Optimizing ColPali for Retrieval at Scale, from Theory to Practice

In this webinar, we’ll explore how ColPali uses multivectors to represent visually rich documents. Moreover, we will address the scaling challenge of ColPali: Building an HNSW index with multivectors can be computationally demanding at scale. The quadratic complexity of comparing vectors leads to slow & inefficient processes.

By mean-pooling ColPali multivectors and using them for a first-stage retrieval followed by reranking with the original multivectors, we made the search 12x faster while keeping the near-identical performance of the original ColPali!

Key topics we’ll cover:
- How ColPali improves document retrieval
- Boosting ColPali with Qdrant’s Binary Quantization and beyond
- ColPali pooling optimization: the same accuracy as the original ColPali, but an order of magnitude faster!
- ColPali in RAG and Vision RAG, practical approach

Demo:

Hosts:

[RobCaulk]

I've been waiting for a break down like this to help me wrap my head around ColPali. Thanks!!

[AmitDeshmukh-ld]

I'm currently building with qdrant (love the binary quantization and multi vector approach to scale retrival with colpali) I was wondering if a js/ts example exists because thats primarily our tech stack. If not I'll try to put something out eventually.

[nitin]

Towards the end, she passed the whole image to a large 90b llama or gpt 4o, what's the point if have to pass the whole image instead of patches. Better owuld be if we can get the patches retrieved using copli and run some small vision model to extract answer.

[rodyatube]

i've used tesseract ocr to get text from images and the result is just ok, certainly no where near what you shown colpali can do. will certainly give it a try.

[haralc]

Why is this better than asking GPT4o to read the image?