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Build a local semantic memory from documents for search and graph exploration.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "vkb" yet — see the docs or source repo.
Please ingest my local papers, meeting notes, and PDFs into vkb, build a searchable semantic memory, and answer: "What are the main multimodal retrieval approaches from the past two years?"
Returns semantic search results and a summarized answer grounded in the ingested materials, with relevant document references.
Ingest PRDs, user feedback, and iteration logs into vkb, then trace through the knowledge graph to find features, issues, and owners related to "declining signup conversion."
Outputs entity relationships connected to the target issue, helping identify possible causes and ownership.
Ingest the team's technical docs, runbooks, and incident reviews into vkb, then answer: "Which dependencies and logs should be checked first when service startup fails?"
Provides structured troubleshooting guidance based on the local knowledge base, with references to relevant source documents.
Parse code into structured knowledge for search, specs, and migration analysis.
Build an intelligent knowledge base with semantic search and reasoning.
Build a project knowledge graph for code search, traversal, and Q&A.
Search markdown knowledge bases with hybrid ranking and intelligent reranking.
Connect Obsidian vaults for local-first AI memory search and knowledge management.
Unified gateway for semantic search, graph queries, and knowledge routing.