List all document stores with details for retrieval-augmented generation (RAG) in Flowise.
MIT
743,120 tools. Updated 2026-11-04 19:04
"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
- Perform RAG queries on BambiSleep data to retrieve relevant answers from FAQs, sessions, triggers, safety, or transcripts, with configurable result limits.MIT
- Create a named local vector index for retrieval-augmented generation. Documents added are embedded via Ollama for local RAG without cloud dependencies.-
- List existing knowledge bases (documents, FAQs, web content) to prevent duplicates and find knowledge_base_ids for agent attachment.MIT
- Retrieve Google Trends keyword analytics: interest over time, by region, related queries, and topics.MIT
Matching MCP Servers
- AlicenseBqualityDmaintenanceEnhances AI model capabilities with structured, retrieval-augmented thinking processes that enable dynamic thought chains, parallel exploration paths, and recursive refinement cycles for improved reasoning.124MIT
- 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.112 PyPIApache 2.0
Matching MCP Connectors
The topics in a set of documents in one call, with each document's topic mixture. Nothing stored.
Crypto scam checks (tokens, sites, wallets), chain reads, web page to Markdown, email checks.
- Groups related keywords into topic clusters, generating candidate pillar topics for SEO content strategy.MIT
- Get curated working imports, snippets, and migration notes for LangChain, LlamaIndex, Ollama, or XRPL by framework and topic. Use when writing or migrating; unknown topics fall back to related docs.MIT
- Generate token-sized chunks from text, Markdown, or files for retrieval-augmented generation (RAG) and embeddings, preserving headings, paragraphs, sentence boundaries, overlap, and offsets.MIT
- Generate numerical vector embeddings for text inputs, enabling semantic search, clustering, deduplication, or retrieval-augmented generation. Returns one embedding array per text in input order.MIT
- Create a new vector store to organize documents for semantic search and RAG. Attach files, set expiration, and configure chunking strategy.MIT
- Find related Grokipedia articles linked from a specific page to explore connected topics, build knowledge graphs, or conduct follow-up research.MIT
- Extract answers from web pages by analyzing content with AI. Provide a URL and question to get specific information from the page.MIT
- Discover related Wikipedia topics for any article using its links and categories. Adjust the limit to control the number of results.MIT
- Ask questions and get referenced answers from your NotebookLM notebook sources, using retrieval-augmented generation to ground responses in your selected documents.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
- Ask questions about your entire video and memory library. Combines video understanding with text memories to provide comprehensive answers.MIT
- Generate context-aware embeddings for document chunks by considering surrounding text, improving retrieval-augmented generation (RAG) accuracy.MIT
- Aggregate ranked code retrieval, structure-backed related files, subsystem summaries, and relevant hubs into a bounded report for broad subsystem understanding when exact lookup is insufficient.MIT