List all document stores with details for retrieval-augmented generation (RAG) in Flowise.
MIT
510,057 tools. Updated 2026-09-03 19:28
"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.
- Get Google Trends keyword analytics, including interest over time, by region, related queries, and related topics.MIT
- Fetch related topics for a Google Trends explore request. Returns the related topics widget data, or an empty array when Google provides no topic rows for the term/filter combination.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
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.1Apache 2.0
Matching MCP Connectors
Search public Australian environmental evidence with provenance across authoritative catalogues.
RAG-as-a-service MCP sunucusu — çok-kiracılı koleksiyon yönetimi, metin ingest (chunk+embed+upsert,…
- List existing knowledge bases (documents, FAQs, web content) to prevent duplicates and find knowledge_base_ids for agent attachment.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
- Groups related keywords into topic clusters, generating candidate pillar topics for SEO content strategy.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
- Extract answers from web pages by analyzing content with AI. Provide a URL and question to get specific information from the page.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 a natural language question about an indexed repository and get an answer derived from relevant code context using retrieval-augmented generation.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
- 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
- Create a new RAG knowledge base collection for organizing documents. Populate it with files using separate upload tools for retrieval-augmented generation.MIT
- Rerank retrieved passages by relevance to a query using NVIDIA NIM models. Improves RAG pipeline retrieval quality by reordering results with a reranking model.MIT