GroundX RAG

Ragie

Enhances connectivity with Ragie's knowledge repository system for streamlined retrieval and extraction of data from extensive datasets, optimizing search and information access.

RAG Docs

Enhances information retrieval through semantic search functionality and a vector database (Qdrant), facilitating streamlined access to extensive document repositories.

RAG Documentation

Experience advanced knowledge access with seamless integration of Qdrant vector search and documentation retrieval in Model Context Protocol (MCP). Unlock context-aware responses and enable semantic querying for a richer user experience.

Paperless-NGX

Empower artificial intelligence (AI) to seamlessly engage with Paperless-NGX platforms, facilitating the organization, retrieval, and administration of document libraries using intuitive verbal instructions.

Paperless-NGX

Easily connect with the Paperless-NGX API for streamlined document handling tasks such as document organization, searching, modification, and file upload. Simplify the management and access of your digital documents with this seamless integration.

QDrant RagDocs

Employs Qdrant vector database and embeddings for enhanced semantic search and documentation organization, facilitating Retrieval-Augmented Generation within the Model Context Protocol (MCP) framework.

RAG Documentation Search

Enhances document search with semantic vectors for contextually relevant results from specified document repositories.

SearXNG

Enhance your search experience by effortlessly combining various search engines through the Model Context Protocol (MCP). Access a comprehensive search solution for efficient information retrieval and reliable fact verification.

Docs RAG

Utilizing a RAG framework developed with TypeScript, LlamaIndex, and Gemini embeddings, this protocol empowers artificial intelligence to search and assess native files and Git repositories effectively.