Ask questions about memory files using retrieval-augmented generation to get answers from stored content with configurable search modes.
MIT
133,382 tools. Last updated 2026-05-25 16:29
"Chat with RAG (Retrieval-Augmented Generation)" 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
- Extract answers from web pages by analyzing content with AI. Provide a URL and question to get specific information from the page.MIT
- Test a domain's chat by sending a message that triggers RAG search and LLM generation, mimicking production to verify the knowledge base is functioning correctly.MIT
- Chat with local LLMs using conversation history, system messages, tool calling, and adjustable generation settings.AGPL 3.0
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- Alicense-qualityCmaintenanceEnhances AI model capabilities with structured, retrieval-augmented thinking processes that enable dynamic thought chains, parallel exploration paths, and recursive refinement cycles for improved reasoning.Last updated23MIT
- Alicense-qualityCmaintenanceProvides 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.Last updatedMIT
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- Parse code files into semantic chunks such as functions, classes, and methods to improve retrieval in RAG systems.
- Start a stateful chat session with a Gemini model, returning a unique sessionId for continued interaction. Customize with initial history, generation settings, and safety configurations.MIT
- 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
- Perform semantic search and question-answering on your Obsidian vault using a local retrieval-augmented generation pipeline.MIT
- Delete files from the RAG system to manage storage and maintain relevant content for retrieval-augmented generation tasks.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
- 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
- Execute complete RAG workflows to answer questions using document context. Handles embedding generation, semantic search, and context retrieval automatically for Teradata databases.MIT
- Answer questions about Commodore 64 documentation by retrieving and synthesizing information from multiple sources. Provides answers with citations and confidence scores.
- Query documents with context using a Retrieval-Augmented Generation (RAG) system. Automatically creates an index if it does not exist, enabling quick access to relevant information from stored repositories and text files.
- Generate search results optimized for RAG context by providing relevant web content based on your query, with adjustable depth and result limits.