Automated schema extraction for custom extractor processors is in Preview.
This feature allows you to automatically extract a document schema from a test document you supply. Then, you can approve or decline the schema and edit it manually. This saves time and effort when defining the document schema for your custom processor and allows you to focus on refining the schema.
When creating a custom extractor processor, find the Generate from document option in the Get started tab of the Google Cloud console.
]]>Gemini layout parser is in
Preview.
The Gemini layout parser gives better layout quality on table recognition,
reading order and text recognition on PDF files. You can enable the feature by
default by selecting layout parser processor version
pretrained-layout-parser-v1.4-2024-08-25, pretrained-layout-parser-v1.5-2025-08-25
or pretrained-layout-parser-v1.5-pro-2025-08-25 for your processor.
Layout parser support for DOCX, PPTX, XLSX, and XLSM file types in Document AI is in General Availability (GA). It makes content like paragraphs, tables, lists, and structural elements like headings, page headers, and footers easily accessible. It also creates context-aware chunks that facilitate information retrieval in a range of generative AI and discovery applications.
For more information, see Process documents with Layout Parser.
]]>Custom splitter model
pretrained-splitter-v1.5-2025-07-14 with zero-shot splitting, classification
and confidence scores is available as Release Candidate
(Preview).
Layout parser lets you parse images and tables as annotations in Preview.
Layout parser can identify if there are images or tables in parsed documents. When found, images and tables are annotated as a descriptive block of text with the information depicted in the image and table.
]]>Capacity reservation is available for Document AI in Preview. This lets you grant capacity to selected processors and maintain a steady real-time, high-volume processing flow for document processing requests.
For the necessary steps, read make a capacity reservation request section of "Quotas".
Custom extractor model
pretrained-foundation-model-v1.5.1-2025-08-07 with improved adaptive few-shot
learning is available as Release Candidate
(Preview).
Support for confidence scores in Custom classifier
models pretrained-foundation-model-v1.4-2025-05-16 and pretrained-classifier-v1.5-2025-08-05
is in Preview.
For best performance, use them with fine-tuned models.
]]>Custom classifier
model pretrained-classifier-v1.5-2025-08-05
powered by Gemini 2.5 Flash is in Preview. It has ML processing available for US and EU regions, a
maximum page limit of 30 pages,
and processing requests of 120 pages per minute.
Unlike the prior custom classifier, which used classical machine learning, this version features a new platform. It accommodates:
For more information on processor versions, see Managing processor versions.
]]>Custom Extractor version pretrained-foundation-model-v1.4-2025-02-05 will no longer be accessible on February 5, 2026.
To avoid service disruptions, migrate to a later version such as
pretrained-foundation-model-v1.5-2025-05-05 or pretrained-foundation-model-v1.5-pro-2025-06-20.
To learn more about the migration process, refer to Manage processor
versions.
Document AI supports two service tiers and associated quotas: provisioned and best effort tiers.
The base is the provisioned tier quota, which provides 120 pages per minute for Gemini 2.0 and 2.5 Flash LLM and 30 pages per minute for Gemini 2.5 Pro LLM.
If you require more volume, best effort tier quota provides 120 pages per
minute for Gemini 2.0 2.5 Flash and 60 pages per minute for
Gemini 2.5 Pro. It's only used when the provisioned quota has been
exhausted. This applies to the BestEffortOnlineProcessDocumentPagesPerMinutePerProjectUS
and EU quotas and, in the console, best_effort_online_process_document_pages_us and eu.
Best effort can get up to 240 pages per minute for custom data extractor models v1.4 and v1.5 with a quota increase request (QIR). You can make a QIR by contacting your sales team representative.
There is no service level agreement (SLA) for best effort tier.
]]>Custom extractor model pretrained-foundation-model-v1.5-pro-2025-06-20 is
available as General Availability (GA).
For more information about available models, see the custom extractor page.
]]>Derived entity and signature detection are now supported in custom
extractor models pretrained-foundation-model-v1.4-2025-02-05
as General Availability (GA)
and in pretrained-foundation-model-v1.5-2025-05-05, as well as pretrained-foundation-model-v1.5-pro-2025-06-20
as Preview.
Signature detection lets you identify handwritten signatures by using visual cues in the document. Derived entity detection lets you deduce entities by inference without requiring the value to be explicitly present in the text. You can use this feature to deduce the country in an address, counting items in a table, or detecting if an ID is fake.
These can be enabled in the console when creating labels or by using the
DocumentSchema.EntityType
resource in the API.
For more information, read Custom extractor with derived fields and choose label attributes.
]]>Custom extractor model
pretrained-foundation-model-v1.5-pro-2025-06-20
powered by Gemini 2.5 Pro is in Preview.
It has ML processing available for US and EU regions, a maximum page limit of
30 pages, and processing requests
of 30 pages per minute.
For more information, see Managing processor versions.
]]>Document AI now supports Identity and Access Management (IAM) deny policies. These policies allow you to define deny rules that prevent certain principals from using certain permissions to access Google Cloud resources, regardless of the roles they're granted.
For more information, read Deny policy overview and Document AI security and compliance.
Document AI VPC service controls (VPC-SC) integration now supports identity groups.
For more information on setting up VPC-SC identity groups, read Configure identity groups and third-party identities in ingress and egress rules.
]]>The Document AI CDE processor now supports merging the child entities
of nested entities that extend across several pages. This is supported in custom
extractor model pretrained-foundation-model-v1.5-2025-05-05.
This change is automatic in all processors.
For customers with existing v1.5 processors, to make use of this feature, you must relabel the nested entities in different pages.
To learn more about the labeling process, refer to Label documents.
]]>Custom Extractor model pretrained-foundation-model-v1.5-2025-05-05 is in General Availability (GA) and has fine-tuning available for the US and EU.
From version v1.4 and later, we will use a new quota for online processing called Number of online process document pages per minute per processor type and model version. This quota will be enforced at a per-page and per-foundation model level. There will be no change to the batch processing quota.
These can be enabled in the console when creating labels and by using the DocumentSchema.EntityType.
For more information, read Managing processor versions.
]]>We've increased the maximum file size for online processing requests from 20 MB to 40 MB. This applies to all types of processors.
For more information, see the Document AI limits page.
]]>Cross-regions importing of fine-tuned models is now supported for processor versions based on Gemini 1.5 and later, such as
custom extractors
pretrained-foundation-model-v1.2-2024-05-10 and later.
For more information, see Managing processor versions.
]]>Custom extractor model pretrained-foundation-model-v1.5-2025-04-25 powered by Gemini 2.5 Flash LLM is available as Public Preview in US regions. The custom extractor model supports a quota of up to 15 pages per minute for online process requests.
For more information about available models, see Custom extractor model versions.
]]>Previous Custom Extractor versions pretrained-foundation-model-v1.0-2023-08-22 and pretrained-foundation-model-v1.1-2024-03-12 will be deprecated on April 9, 2025. To ensure uninterrupted service, prediction traffic to these versions, including any fine-tuned variants, will be automatically redirected to the latest version, pretrained-foundation-model-v1.4-2025-02-05.
For guidance on how to fine-tune a new version, refer to the fine tuning documentation.
]]>All processors can now extend the Maximum page limit for online and synchronous requests up to 30 pages.
To do so, enable imageless_mode in ProcessRequest.
For Custom Extractor, you will need to first request to be allowlisted for this feature by filling out the form Allowlist Request for 30 Page limit in CDE.
]]>As we launch Custom Extractor version pretrained-foundation-model-v1.4-2025-02-05 in GA with fine tuning (in Preview), these versions will no longer be accessible effective September 24, 2025:
pretrained-foundation-model-v1.2-2024-05-10 pretrained-foundation-model-v1.3-2024-08-31 To avoid service disruptions, migrate to a later version, such as pretrained-foundation-model-v1.4-2025-02-05. To learn more about the migration process, refer to our Manage processor versions documentation.
Customers and projects can access pretrained-foundation-model-v1.2-2024-05-10 and pretrained-foundation-model-v1.3-2024-08-31 until September 24, 2025. This includes the ability to create tuning jobs and access fine-tuned processor versions.
Starting March 24, 2025:
pretrained-foundation-model-v1.2-2024-05-10 can only be used for batch processing.pretrained-foundation-model-v1.2-2024-05-10 and pretrained-foundation-model-v1.3-2024-08-31 will have a quota limit of 120 pages per minute.This update requires planning, but if you have questions or need assistance, contact Google Cloud support.
]]>Custom Extractor model pretrained-foundation-model-v1.4-2025-02-05 is in General Availability (GA), and has fine-tuning available in Preview for the US and EU.
From version v1.4 and later, we will use a new quota for online processing called Number of online process document pages per minute per processor_type_and_model_version. This quota will be enforced at a per-page and per-foundation model level. There will be no change to the batch processing quota.
Custom extractor model pretrained-foundation-model-v1.4-2025-02-05 powered by Gemini 2.0 Flash LLM is available as Public Preview in US and EU regions with improved accuracy. The Custom Extractor Model supports a quota of up to 120 pages per minute for online process requests.
For more information about available models, see Custom extractor model versions.
]]>Model pretrained-ocr-v2.1.1-2025-01-31 is available as a Release Candidate in the regions asia-south1, australia-southeast1, europe-west2, europe-west3 and northamerica-northeast1.
For more information about available models, see Enterprise Document OCR.
Model pretrained-ocr-v2.1-2024-08-07 has General Availability (GA) in the US and EU.
For more information about available models, see Enterprise Document OCR and Regional and multi-regional support availability.
]]>For processor versions pretrained-foundation-model-v1.2-2024-05-10 and pretrained-foundation-model-v1.3-2024-08-31 custom extractors, customer-managed encryption keys (CMEK) is now supported when importing fine-tuned processor versions.
For more information, see Import processor versions.
]]>Effective January 27, 2025, new and existing processors require explicit storage.objects.get permissions to access Google Cloud Storage buckets for training dataset imports and offline/batch processing.
You will need to review your use of training dataset imports and offline/batch processing to verify that the users of these APIs have appropriate permissions to access Google Cloud Storage buckets.
Ensure that users of these APIs have been granted one of the predefined or legacy Cloud Storage roles that includes the storage.objects.get permission (such as Storage Object Viewer). You can assign these roles in the Permissions tab of the relevant Cloud Storage bucket.
We understand that this update requires planning, but we're here to support you during this process. If you have questions or need assistance, contact Google Cloud support.
]]>Property description is now Generally Available (GA) as part of the custom extractor in both the Document AI section of the Google Cloud console and the API, with additional support for parent entities in hierarchies.
Property description allows you to provide additional context, insights, and prior knowledge for each entity to improve extraction accuracy.
]]>You can copy processor versions of pretrained-foundation-model-v1.2-2024-05-10 and pretrained-foundation-model-v1.3-2024-08-31 between projects by following the steps in Import a processor version.
The Document AI section of the Google Cloud console now allows you to configure property descriptions as part of the Custom extractor processor-creation process.
Property description allows you to provide additional context, insights, and prior knowledge for each entity to improve extraction accuracy.
Property descriptions can be edited after schema creation. After you update the property descriptions, you will need to either call the pretrained models or create or fine-tune a new processor version for the changes to take effect.
]]>Custom Extractor pretrained-foundation-model-v1.2-2024-05-10 and pretrained-foundation-model-v1.3-2024-08-31 are now Stable versions.
v1.2 and v1.3 now have the following features:
v1.2 is recommended for the best quality. v1.3 is recommended for the lowest latency.
We recommend creating a new processor and relabeling the training and evaluation documents to benefit from both the improved quality with the new processor versions of Custom Extractor (v1.2 and v1.3) and the enhanced labeling system.
]]>