All 155 Dependencies
Every package knowhere depends on, ranked by repo health score.
Knowhere is an open-source document memory infrastructure stack that sits between raw files and AI agents. Instead of dumping flat text at an LLM, Knowhere ingests PDFs, Office files, images, and markdown, then reconstructs their full hierarchical structure using a proprietary tree-building algorithm — preserving headings, sections, tables, and cross-document relationships as a navigable memory graph.
The platform runs in two stages: build and retrieve. During the build phase, documents are routed to specialized parsers (defaulting to MinerU for PDFs), then a multi-pass document agent using a ReAct-style loop analyzes page anatomy, detects table-of-contents structures, assigns heading levels, and organizes chunks with full section-path context. During retrieval, a hybrid engine fuses keyword (BM25), path, semantic, and vector channels using Reciprocal Rank Fusion, then an LLM-driven navigation agent walks the section tree to drill into the most relevant regions.
Knowhere exposes its retrieval engine as an MCP (Model Context Protocol) server, making it natively compatible with Claude, Cursor, and other agentic tool frameworks. Every result carries traceable source paths — document, section, chunk, and linked assets — so downstream agents can cite evidence rather than hallucinate. Internal benchmarks show +36% first-try accuracy and +11% recall over feeding raw documents directly to agents.
The full stack is self-hostable via Docker Compose, with official Python and Node.js SDKs available for cloud API access. A companion dashboard, worker service, and shared infrastructure package ship as separate repositories in the Ontos-AI ecosystem, all orchestrated by a uv workspace.
