List all document stores with details for retrieval-augmented generation (RAG) in Flowise.
MIT
467,125 tools. Updated 2026-08-20 10:31
"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.
- 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
- List existing knowledge bases (documents, FAQs, web content) to prevent duplicates and find knowledge_base_ids for agent attachment.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
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
Matching MCP Connectors
RAG-as-a-service MCP sunucusu — çok-kiracılı koleksiyon yönetimi, metin ingest (chunk+embed+upsert,…
B2B and local lead gen: verified emails, site contacts, Maps and Yellow Pages leads.
- List documents and web pages ingested into a knowledge store for RAG retrieval, with pagination and filtering.MIT
- Retrieve text segments (chunks) from a knowledge store for RAG retrieval, with optional pagination, filtering by text or source.MIT
- Create a minimal Kotlin Android project in an empty directory with Gradle build files, manifest, Activity, resources, README, and gitignore — ready to build immediately if Gradle is installed.MIT
- Search official Android, Kotlin, and AOSP documentation for accurate excerpts with source URLs. Required before generating any Android/Kotlin code to ensure up-to-date information.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
- Create a new RAG knowledge base collection for organizing documents. Populate it with files using separate upload tools for retrieval-augmented generation.MIT
- Run hybrid BM25, symbol, path, and exact-match retrieval over code chunks to answer implementation, behavior, error, and call-site questions.MIT
- Rerank passages by relevance to a query using NVIDIA NIM models to improve RAG retrieval quality.MIT
- Parse code files into semantic chunks such as functions, classes, and methods to enhance RAG retrieval.MIT