List all document stores with details for retrieval-augmented generation (RAG) in Flowise.
MIT
604,423 tools. Updated 2026-09-23 19:45
"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.-
- Index a dataset so text chunks become searchable. Splits content into RAG-ready chunks and generates embeddings for semantic retrieval.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
Decision Layer for AI Agents — 58+ tools, Advisor, MCP. Free key: POST /v1/register {}.
Search
- Check whether a project is indexed by viewing counts of files, symbols, dependency edges, and index.db size. Zero counts signal that indexing must run before graph tools return results.MIT
- Upload files to a durable knowledge store for indexing, so they become searchable across RAG queries. Provide a bucket destination and file content either by local path or base64.MIT
- Keep RAG search current by re-indexing catalog schema after changes. Uses schema hashing to skip unchanged schemas.Apache 2.0
- Create a searchable knowledge base from plain text, automatically chunked and indexed for RAG retrieval. Ideal for FAQs, product specs, and policies.MIT
- List existing knowledge bases (documents, FAQs, web content) to prevent duplicates and find knowledge_base_ids for agent attachment.MIT
- Upload 1–100 knowledge files in one request to the Calypso RAG knowledge store for bulk indexing, with batch status polling until ready.MIT
- Retrieve author-written Agent Skills for an LPM package, including usage patterns, anti-patterns, gotchas, and best practices for code generation. Skills are version-specific and resolve from local package.json.ISC
- Perform semantic search over a stored memory namespace to retrieve the most relevant entries. Use this to recall context for RAG pipelines.MIT
- Register a GitHub repository as a documentation source for RAG indexing. Specify repo, branch, and file patterns, then run ingestion to make the docs searchable.Apache 2.0
- 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
- Index a GitHub repository to enable search, or update an existing index. After indexing, files become queryable.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
- Analyze code and infrastructure files for security vulnerabilities. Creates temporary files, runs Symbiotic CLI scan, and cleans up.MIT
- Extract structured data from PDFs, images, or Office files into Markdown or spatial JSON with bounding boxes, confidence scores, and reading order—from a local file or URL—for RAG and search indexing.MITDestructive