Object enrichments

You can use object enrichments to automatically generate structured metadata without building custom machine learning pipelines. Object enrichments applies Google's pre-trained machine learning models directly to your stored objects to help you search, curate, and govern your unstructured data at scale.

To start generating metadata for your objects, see Configure object enrichments.

How it works

To use object enrichments, you work with the following parts of the system:

  • Feature configuration (FeatureConfig): You create a feature configuration to specify which buckets or projects Cloud Storage monitors.
  • Machine learning models: In your configuration, you choose the models that you want to apply, such as models that detect objects, label images, analyze faces, or categorize PDF documents.
  • Object enrichments (metadata): Cloud Storage scans your objects asynchronously and evaluates eligible objects with your selected models. Cloud Storage then attaches the resulting metadata directly to your objects as object contexts.

After Cloud Storage attaches the enrichment metadata to your objects, the metadata is ready to use. You can inspect individual object enrichments, or use Storage Insights datasets to analyze metadata across millions of objects by using SQL in BigQuery.

The following diagram shows how Cloud Storage uses object enrichments to extract context from unstructured data and store it as object contexts for downstream AI and analytics workflows:

Figure 1. Object enrichments workflow from raw data to object contexts and AI agents.
Figure 1. How object enrichments extracts metadata and stores it as object contexts.

Supported models

When you create a feature configuration, you select the machine learning models that best fit your data and analysis goals. Cloud Storage evaluates eligible objects by using your chosen models and generates structured metadata under contexts.custom.

The supported models table lists each model, what it detects, and sample output data. For detailed schema definitions and context keys, see Supported models and schemas.

Model Description Sample output data
Object detection Detects multiple objects within an image and provides their positions and rectangular boundaries. Product, Animal, Person
Image labeling Detects and extracts information about entities in an image across a broad group of categories, such as objects, locations, and activities. Landscape, Urban, Night
Face detection Detects faces in images, including bounding polygons, facial landmarks (eyes, nose, mouth), orientation angles, and expression likelihoods. Face count, Roll and tilt angle
Explicit content detection Evaluates images for sensitive or inappropriate content, including adult or violent material. Adult and violent content likelihood ratings
PDF classification Classifies PDF documents into standard business and industry categories based on their content. Finance, Taxes, Invoice (categories and scores)

Use cases

You can use object enrichments to streamline unstructured data management and prepare objects for the following downstream analytics and AI workflows:

  • Organize and catalog unorganized objects: Automatically classify collections of PDF documents (such as invoices, bank statements, and tax forms) and images. You can use these classifications to build searchable data catalogs across your buckets without writing custom code.
  • Ground AI agents and improve search: Turn disorganized document repositories into structured knowledge bases. You can use document categories and image labels to improve retrieval during retrieval-augmented generation (RAG) and ground your generative AI applications.
  • Sample and prepare data for generative AI: Differentiate between document types to perform stratified sampling before routing content to large language models (LLMs) or human review.
  • Curate machine learning training datasets: Filter and segment images and documents by taxonomy or visual labels to assemble domain-specific training datasets for model fine-tuning.
  • Scan for sensitive or explicit content: Automatically inspect uploaded images for inappropriate material to support your content moderation and safety policies.
  • Enhance multimodal prompts: Pass structured metadata—such as bounding boxes and document categories—as context when prompting multimodal models like Gemini.
  • Simplify data architectures: Eliminate custom ingestion pipelines and compute infrastructure by generating metadata directly in Cloud Storage.

Query and analyze your metadata

Cloud Storage attaches object enrichments directly to your objects as object contexts. Depending on your scale and workflow, you can access and analyze this metadata in the following ways:

  • Inspect individual objects: Use the Google Cloud console or the Cloud Storage APIs to inspect enrichments for specific objects. For instructions, see Manage object enrichments.
  • Analyze at scale with SQL: Use Storage Insights datasets to export metadata across millions or billions of objects directly into BigQuery. After Storage Insights exports the metadata, you can query the object_contexts_catalog_view view by using standard SQL. For example, you can search for labels, filter by category, or find objects that have high confidence scores.

To get started with SQL queries and sample recipes, see Analyze object enrichments in BigQuery.

Considerations and limitations

Before you configure object enrichments, review the following operational considerations:

Processing location

Object enrichments processes data in three geographic locations: us, eu, and asia. When selecting a processing location, keep the following considerations in mind:

  • Compliance: Make sure that your chosen processing location meets your regulatory and data sovereignty requirements.
  • Performance: Choose the processing location closest to your bucket's storage region.
  • Cost: To avoid duplicate charges, configure only one processing location for each bucket and model. If you configure multiple locations for the same bucket and model, Cloud Storage processes your objects in each location and bills you for every run.

Processing and performance

Review the following constraints on content types, object sizes, and processing timelines:

  • Asynchronous processing: Cloud Storage processes objects asynchronously.

  • Object size limit: Objects (both images and PDFs) must be 20 MiB or smaller.

  • Supported content types: Cloud Storage uses the Content-Type metadata field set on an object to determine whether to evaluate it. If an object has an invalid or missing Content-Type, Cloud Storage doesn't process the object even if the underlying data is a valid image or PDF. To make sure that Cloud Storage processes the object, update the Content-Type metadata to one of the following supported types:

    • Images: BMP, GIF, ICO, JPEG, PNG, TIFF, and WEBP.
    • Documents: PDF (application/pdf).

Unsupported features

Object enrichments is incompatible with the following bucket types and operations:

  • Bucket relocation: Object enrichments is incompatible with bucket relocation. Remove the bucket from your feature configurations before relocating the bucket.
  • Unsupported bucket types: You can't enable object enrichments on Rapid buckets or buckets that have hierarchical namespace enabled.

Configuration and scope management

When you define and maintain target buckets in your feature configurations, the following requirements apply:

  • Bucket scoping: Specify exact bucket names in your feature configuration. Cloud Storage doesn't support regular expressions or wildcard patterns.
  • Bucket lifecycle: Cloud Storage doesn't automatically add newly created, recreated, or relocated buckets to existing configurations. You must update your feature configuration to include these buckets.

Costs and security

Review the following operational details regarding encryption keys and data access charges:

  • Customer-supplied keys (CSEK): Object enrichments doesn't support objects encrypted with customer-supplied encryption keys.
  • Storage access and Autoclass: To generate metadata, object enrichments reads your object data. These read operations incur standard data access charges. Additionally, if your bucket has Autoclass enabled, these reads trigger storage class transitions.

Pricing

Using object enrichments incurs charges for model inference, metadata storage, data access operations, and networking. For more information, see Pricing.

Monitor and troubleshoot

To track metadata generation progress and resolve issues when configuring object enrichments, use the following resources:

What's next