With ZeroX that launched a month ago and grew to 1.2K stars, it's clear that using multimodal LLMs to parse documents as images is the new way to go. We were tr

With ZeroX that launched a month ago and grew to 1.2K stars, it's clear that using multimodal LLMs to parse documents as images is the new way to go. We were trying to add a pipeline like this to our service, but were quite challenged by the most important step: retrieval. MiniCPM-Llama3-V-2_5 can answer about 95% of questions correctly based on a document page, but it first needs to be fed the right pages. We attempted to parse the pages into text and run embedding models on them. While it worked, the results were suboptimal since the models often missed important context inside pages, especially in visually rich documents. So we decided to train the first embedding model that ingests not only the text, but also positional information to improve its understanding of content hierarchy on the page. It's still in alpha and we haven't fully trained it yet, but we are looking for feedback and ideas!