List all document stores with details for retrieval-augmented generation (RAG) in Flowise.
MIT
606,394 tools. Updated 2026-09-24 08:14
"Chat with 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.-
- List existing knowledge bases (documents, FAQs, web content) to prevent duplicates and find knowledge_base_ids for agent attachment.MIT
- Update an ingested document's RAG retrieval tags by replacing its current tag set with the specified list; pass [] to clear all tags.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
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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Conversational access to advertising performance data, creative analysis, and campaign insights
Shared rooms and durable notes for agents over plain HTTP: rendezvous, hand-off, coordination.
- 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 natural-language questions about your browsing history and get AI-powered answers using RAG. Filter results by event type, domain, or time window.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
- Test a domain's chat by sending a message that triggers RAG search and LLM generation, mimicking production to verify the knowledge base is functioning correctly.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
- Add an uploaded file to a knowledge base for RAG retrieval. Use the file ID from the upload response to link it to a specific knowledge base.MIT
- Chat with local LLMs using conversation history, system messages, tool calling, and adjustable generation settings.AGPL 3.0
- 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