{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/8"}],"enrichment":{"capability":"ColPali-engine provides training and inference code for vision-language document retrieval models that create multi-vector embeddings from document images using visual transformers, enabling efficient document search without OCR.","skillfed_tags":["document-retrieval","vision-language-models","information-retrieval"],"use_cases":["Build a document search engine that retrieves pages from PDFs or scanned documents based on natural language queries without OCR","Index and retrieve technical documentation, research papers, or forms where layout and visual structure matter for understanding","Create a multilingual document retrieval system using models with support across multiple languages","Fine-tune a pre-trained model on domain-specific documents using the training utilities and LoRA support","Deploy efficient document ranking in production with optional fused MaxSim kernels for reduced memory usage"],"what_it_does":"ColPali-engine is a PyTorch-based library for training and running inference with vision-language document retrieval models. It implements the ColPali architecture and variants (ColQwen, ColSmol, etc.) that convert document images into multi-vector embeddings using visual transformers, enabling efficient semantic search over documents without requiring separate OCR or layout recognition pipelines. The library depends on numpy, scipy, torch, torchvision, transformers, pillow, peft, and requests.\n\nThe package is designed for developers and researchers building document retrieval systems. It supports multiple pre-trained model variants with different performance-efficiency tradeoffs, from small models (256M parameters) to larger ones (4.5B+). The core approach follows ColBERT's late-interaction ranking method adapted to the visual domain, allowing both the textual and visual content (layout, charts, images) of documents to influence retrieval scoring.","worth_installing":"Yes. Active maintenance, permissive MIT license, low install friction, no known vulnerabilities, and a focused scope for vision-based document retrieval make it a solid choice. Install if you need to search documents visually without OCR pipelines; skip if you only work with plain text or have existing OCR infrastructure."},"id":"colpali-engine","links":{"html":"https://skillfed.io/packages/colpali-engine","md":"https://skillfed.io/packages/colpali-engine.md","pypi":"https://pypi.org/project/colpali-engine/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-08","license_spdx":null,"license_treatment":"permissive","name":"colpali-engine","python_support":"supports_current","summary":"The code used to train and run inference with the ColPali architecture."},"popularity":{"monthly_downloads":154293,"position":10856,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.3.17"}