

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.

Clarity Gate is a document verification system that checks whether claims are properly marked as uncertain or validated before documents enter RAG knowledge bases. It helps prevent LLMs from mistaking assumptions for facts by enforcing epistemic markers and requiring human-in-the-loop verification for unverified claims.