Alternatives
Products that do what Vision-Based, Vectorless RAG for Long Douments does
In modern document question answering (QA) systems, Optical Character Recognition (OCR) serves an important role by converting PDF pages into text that can be processed by Large Language Models (LLMs). The resulting text can provide contextual input that enables LLMs to perform question answering over document content. Traditional OCR systems typically use a two-stage process that first detects the layout of a PDF — dividing it into text, tables, and images — and then recognizes and converts these elements into plain text. With the rise of vision-language models (VLMs) (such as Qwen-VL and…
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The traditional pipeline for unstructured data extraction typically follows these steps: 1. Image → OCR Model (e.g., Google Vision) → Layout Model (e.g. Surya) → LLM → Final Answer However, this can be streamlined using a Vision-Language Model (VLM): 2. Image → VLM → Final Answer Recently VLMs have improved a lot for OCR and document understanding tasks, specifically the Qwen-2.5-VL series. We can run the Qwen-2.5-VL-7B-AWQ model locally with just 16GB VRAM, and perform end-to-end information extraction (fields and table extraction) without any external models. Hallucination with VLMs One…
2025 · github.com
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2017 · pdflower.com
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Hey HN, we are releasing IRPAPERS to answer a highly pragmatic question: when building a RAG pipeline over PDFs, should you OCR the text or just embed the raw page images? Processing PDFs in production usually involves stringing together brittle OCR heuristics. While recent multimodal embeddings (like ColModernVBERT or ColPali) allow you to skip OCR entirely and retrieve directly from visual layouts, we wanted to measure if the computational overhead is actually worth the utility. The short answer: Transformer-based image pipelines won't be perfect for every use-case, but they fix exactly…
Feb 2026 · github.com
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