BigQuery Teams Up with Document AI for AI Magic

BigQuery and Document AI combine to provide use cases for generative AI and document analytics Organizations are producing enormous volumes of text and other document data as the digital transition quickens

They are thrilled to introduce an interface between BigQuery and Document AI to help you better use this data This integration will make it simple to draw conclusions from document data and create new large language model (LLM) applications

Some clients attempted to build autonomous Document AI pipelines prior to this connection, which required manually curating extraction algorithms and schema

With the help of this connection, clients may now quickly and simply build remote models in BigQuery for their unique extractors in Document AI

By establishing row-level access controls, which restrict users’ access to specific documents and, therefore, limit AI’s ability to protect privacy and security, you can control the unstructured data in the tables

There are several methods for training and deploying text models with BigQuery ML. BigQuery ML, for instance, may be used to categorize product comments into distinct groups

The PaLM 2 model is called by BigQuery’s ML.GENERATE_TEXT function to produce texts that may be used to condense the documents