List all document stores with details for retrieval-augmented generation (RAG) in Flowise.
MIT
306,549 tools. Last updated 2026-07-25 13:02
"RAG (Retrieval-Augmented Generation) system that can store papers and web pages" 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.
- Retrieve relevance-ranked web content with actual page text, tables, and code for AI grounding and RAG pipelines.MIT
- 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
- Scrape a web page, embed the content, and store vectors in your vector database in a single call. Add web data to your RAG pipeline for retrieval.MIT
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- 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
tldr-pages community simplified man pages (cached 24h)
send-that-email MCP — wraps StupidAPIs (requires X-API-Key)
- Scrape a web page, divide its content into structured text chunks optimized for RAG retrieval, and return them ready for downstream processing. No embedding or vector database required.MIT
- Parse code files into semantic chunks such as functions, classes, and methods to improve retrieval in RAG systems.MIT
- Store typed knowledge (observation, decision, learning, error, pattern) with tags, importance, and optional relation. Embeddings enable semantic retrieval.MIT
- Fetch any public web page and extract its readable content as clean markdown. Respects robots.txt, handles redirects, ideal for research and RAG.MIT
- Convert PDF files to structured Markdown, fixed-size token chunks, or heading-aware semantic chunks for retrieval-augmented generation. Supports encrypted PDFs and custom chunk overlap.MIT
- Store a dialog turn with role, content, and metadata to persistent session memory for later retrieval and context assembly.MIT
- Convert web pages to clean markdown format by extracting content, removing unnecessary elements, and ranking information for RAG applications.MIT
- Convert any permitted public web page into clean Markdown for AI agents or note-taking. Returns explicit errors for blocked, paywalled, or login-gated pages.MIT
- Given a URL, extracts the page title, readable markdown, and plain text. Helps AI agents read web content, scrape data, and feed RAG pipelines.MIT
- Create a knowledge base in Amazon Bedrock to store and organize documents for retrieval-augmented generation (RAG), supporting vector search, custom parsing, and chunking strategies.MIT
- Create a new vector store to organize documents for semantic search and RAG. Attach files, set expiration, and configure chunking strategy.MIT
- Access the LightRAG Web UI and API documentation to run retrieval-augmented generation queries on your codebase.MIT
- Retrieve top-k passages from a sample RAG corpus using various retrieval methods to see what context a RAG system would surface for a query.MIT
- Scrape a web page, generate embeddings, and sync the vectors to your vector database for retrieval. Supports multiple embedding and vector DB providers.MIT