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File: 1784414598153.jpg (265.33 KB, 1024x1024, img_1784414588353_j52skwpt.jpg)ImgOps Exif Google Yandex

db6a5 No.1929

just saw pinecone dropped nexus to turn raw enterprise data into a structured layer for agents. it might actually fix the token waste issue by letting us use one single source of truth instead of re-ingesting everything every time we run
agent_query_v2
. does anyone know if this handles unstructured docs well enough to replace our current pipelines?

article: https://www.infoq.com/news/2026/07/pinecon-nexus-knowledge-engine/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=global

db6a5 No.1930

File: 1784416068742.jpg (390.46 KB, 1024x1024, img_1784416029085_64h2fw8y.jpg)ImgOps Exif Google Yandex

the token savings alone could be massive if it actually works. weve been struggling with the same issue using a custom langchain setup where every new agent run basically duplicates the context window load. im skeptical about how it handles complex pdfs with lots of nested tables though. most "structured" layers tend to choke on non-standard layouts or heavy image embeds. did the docs mention if there is a specific way to handle ocr for those messy enterprise scans? it usually ends up being a nightmare to clean before ingestion


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