List all document stores with details for retrieval-augmented generation (RAG) in Flowise.
MIT
650,527 tools. Updated 2026-10-14 05:28
"Information about RAG (Retrieval-Augmented Generation) or rag-related topics" matching MCP tools:
- Ask questions about memory files using retrieval-augmented generation to get answers from stored content with configurable search modes.MIT
- Ask natural-language questions about your browsing history and get AI-powered answers using RAG. Filter results by event type, domain, or time window.MIT
- Ask your codebase questions in natural language and retrieve cited code snippets via local RAG.-
- Retrieve a paginated list of Knowledge AI stores, filtered by name or project, to manage the RAG content used by AI Agents for answering questions.MIT
- Upload files to process and index them for searchable knowledge retrieval using RAG (Retrieval-Augmented Generation) technology.MIT
Matching MCP Servers
- AlicenseBqualityDmaintenanceEnables retrieval-augmented generation by embedding queries with a chosen provider (e.g., OpenAI) and searching supported vector stores (Pinecone, pgvector) to return relevant content.1Apache 2.0
- FlicenseNot gradedqualityDmaintenanceAn MCP server that retrieves relevant PDF chunks via local embeddings and returns them to IDE agents (Cursor, Kiro, Claude Code) for answer generation.-
Matching MCP Connectors
RAG-as-a-service MCP sunucusu — çok-kiracılı koleksiyon yönetimi, metin ingest (chunk+embed+upsert,…
- tw-legal-ragOAuth
Citation-guarded retrieval over 22M Taiwan court judgments and administrative interpretations
- Check whether a project vault is indexed for RAG to confirm readiness for retrieval-augmented generation tasks in agentic workflows.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
- Generate text embeddings to enable semantic search and retrieval-augmented generation (RAG) for AI applications.MIT
- Delete files from the RAG system to manage storage and maintain relevant content for retrieval-augmented generation tasks.MIT
- List knowledge bases used for RAG workflows to identify available datasets for retrieval-augmented generation tasks.MIT
- Scans document text, source, and type to validate and prepare content for retrieval-augmented generation (RAG) ingestion.Apache 2.0
- Discover available knowledge bases for Retrieval-Augmented Generation (RAG) workflows. Lists all document repositories to enable integration into AI applications.MIT
- Answer questions about Commodore 64 documentation by retrieving and synthesizing information from multiple sources. Provides answers with citations and confidence scores.-
- Generate dense vector embeddings for semantic search, similarity comparison, and RAG. Converts text strings into base64-encoded vectors for retrieval applications.-
- Upload a document to the RAG knowledge base for indexing, enabling retrieval-augmented generation queries.Apache 2.0
- Create a named local vector index for retrieval-augmented generation. Documents added are embedded via Ollama for local RAG without cloud dependencies.-
- Retrieve detailed information about a specific RAG project within the Calibre ebook library, including its configuration, contents, and organization for semantic search and contextual conversations.-
- Perform RAG queries on BambiSleep data to retrieve relevant answers from FAQs, sessions, triggers, safety, or transcripts, with configurable result limits.-
- Generate production-ready RAG architecture blueprints with vector DBs (ChromaDB, Qdrant, Pinecone), local LLMs (Ollama, vLLM), and LangGraph/LangChain orchestration for any target use case.MIT