List all document stores with details for retrieval-augmented generation (RAG) in Flowise.
MIT
397,122 tools. Last updated 2026-08-05 15:18
"Information about RAG (Retrieval-Augmented Generation)" 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
- Retrieve a paginated list of Knowledge AI stores, filtered by name or project, to manage the RAG content used by AI Agents for answering questions.MIT
- List documents and web pages ingested into a knowledge store for RAG retrieval, with pagination and filtering.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.Last updated124MIT
- 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
Local-first RAG engine with MCP server for AI agent integration.
Citation-guarded retrieval over 22M Taiwan court judgments and administrative interpretations
- Retrieve text segments (chunks) from a knowledge store for RAG retrieval, with optional pagination, filtering by text or source.MIT
- Parse code files into semantic chunks such as functions, classes, and methods to improve retrieval in RAG systems.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 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
- 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
- Ask your codebase questions in natural language and retrieve cited code snippets via local RAG.
- Upload files to process and index them for searchable knowledge retrieval using RAG (Retrieval-Augmented Generation) technology.MIT