With 2,600+ GitHub stars since its May 2026 open-source launch, Knowhere solves the last-mile problem of document intelligence for AI systems — transforming complex unstructured PDFs, reports, and multi-page documents into structured JSON chunks that LLMs can consume without hallucination. The platform processes documents through an AI-native parsing pipeline that handles 20+ page documents with deep hierarchies, intricate tables, and multimodal content including images with OCR, achieving 95% precision in information extraction while reducing token costs by 50% compared to raw document ingestion. The knowledge tree architecture maintains historical context across multiple documents, enabling cross-document graph navigation for agentic retrieval that goes beyond simple chunk-based RAG. Built on Python 3.11+ with MinerU as the default PDF parser, the backend API runs alongside async workers that process document ingestion, graph construction, and embedding generation. The self-hosted Docker Compose stack packages the API server, processing workers, and Next.js dashboard for managing API keys, webhooks, and document-processing jobs, backed by PostgreSQL and Redis. Both Python and Node.js SDKs provide programmatic access for integration into existing AI pipelines and agent frameworks. LLM providers include DeepSeek and Alibaba Cloud DashScope with configurable key rotation for rate-limit management. Deploy on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache 2.0 licensed.

Docling Studio shows you exactly what a document parser sees, drawing a colored box around every title, paragraph, table, and picture it extracts from your PDFs. Import a PDF, run an analysis, and the result opens with three linked panels: the document tree on the left, the paged preview in the center, and the selected element on the right with its type, page, position, and text or table. Click any box on the page and the same element lights up in the tree and properties panel as other boxes fade away. The layers bar counts each element type and toggles boxes per type, or hides them all to reveal the bare page. You can analyze several documents at once from the list, watch progress percentages on long files, and queue work three analyses at a time. Ask a question and a local model answers from the document, with a reasoning timeline that links each step back to the passage it read. Finished results download as the original PDF, clean Markdown, or Docling JSON for downstream pipelines. AI agents connect over MCP to read and cite your documents, and an optional connection to your own Docling Serve server handles heavier parsing loads. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.