Knowhere is a document parsing and retrieval system that transforms unstructured documents into structured chunks ready for AI agents and retrieval-augmented generation pipelines.
The tool addresses the challenge of preparing messy, complex documents for use by AI systems. It ingests unstructured files and produces persistent, navigable memory through parsing, hierarchy reconstruction, multi-modal structuring, and graph construction in a single pipeline. Every extracted element maintains connections to its source document, section, page numbers, and related assets. The system offers complementary Vision and Text processing tracks: text-native documents are parsed to preserve precise extracted structure, while complex PDFs and PowerPoint files can be processed directly as pages by vision models. Both tracks converge into a unified memory schema, retrieval engine, and citation model.
Knowhere suits teams building agentic RAG systems or vector-based RAG pipelines that need reliable document understanding across local and offline collections. It is particularly valuable when working with complex, multi-format document sets where maintaining provenance and structure matters for agent reasoning and citation accuracy. The project provides both a managed cloud API and self-hosted deployment options, allowing teams to choose between avoiding deployment complexity or maintaining full control over their document processing infrastructure.
The project maintains active development with passing continuous integration checks and an open community discussion space. Container images are published for deployment flexibility. The codebase requires Python 3.11 or later and is distributed under an open-source license, making it accessible for both commercial and community use.
