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ColPali is a fascinating leap in document retrieval. Its precision in handling visually rich PDFs is phenomenal, but scaling it to handle real-world datasets comes with its share of computational challenges.
Here’s how we solved these challenges to make ColPali 13x faster without sacrificing the precision it’s known for.
The Scaling Dilemma ColPali generates 1,030 vectors for just one page of a PDF. While this is manageable for small-scale tasks, in a real-world production setting where you may need to store hundreds od thousands of PDFs, the challenge of scaling becomes significant.