"Creating a Document as a Knowledge Base" matching MCP servers:

A knowledge base MCP server that aggregates team knowledge from multiple sources into Postgres. It provides hybrid search (full-text + vector + RRF) via MCP tools, and enables direct recording of decisions, learnings, and pitfalls.

Enables local full-text and semantic search over saved content, providing search and retrieval capabilities via MCP for AI assistants.

Provides cited retrieval over local-first knowledge sources (TXT, CSV, Google Sheets, Zoho Mail, MBOX) via SQLite FTS5, with tools for searching and checking source status, returning evidence links instead of generated answers.

A cross-project knowledge base MCP server supporting three-layer search (text matching, TF-IDF, and semantic vectors) with a Web UI for management.

A persistent memory system that captures, classifies, synthesizes, and retrieves knowledge for AI agents, turning raw information into refined, retrievable knowledge that compounds intelligence over time.

A local knowledge base server that connects to AI assistants, turning markdown files into a semantically searchable memory layer via OpenAI embeddings and SQLite.

基于MCP协议的知识库服务器，内置LLM层，提供结构化知识存储、语义搜索和主动知识推送，支持通过工具进行知识学习、搜索和关联推理。

Provides semantic search and data retrieval capabilities over a knowledge base with multiple tools including keyword search, category filtering, and ID-based lookup with in-memory caching.

Enables semantic search across Clarion programming documentation via natural language queries in Claude AI, supporting 21 manuals and 21,747 indexed chunks.

Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.

Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.

Enables semantic search and document management with support for text, PDF, and image uploads using your own Supabase database and OpenAI API keys. Supports multi-tenant deployment on Cloudflare Workers or local hosting.

Builds a searchable knowledge base from YouTube video transcripts with hybrid semantic and keyword search. Allows LLM assistants to search, organize, and retrieve timestamped information from videos you've watched.

MCP server for managing and searching multi-tenant knowledge bases backed by SQLite with FTS5, enabling AI agents to persist and retrieve content via full-text search.

Provides read-only hybrid RAG search and discovery over a local-first AI knowledge corpus, enabling semantic and keyword search, browse, digest, and status tools.

A Python-based MCP server that enables document-based question answering by processing PDF, TXT, and Markdown files through OpenAI's API. It provides hallucination-free responses based strictly on document content using semantic search and includes a web interface for management.

Enables document-based question answering using OpenAI's GPT-4 with semantic search and embeddings. Upload PDF, TXT, or Markdown files and get answers strictly based on document content with source attribution and confidence scores.

A production-ready RAG pipeline exposed as an MCP server, enabling querying of VA education manuals with ranked chunks and citations, and integrated with Claude Desktop for retrieval-augmented generation.

Enables indexing a Markdown knowledge base with local sentence embeddings and serving retrieval over MCP, allowing LLM clients to query the indexed knowledge.

A local-first RAG system that indexes personal documents and enables querying them via an MCP tool, keeping data private with local embeddings.