{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/7"}],"enrichment":{"capability":"Provides a lightweight Python wrapper to interact with the MinerU Vision-Language Model, supporting multiple deployment backends for document layout detection and content recognition.","skillfed_tags":["document-extraction","vision-language-model","multimodal-inference"],"use_cases":["Extract structured text, tables, and equations from scanned documents or PDFs for downstream processing.","Detect and localize text regions in document images for document layout analysis.","Convert document images to structured HTML or LaTeX for archival or republishing workflows.","Run document understanding on Apple Silicon Macs without external servers using the mlx-engine backend.","Build a document processing service using the http-client backend to call a centralized model server."],"what_it_does":"mineru-vl-utils is a client library for the MinerU Vision-Language Model, a multimodal AI system that detects document layout and recognizes text, tables, equations, and images from visual input. It abstracts away the complexity of model serving and inference by providing a unified MinerUClient interface that works across seven different deployment modes: http-client (remote server), transformers (HuggingFace), mlx-engine (Apple Silicon), lmdeploy-engine, vllm-engine (synchronous), vllm-async-engine (asynchronous), and llama-cpp-engine (in-process). The model outputs structured ContentBlock objects containing block type, bounding box, rotation angle, and recognized content (text as strings, tables as HTML, equations as LaTeX).\n\nYou use it by instantiating a MinerUClient with your chosen backend, then calling two_step_extract() on images to get back a list of detected and recognized content blocks. The package handles all the marshalling between your code and the underlying model, whether that model runs locally via transformers, on Apple Silicon via mlx-engine, in a remote HTTP service, or in-process via llama-cpp-engine. Installation is modular: the base package includes http-client support, and optional extras pull in backend-specific dependencies.","worth_installing":"Yes. The package is actively maintained (released one day ago), has low install friction, carries a permissive Apache-2.0 license, and offers a clean abstraction over a capable multimodal model with flexible deployment options. Choose it if you need to extract structured content from documents and want to avoid managing model serving yourself; the http-client backend requires an external server, but other backends let you run inference locally."},"id":"mineru-vl-utils","links":{"html":"https://skillfed.io/packages/mineru-vl-utils","md":"https://skillfed.io/packages/mineru-vl-utils.md","pypi":"https://pypi.org/project/mineru-vl-utils/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-13","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"mineru-vl-utils","python_support":"supports_current","summary":"Utilities for MinerU Vision-Language models"},"popularity":{"monthly_downloads":198484,"position":9729,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.2.1"}