List all document stores with details for retrieval-augmented generation (RAG) in Flowise.
MIT
650,439 tools. Updated 2026-10-13 06:11
"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
- Answer questions about Commodore 64 documentation by retrieving and synthesizing information from multiple sources. Provides answers with citations and confidence scores.-
- 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
- Find relevant Redis documentation and knowledge base articles by asking questions about concepts, data structures, features, and common use cases like caching, rate limiting, and RAG.MIT
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- 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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About Bureau, the self-hosted office for AI agents: overview, FAQ search, roadmap, install.
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- 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
- 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
- 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
- Ask your codebase questions in natural language and retrieve cited code snippets via local RAG.-
- 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