List all document stores with details for retrieval-augmented generation (RAG) in Flowise.
MIT
730,291 tools. Updated 2026-10-31 20:15
"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
- Discover available knowledge bases for Retrieval-Augmented Generation (RAG) workflows. Lists all document repositories to enable integration into AI applications.MIT
- Create a named local vector index for retrieval-augmented generation. Documents added are embedded via Ollama for local RAG without cloud dependencies.-
- Create a searchable knowledge base from plain text, automatically chunked and indexed for RAG retrieval. Ideal for FAQs, product specs, and policies.MIT
- List existing knowledge bases (documents, FAQs, web content) to prevent duplicates and find knowledge_base_ids for agent attachment.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
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- Fetch the Phaset integration API schema (RecordUpdate structure) needed to generate accurate manifest files for your integration.MIT
- Perform semantic search over a stored memory namespace to retrieve the most relevant entries. Use this to recall context for RAG pipelines.MIT
- Find the right integration tool for any task by searching available actions with semantic search. Describe what you want to do and get relevant tool suggestions.MIT
- Connect a GitHub repository as an integration to enable content generation from its files.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
- Generate embedding vectors for semantic search, RAG retrieval, and similarity scoring using IBM Granite models. Supports up to 64 texts per call with no IBM Cloud account required.Apache 2.0
- 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 natural-language questions about your browsing history and get AI-powered answers using RAG. Filter results by event type, domain, or time window.MIT
- Generate context-aware embeddings for document chunks by considering surrounding text, improving retrieval-augmented generation (RAG) accuracy.MIT
- Create a new RAG knowledge base collection for organizing documents. Populate it with files using separate upload tools for retrieval-augmented generation.MIT