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Query a knowledge corpus through unified vector and graph retrieval tools.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "kb-gateway" yet — see the docs or source repo.
Use route_query to find the core concepts, common architectures, and best practices for 'retrieval-augmented generation' in the knowledge base, and group the results by theme.
A theme-based knowledge summary covering concepts, relevant materials, and key takeaways.
Use query_namespace to search for 'Transformer attention mechanism' in the 'ml-course-notes' namespace, extract the 5 most relevant items, and provide a brief summary.
A list of highly relevant results from the specified namespace with concise summaries.
Use graph_query to analyze the relationships among 'vector databases', 'knowledge graphs', 'semantic search', and 'RAG' in the knowledge graph, and explain their connection paths.
An explanation of entity relationships, key connection paths, and a brief interpretation of the overall knowledge structure.
Query a learning corpus and knowledge graph through a unified MCP gateway.
Route AI agents to the right MCP tools with MongoDB smart search.
Let AI agents search an ArXiv paper knowledge base with natural language.
Give AI agents local persistent memory with searchable cross-session knowledge storage.
Turn unstructured text into a searchable knowledge graph memory system.
Ingest documents into Neo4j to build and query a knowledge graph.