Ask questions about memory files using retrieval-augmented generation to get answers from stored content with configurable search modes.
MIT
134,392 tools. Last updated 2026-05-25 19:04
"RAG (Retrieval-Augmented Generation) system that can store papers and web pages" matching MCP tools:
- Create a named local vector index for retrieval-augmented generation. Documents added are embedded via Ollama for local RAG without cloud dependencies.
- Stores a knowledge fragment with source and evidence tier metadata for future retrieval via semantic RAG queries.MIT
- Convert web pages to clean markdown format by extracting content, removing unnecessary elements, and ranking information for RAG applications.MIT
- Create a knowledge base in Amazon Bedrock to store and organize documents for retrieval-augmented generation (RAG), supporting vector search, custom parsing, and chunking strategies.MIT
- Extract answers from web pages by analyzing content with AI. Provide a URL and question to get specific information from the page.MIT
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- Find comprehensive web content across 29M+ pages. Retrieve up to 30K characters per result with strict filters for research, news, or educational sources.MIT
- List all valid hook event names that listeners can subscribe to, covering memory lifecycle, knowledge graph operations, and other system events.MIT
- Store content with optional tags and context, enabling future retrieval via basic or structured search.MIT
- Parse code files into semantic chunks such as functions, classes, and methods to improve retrieval in RAG systems.
- Upload files to process and index them for searchable knowledge retrieval using RAG (Retrieval-Augmented Generation) technology.MIT
- Execute RAG queries against Amazon Bedrock Knowledge Bases to retrieve relevant documents using vector search for enhanced information retrieval.MIT
- Search uploaded documents using RAG to find answers with citations. Ask questions to retrieve information from your knowledge base.MIT
- Retrieve a complete list of all files stored in the RAG system for document management and semantic search operations.MIT
- Perform semantic search and question-answering on your Obsidian vault using a local retrieval-augmented generation pipeline.MIT
- 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.MIT
- Delete files from the RAG system to manage storage and maintain relevant content for retrieval-augmented generation tasks.MIT
- Generate Stylus/Rust smart contract code for Arbitrum using RAG context and version-aware generation. Supports ERC standards and custom contracts with optional tests.MIT
- Retrieve statistics about the Retrieval-Augmented Generation system's performance and usage metrics to monitor and analyze its operational data.MIT
- Remove stored files, vector memory, or all data from the RAG system to free storage space or reset the knowledge base.MIT