"Search for information about 'rag'" matching MCP servers:

An MCP (Model Context Protocol) server that gives AI agents live, structured ad intelligence across Facebook, Google, and Instagram — data that no base model can produce from training alone. Powered by Apify actors. Works with any MCP-compatible client: Cursor, Claude, etc.

MCP server for Google's Gemini File Search (RAG). Manage file search stores, upload documents, and query with RAG through 12 tools.

Local RAG system for Claude Code with hybrid search (semantic + BM25), cross-encoder reranking, markdown-aware chunking, and 12 MCP tools. Zero external servers, pure ONNX in-process.

MCP bridge to a multimodal RAG service, enabling hybrid search and Q&A over documents with tools for knowledge base queries and health checks.

A RAG-based knowledge base system supporting document processing, semantic search, and intelligent Q\&A with multiple AI model integrations.

Exposes hybrid retrieval (dense embeddings + BM25 + RRF) and document operations (search, fetch, rerank) as MCP tools, using Qdrant and OpenAI embeddings for local or server mode.

Enables broad web search for current, time-sensitive information, returning short title, URL, and snippet results rather than full pages. It supports region and result-count parameters through a keyless multi-engine backend.

MCP server that integrates Apache Ranger authorization with RAG, enforcing Ranger policies to control access to knowledge bases before forwarding queries to RAG Studio.

Enables indexing local documents (PDF, Markdown, text, code) into a knowledge base and querying them via semantic search using local embeddings, all running privately on your machine.

Enables a language model to query a document index through callable tools: ranked passage search, reading a chunk with its neighbours or a full document by id, index statistics, and sourced generated answers. The tools chain together, letting a model search, then read around the best chunk ids it received.

Enables local-first hybrid knowledge retrieval from authorized Markdown and plain-text files, combining full-text and vector search with reranking and traceable source references via a single search tool.

Provides retrieval and analysis tools for a quantitative-finance research library, enabling whole-passage answers with verifiable citations, citation checking, entity graph exploration, and corpus inspection.

Provides SQL-backed semantic search over indexed notes using pgvector, exposing tools to search and list note sources via natural language.

Provides semantic vector search over local codebases via MCP, enabling hybrid search (dense + sparse + RRF) for any MCP client like GitHub Copilot or Claude Desktop.

Enables RAG over messy PDFs — extract, chunk, embed, and search scanned, multi-column, and table-heavy documents.

Enables AI agents to connect local knowledge bases, preview and index selected directories, search for traceable snippets with source metadata, and read indexed document versions through MCP tools.

Enables semantic search and question answering over a knowledge base using hybrid retrieval and grounded answers, all running offline with no API keys.

A local RAG MCP server that indexes project documentation and code into ChromaDB for semantic search, supporting multiple tech stacks and deployment modes.

Enables ingestion and semantic search over text documents using PostgreSQL + pgvector and OpenAI-compatible embeddings, allowing any LLM agent to retrieve relevant chunks for grounded answers.

A Model Context Protocol (MCP) server that provides a local-first RAG engine for your markdown documents. It uses a file-based Milvus vector database to index your notes, enabling LLMs to perform semantic search and retrieve relevant content from your local files.