Add files to a searchable knowledge base for AI applications by uploading documents to the Gemini RAG MCP Server's FileSearchStore for processing and indexing.
Download entire documentation websites for offline access and RAG indexing. Supports configurable depth and concurrency settings for efficient website retrieval.
Upload text content for RAG indexing to make it searchable in a knowledge base. The tool processes content and adds metadata for information retrieval.
Provides retrieval-augmented generation (RAG) capabilities by ingesting various document formats into a persistent ChromaDB vector store. It enables semantic search and retrieval using either OpenAI or Ollama embeddings for processing local files, directories, and URLs.
Enables intelligent handling of large files through smart chunking, search with regex support, line navigation, and streaming capabilities without loading entire files into memory.
An MCP server that enables interacting with Google's Indexing API, allowing agents to submit URLs to Google for indexing or removal from search results through natural language commands.