List all document stores with details for retrieval-augmented generation (RAG) in Flowise.
MIT
235,598 tools. Last updated 2026-06-25 16:05
"Information about RAG (Retrieval-Augmented Generation) or rag-related topics" matching MCP tools:
- 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
- Upload files to process and index them for searchable knowledge retrieval using RAG (Retrieval-Augmented Generation) technology.MIT
- Generate 768-dimensional embedding vectors for retrieval-augmented generation (RAG). Supports single text or batch input, with a pay-per-call cost of 2 sats.MIT
- Ask questions about memory files using retrieval-augmented generation to get answers from stored content with configurable search modes.MIT
- Search uploaded documents using RAG to find answers with citations. Ask questions to retrieve information from your knowledge base.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.Last updated1Apache 2.0
- Flicense-qualityCmaintenanceAn MCP server that retrieves relevant PDF chunks via local embeddings and returns them to IDE agents (Cursor, Kiro, Claude Code) for answer generation.Last updated
Matching MCP Connectors
Medical RAG: semantic search for clinical guidelines, drug interactions, diagnoses & EHR data.
Apple Developer Documentation with Semantic Search, RAG, and AI reranking for MCP clients
- 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
- Answer questions about Commodore 64 documentation by retrieving and synthesizing information from multiple sources. Provides answers with citations and confidence scores.
- 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 the current Retrieval-Augmented Generation (RAG) configuration settings from OpenWebUI.MIT
- Retrieve relevance-ranked web content with actual page text, tables, and code for AI grounding and RAG pipelines.
- Find diverse nearest neighbors by balancing relevance and diversity, reducing redundant results. Ideal for RAG pipelines needing broad coverage.Apache 2.0
- Retrieve detailed information about any Apify Actor, including description, input schema, pricing, stats, and README, using its ID or full name.MIT
- Clear cached RAG retrievals to force fresh query results after context changes. Next query rebuilds from source, avoiding stale data.MIT
- Execute a complete RAG workflow to answer questions using retrieved context documents. Handles embedding, semantic search, and answer generation with direct quotes.MIT
- Retrieve wiki and documentation knowledge graph nodes and sources for a project. Merge with codebase context to enhance RAG accuracy.MIT