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.
Perform semantic searches across indexed codebases using RAG (Retrieval-Augmented Generation) to find relevant code snippets based on meaning and context.
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.
An MCP-compatible system that handles large files (up to 200MB) with intelligent chunking and multi-format document support for advanced retrieval-augmented generation.