Try Gemini 1.5 models, the latest multimodal models in Vertex AI, and see what you can build with up to a 2M token context window.Try Gemini 1.5 models, the latest multimodal models in Vertex AI, and see what you can build with up to a 2M token context window.
A document schema defines the structure for a document type (for example,
Invoice or Pay Stub) in Document AI Warehouse, where admins can specify properties
of different data types (Text | Numeric | Date | Enumeration).
Provides operations to create, fetch, update, and delete documents.
Document AI Warehouse uses documents as a data model to organize real world documents,
for example, PDF or .txt and their associated properties.
A folder serves as a container to group and label documents. Users can attach
a document to multiple folders and a folder can contain multiple documents.
It provides the capability to identify natural-language documents that satisfy
a query and optionally to sort them by relevance to the query. Using Document AI Warehouse,
customers can specify their query in string format in the search request.
Property filtering (Customer metadata filtering)
Mark a property filterable if you want to use that property to include or exclude
a portion of documents for a search. For example, you might make a property that represents a
"Vendor" filterable because your users want to search for invoices from a specific vendor.
Client libraries for Document AI Warehouse help support writing custom code that integrates with Google Cloud.
All services are accessible through the client libraries.
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