List all document stores with details for retrieval-augmented generation (RAG) in Flowise.
MIT
260,585 tools. Last updated 2026-07-05 07:35
"Understanding RAG (Retrieval-Augmented Generation or 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
- Create a named local vector index for retrieval-augmented generation. Documents added are embedded via Ollama for local RAG without cloud dependencies.
- Execute a complete retrieval-augmented generation workflow to answer user questions using document context, automatically handling embedding, semantic search, and strict context-grounded responses.MIT
- Parse code files into semantic chunks such as functions, classes, and methods to improve retrieval in RAG systems.MIT
- Retrieve related topics from Google Trends Explore widget for any search term and filter combination. Returns a JSON array of related topics or an empty array.MIT
Matching MCP Servers
- Alicense-qualityDmaintenanceEnhances 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 updated24MIT
- 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
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
- Search and filter RAG-capable MCP servers by query, categories, score, transport, and other criteria to find the right retrieval server for your task.MIT
- Create a new vector store to organize documents for semantic search and RAG. Attach files, set expiration, and configure chunking strategy.MIT
- Extract answers from web pages by analyzing content with AI. Provide a URL and question to get specific information from the page.MIT
- Stores a knowledge fragment with source and evidence tier metadata for future retrieval via semantic RAG queries.MIT
- Retrieve relevant UTCP documentation results using semantic search with enhanced relevance scoring and query expansion. Provides detailed match reasons for sophisticated search needs.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
- Parse code files into semantic chunks such as functions, classes, and methods to enhance RAG retrieval.MIT
- Generate text embeddings for retrieval-augmented generation, semantic search, and clustering. Each request costs $0.005 USDC.MIT
- Creates or updates a node in a persistent graph memory for long-term RAG retrieval. Requires initialized NFT matrix; if missing, purchase license key first.Business Source 1.1
- Parse code files into semantic chunks like functions, classes, and methods to improve RAG retrieval.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
- Run a complete RAG pipeline that retrieves chunks, generates answers, and scores them on context relevance and citation faithfulness. Returns per-query and aggregate metrics.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