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licenseA
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maintenance"primitive" RAG-like web search model context protocol server that runs locally. ✨ no APIs ✨
Last updated
5
126
Python
MIT
Why this server?
This server explicitly labels itself as a "primitive" RAG-like web search model designed to run locally, making it a direct match for the 'rag' search query.
Why this server?
This server directly implements a RAG (Retrieval-Augmented Generation) system for querying documents and providing context from local files to LLMs.
FlicenseAqualityFmaintenanceA TypeScript MCP server that allows querying documents using LLMs with context from locally stored repositories and text files through a RAG (Retrieval-Augmented Generation) system.Last updated417Why this server?
This is described as an open-source platform for Retrieval-Augmented Generation (RAG), which is precisely what the user is searching for.
AlicenseAqualityCmaintenanceAn open-source platform for Retrieval-Augmented Generation (RAG). Upload documents and query them ⚡Last updated12430MITWhy this server?
This server implements Retrieval-Augmented Generation (RAG) using external tools and explicitly mentions semantic searches, core concepts of RAG.
Flicense-qualityDmaintenanceImplements Retrieval-Augmented Generation (RAG) using GroundX and OpenAI, allowing users to ingest documents and perform semantic searches with advanced context handling through Modern Context Processing (MCP).Last updated5Why this server?
This entry directly references a Retrieval-Augmented Generation system for document querying via an API architecture.
Flicense-qualityDmaintenanceAn API that enables document querying through a Retrieval-Augmented Generation system implemented with Memory-Controller-Policy architecture for improved maintainability and scalability.Last updatedWhy this server?
This server provides intelligent document search and retrieval from PDF collections using semantic search capabilities powered by vector storage, a common RAG implementation pattern.
Alicense-qualityDmaintenanceA Model Context Protocol server that enables intelligent document search and retrieval from PDF collections, providing semantic search capabilities powered by OpenAI embeddings and ChromaDB vector storage.Last updated13MITWhy this server?
This server is designed for RAG over codebases using semantic search and embeddings, providing a specific implementation of the requested technology.
Flicense-qualityDmaintenanceEnables semantic search and retrieval of code files using embeddings stored in PostgreSQL. Supports intelligent codebase exploration through natural language queries, file listing, and content retrieval.Last updated16Why this server?
This explicitly offers tools for retrieving and processing documentation using vector search, enabling AI assistants to 'augment their responses' (RAG).
AlicenseAqualityDmaintenanceProvides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context.Last updated7121MITWhy this server?
This is a memory vector server designed for semantic search and memory management, providing the foundational components necessary for a RAG architecture.
Flicense-qualityDmaintenanceModel Context Protocol (MCP) server implementation for semantic search and memory management using TxtAI. This server provides a robust API for storing, retrieving, and managing text-based memories with semantic search capabilities. You can use Claude and Cline AI AlsoLast updated14
