RAG AI Agent Skills

1. 入门示例: tablestore-java-mcp-server 2. 基于 MCP 架构实现知识库答疑系统: tablestore-java-mcp-server-rag - 实现一个目前最常见的一类 AI 应用即答疑系统，支持基于私有知识库的问答，会对知识库构建和 RAG 做一些优化。 1. 入门示例: tablestore-python-mcp-server 1. Mem0-OpenMemory-MCP: tablestore-python-mem0-mcp-server 欢迎加入我们的钉钉公开群，与我们一起探讨 AI 技术。钉钉群号：36165029092

Pre-ingestion verification for epistemic quality in RAG systems. Ensures documents are properly qualified before entering knowledge bases. Produces CGD (Clarity-Gated Documents) and validates SOT (Source of Truth) files.

Read and write to a Pinecone index. The server implements the ability to read and write to a Pinecone index. - semantic-search: Search for records in the Pinecone index. - read-document: Read a document from the Pinecone index. - list-documents: List all documents in the Pinecone index. - pinecone-stats: Get stats about the Pinecone index, including the number of records, dimensions, and namespace

Comprehensive guide to implementing RAG systems including vector database selection, chunking strategies, embedding models, and retrieval optimization. Use when building RAG systems, implementing semantic search, optimizing retrieval quality, or debugging RAG performance issues.

Code-Graph-RAG parses a multi-language codebase with Tree-sitter, builds a knowledge graph of its structure in Memgraph, and lets you query, edit, and optimise that code in plain English. It works across a monorepo of mixed languages under one unified graph schema. - Ruby Support: Ruby joins the graph through a new pluggable ast-grep tier that adds a language from a single YAML pattern file, emitt

Efficiently perform web searches using the mcp-local-rag server with semantic similarity ranking. Use this skill when you need to search the web for current information, research topics across multiple sources, or gather context from the internet without using external APIs. This skill teaches effective use of RAG-based web search with DuckDuckGo, Google, and multi-engine deep research capabilities.

English | 简体中文 🧠 The data layer for AI systems. Skill Seekers turns any documentation, GitHub repo, or PDF into structured knowledge assets—ready to power AI Skills (Claude, Gemini, OpenAI), RAG pipelines (LangChain, LlamaIndex, Pinecone), and AI coding assistants (Cursor, Windsurf, Cline) in minutes, not hours. Skill Seekers is the universal preprocessing layer that sits between raw documentatio

👋 join us on Discord and WeChat If you like Dingo, please give us a ⭐ on GitHub! Dingo is A Comprehensive AI Data, Model and Application Quality Evaluation Tool, designed for ML practitioners, data engineers, and AI researchers. It helps you systematically assess and improve the quality of training data, fine-tuning datasets, and production AI systems. 🎯 Production-Grade Qua

knowledge-to-action-mcp is an MCP server for people whose real project context lives in notes, decisions, roadmaps, and meeting docs, not just code. Most Obsidian MCP servers stop at "read a note" or "search a vault." This one goes further: That means an MCP client can move from: If you work out of Obsidian, your important context is usually spread across: - roadmap notes - meeting notes - decisio

A Model Context Protocol (MCP) server that provides local vector database functionality using FAISS for Retrieval-Augmented Generation (RAG) applications. - Local Vector Storage: Uses FAISS for efficient similarity search without external dependencies - Document Ingestion: Automatically chunks and embeds documents for storage - Semantic Search: Query documents using natural language with sentence

Official OpenAI plugin with OpenAPI schema for semantic search and retrieval-augmented generation (RAG) over personal or organizational documents.

Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.

Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.

MCP Server for Driflyte. The Driflyte MCP Server exposes tools that allow AI assistants to query and retrieve topic-specific knowledge from recursively crawled and indexed web pages. With this MCP server, Driflyte acts as a bridge between diverse, topic-aware content sources (web, GitHub, and more) and AI-powered reasoning, enabling richer, more accurate answers. - Deep Web Crawling: Recursively f

🚀 Try ApeRAG Live Demo - Experience the full platform capabilities with our hosted demo ApeRAG is a production-ready RAG (Retrieval-Augmented Generation) platform that combines Graph RAG, vector search, and full-text search with advanced AI agents. Build sophisticated AI applications with hybrid retrieval, multimodal document processing, intelligent agents, and enterprise-grade management feature

Vectara-MCP provides any agentic application with access to fast, reliable RAG with reduced hallucination, powered by Vectara's Trusted RAG platform, through the MCP protocol. You can install the package directly from PyPI: - Security: Built-in authentication via bearer tokens - Encryption: HTTPS ready - Rate Limiting: 100 requests/minute by default - CORS Protection: Configurable origin validatio