Explore PaLM2 and AI Documents to Question Your World
The goal of internal IT and content management teams has always been to enable knowledge workers to engage with a document, or better yet, a corpus of documents, without having to search through them by hand.
Extracting information from a given document to provide natural language answers to queries is known as document question-answering, or document Q&A. There are many different businesses and disciplines where this kind of process might be used.
With the use of a retrieval Augmented Generation (RAG), you may produce more precise and insightful answers to inquiries by basing them on pertinent data from a knowledge base, such a vector store. PaLM2 and AI Documents OCR (optical character recognition) provide strong capabilities for this job.
Utilized PaLM2 and AI Documents, which offers superior, enterprise-ready AI document processing models, for this article. This serverless, scalable, fully managed system can handle millions of documents without requiring infrastructure to be spun up.
More precisely, They extracted text and layout data from document files using Enterprise Document OCR, a pre-trained model.Additionally, generative AI to generate a text embedding, or vector representation of text, using the textembedding-gecko model from Vertex AI.
Finally, used the Vertex AI text-bison foundation model in PaLM2 to respond to inquiries about the embedding data storage.
Any vector storage may be used to store embeddings. In order to demonstrate the implementation for a limited amount of documents, you did not use a vector store in this blog post; rather, the vectors were kept in a data structure.