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Manage knowledge bases and semantic retrieval workflows through the RAGFlow API.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "RAGFlow MCP Server" yet — see the docs or source repo.
Use the RAGFlow MCP Server to create a product documentation dataset, import these PDF and Markdown files, split them into chapter-based chunks, and return a summary of the indexing status for semantic retrieval.
Returns the dataset creation result, document import and chunking status, and a summary of the searchable index.
Search the existing RAGFlow dataset for “how to configure enterprise permission management,” return the most relevant document chunks, and generate a concise answer with cited sources.
Outputs relevant chunks, source document details, and a grounded answer with citations based on retrieval results.
Use the RAGFlow MCP Server to list the documents and chunk counts in a target dataset, remove duplicate documents, and check whether the corresponding knowledge graph nodes and relationship overview have been generated.
Returns document statistics, cleanup results, and the knowledge graph build status or a summary of nodes and relationships.
Manage RAGFlow datasets and create retrieval-augmented chat assistants via API.
Turn unstructured documents into a searchable knowledge base for AI agents.
Centralize knowledge, run semantic search, ingest documents, and generate RAG answers.
Build private local RAG search and Q&A over personal documents.
Ingest documents into Neo4j to build and query a knowledge graph.
Index a knowledge base into Chroma and retrieve relevant document fragments.