ColPali: Efficient Document Retrieval with Vision Language Models
Overview
ColPali (arXiv: https://arxiv.org/abs/2407.01449) proposes performing document retrieval by directly embedding images of document pages, instead of extracting text through lengthy and brittle OCR/parsing pipelines. The work was released on 2024-06-27 by Manuel Faysse, Hugues Sibille, Tony Wu, Bilel Omrani, Gautier Viaud, Céline Hudelot, et al. (7 authors total).
Motivation
Documents are visually rich structures that convey information through text, but also figures, page layouts, tables, or even fonts. Modern retrieval systems mainly rely on textual information extracted from pages, and thus fail to exploit key visual cues efficiently — limiting applications such as Retrieval Augmented Generation (RAG).
Contributions
- ViDoRe benchmark: The Visual Document Retrieval Benchmark, composed of page-level retrieval tasks spanning multiple domains, languages, and practical settings.
- ColPali model: A Vision Language Model trained to produce high-quality multi-vector embeddings directly from page images.
- Late interaction matching: Combined with ColPali embeddings, it largely outperforms modern document retrieval pipelines while being drastically simpler, faster, and end-to-end trainable.
- Open release: Models, data, code, and benchmarks are available under open licenses at https://hf.co/vidore.
- Latency: define p99 budgets, use cascaded retrieval with early stopping, cache hot queries, asynchronous reranking.
- Quality: verify that offline metric gains transfer to online satisfaction; run interleaved experiments and human audits.
- Safety: open retrieval may introduce poisoning/bias; use source whitelists, adversarial detection, output filtering.
- Cost: route to smaller models, use distillation, hybrid sparse+dense retrieval.
- ColPali: Efficient Document Retrieval with Vision Language Models — https://arxiv.org/abs/2407.01449
- Models, data, code, benchmarks: https://hf.co/vidore
- Cross-Modal Retrieval: A Systematic Review of Methods and Future Directions
- Clotho-AQA: A Crowdsourced Dataset for Audio Question Answering (arXiv:2204.09634)
- EA-VTR: Event-Aware Video-Text Retrieval (ECCV 2024)
- Generative Cross-Modal Retrieval: Memorizing Images in Multimodal LMs (arXiv:2402.10805)
- Hybrid-Vector Retrieval for Visually Rich Documents (arXiv:2510.22215)
- Listen, Think, and Understand / OpenAQA dataset (arXiv:2305.10790)
Original Abstract
> Documents are visually rich structures that convey information through text, but also figures, page layouts, tables, or even fonts. Since modern retrieval systems mainly rely on the textual information they extract from document pages to index documents — often through lengthy and brittle processes — they struggle to exploit key visual cues efficiently. This limits their capabilities in many practical document retrieval applications such as Retrieval Augmented Generation (RAG). To benchmark current systems on visually rich document retrieval, we introduce the Visual Document Retrieval Benchmark ViDoRe, composed of various page-level retrieval tasks spanning multiple domains, languages, and practical settings. The inherent complexity and performance shortcomings of modern systems motivate a new concept: doing document retrieval by directly embedding the images of the document pages. We release ColPali, a Vision Language Model trained to produce high-quality multi-vector embeddings from images of document pages. Combined with a late interaction matching mechanism, ColPali largely outperforms modern document retrieval pipelines while being drastically simpler, faster and end-to-end trainable. We release models, data, code and benchmarks under open licenses at https://hf.co/vidore.
Engineering Notes (from forum analysis)
The original forum post situates ColPali at the intersection of RAG and large-scale search/retrieval systems, and offers deployment guidance:
> Note: The forum post's quantitative claims should be verified against the original PDF tables.