Extract and analyze web page content to answer specific questions using RAG (Retrieval Augmented Generation). Provide AI-generated responses based on relevant page sections for accurate insights.
Retrieve detailed information about a specific RAG project, including its structure, content, and configuration for effective library management and content retrieval.
Add files to a RAG system for document retrieval, supporting PDF, DOCX, TXT, MD, CSV, and JSON formats to enable semantic search and information access.
An MCP server that implements Retrieval-Augmented Generation to efficiently retrieve and process important information from various sources, providing accurate and contextually relevant responses.
Enhances AI model capabilities with structured, retrieval-augmented thinking processes that enable dynamic thought chains, parallel exploration paths, and recursive refinement cycles for improved reasoning.
A server that integrates Retrieval-Augmented Generation (RAG) with the Model Control Protocol (MCP) to provide web search capabilities and document analysis for AI assistants.