List all document stores with details for retrieval-augmented generation (RAG) in Flowise.
MIT
254,253 tools. Last updated 2026-07-01 07:04
"RAG (Retrieval-Augmented Generation) MCP Integration for ChatGPT" 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.
- Parse code files into semantic chunks such as functions, classes, and methods to improve retrieval in RAG systems.MIT
- Record retrieval queries, chunks, scores, sources, and rankings for later audit and diff workflows.MIT
- 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
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
- 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
Matching MCP Connectors
Thin remote MCP server for Socializioz phase-1 ChatGPT workflows.
The PropelAuth Integration MCP Server helps you and your favorite AI agent integrate PropelAuth as quickly and easily as possible into your project. Whether you're integrating PropelAuth into your Next.js project or your FastAPI backend, the Integration MCP Server will ensure your AI agent has the best context possible for a successful integration.
- 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
- Retrieve paginated MCP integration records for your organization, including total count and pagination indicators to discover integration IDs.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