List all document stores with details for retrieval-augmented generation (RAG) in Flowise.
MIT
216,189 tools. Last updated 2026-06-20 07:08
"Kotlin RAG (Retrieval-Augmented Generation) implementation resources" 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.
- Parse code files into semantic chunks such as functions, classes, and methods to improve retrieval in RAG systems.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 natural-language questions about your browsing history and get AI-powered answers using RAG. Filter results by event type, domain, or time window.MIT
Matching MCP Servers
- Alicense-qualityDmaintenanceEnhances 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 updated24MIT
- Alicense-qualityDmaintenanceEnables 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 updatedApache 2.0
Matching MCP Connectors
Medical RAG: semantic search for clinical guidelines, drug interactions, diagnoses & EHR data.
Apple Developer Documentation with Semantic Search, RAG, and AI reranking for MCP clients
- Retrieve relevant UTCP documentation results using semantic search with enhanced relevance scoring and query expansion. Provides detailed match reasons for sophisticated search needs.MIT
- Stores a knowledge fragment with source and evidence tier metadata for future retrieval via semantic RAG queries.MIT
- Parse code files into semantic chunks such as functions, classes, and methods to enhance RAG retrieval.MIT
- Parse code files into semantic chunks like functions, classes, and methods to improve RAG retrieval.MIT
- Generate 768-dimensional embedding vectors for retrieval-augmented generation (RAG). Supports single text or batch input, with a pay-per-call cost of 2 sats.MIT
- Run a complete RAG pipeline that retrieves chunks, generates answers, and scores them on context relevance and citation faithfulness. Returns per-query and aggregate metrics.MIT
- Execute RAG queries against Amazon Bedrock Knowledge Bases to retrieve relevant documents using vector search for enhanced information retrieval.MIT
- Upload files to process and index them for searchable knowledge retrieval using RAG (Retrieval-Augmented Generation) technology.MIT
- Search Google and retrieve relevant results with similarity-based ranking for retrieval-augmented generation.PythonMIT
- Search uploaded documents using RAG to find answers with citations. Ask questions to retrieve information from your knowledge base.MIT
- Perform semantic search and question-answering on your Obsidian vault using a local retrieval-augmented generation pipeline.MIT
- Generate text embeddings to enable semantic search and retrieval-augmented generation (RAG) for AI applications.MIT
- Delete files from the RAG system to manage storage and maintain relevant content for retrieval-augmented generation tasks.MIT
- Add files to a RAG system for document retrieval, supporting PDF, DOCX, TXT, MD, CSV, and JSON formats to enable semantic search and information access.MIT