List all document stores with details for retrieval-augmented generation (RAG) in Flowise.
MIT
306,355 tools. Last updated 2026-07-25 08:31
"Tools for Automatically Indexing Code Files and RAG (Retrieval-Augmented Generation)" matching MCP tools:
- Upload files to process and index them for searchable knowledge retrieval using RAG (Retrieval-Augmented Generation) technology.MIT
- Ask questions about memory files using retrieval-augmented generation to get answers from stored content with configurable search modes.MIT
- Upload a document to the RAG knowledge base for indexing, enabling retrieval-augmented generation queries.Apache 2.0
- 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
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
- 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.Last updated1Apache 2.0
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Cloudflare Workers MCP server: code-explainer
- Generate or modify code files with smart diffs. Provide file path and detailed prompt to create or edit files using context for accurate code generation.MIT
- Scrape a web page, embed the content, and store vectors in your vector database in a single call. Add web data to your RAG pipeline for retrieval.MIT
- Scrape a web page, divide its content into structured text chunks optimized for RAG retrieval, and return them ready for downstream processing. No embedding or vector database required.MIT
- Scan markdown files, chunk and embed them into a searchable index for semantic retrieval. Only re-processes changed files to save time.MIT
- Search for AI workflows by name, description, or keywords to find tools for image generation, text processing, and more.MIT
- Parse code files into semantic chunks such as functions, classes, and methods to improve retrieval in RAG systems.MIT
- Submit a URL to Google Indexing API for crawl notification. Specify whether the URL was updated or deleted. Automatically retries transient errors.MIT
- Create a searchable index from code and documentation to enable semantic search and cross-references across your codebase.MIT
- Convert PDF files to structured Markdown, fixed-size token chunks, or heading-aware semantic chunks for retrieval-augmented generation. Supports encrypted PDFs and custom chunk overlap.MIT
- Analyze git diff between two refs to map changed files to wiki pages and code entities, or answer questions about the diff using RAG.MIT
- Preview indexing scope: list files and directories that would be indexed, without performing the actual indexing.Apache 2.0
- Report daemon startup, scan, and realtime indexing health to confirm completion and debug degraded behavior without log files.MIT
- Analyzes Python repositories to report test-to-code ratio, match test files to source modules, count assertions per test, and identify untested source files without prior indexing.MIT
- Analyze AI agent session logs to retrieve token usage, cost breakdown by tool/server, top files, and models used. Parses Claude Code JSONL logs automatically.MIT