Google Document AI Warehouse API

Name: Document AI Warehouse API
Brand: Google Document AI Warehouse API
Availability: InStock

✓ Official Vendor SpecStorageDocument Managementoauth222 EndpointsREST

Install Jentic One Beta

Connect the Document AI Warehouse API to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Document AI Warehouse API, or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.

Two steps, two machines. Install the instance in a safe environment, then register your agent from wherever it runs.

Step 1: Jentic One Host machine

# On the machine that will host your Jentic One instance:
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh

Step 2: Agent machine

# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh
jentic register       # connects your agent to your Jentic One instance

Jentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.

Capabilities

What an agent can do with Document AI Warehouse API.

Initialize and manage Document AI Warehouse locations per project

Upload documents with structured properties or reference Cloud Storage URIs

Define document schemas to constrain valid property types

GET STARTED

Start building with Document AI Warehouse API

Explore with Jentic

View OpenAPI Document

For Agents

Upload, classify, search, and link enterprise documents in a managed warehouse so an agent can find and act on contracts, invoices, and forms by their extracted properties.

Use for: I need to upload an invoice PDF and tag it with a vendor, Search for contracts signed in the last quarter, Retrieve a document by its resource name, Find all documents tagged with a specific property value

Not supported: Does not perform OCR, extract entities, or render documents - use for governed storage, schema-typed properties, and search of documents only.

Google Document AI Warehouse API stores, organises, and searches structured and unstructured business documents. It exposes location lifecycle, document and folder resources, document schemas that constrain extracted properties, and rule-based access policies. Documents can be uploaded directly or referenced from Cloud Storage and are then searchable by extracted entities, full text, and structured metadata. The API also supports linking documents into folders, creating semantic relationships, and integrating with Document AI processors for automatic property extraction.

Use Cases

Patterns agents use Document AI Warehouse API for, with concrete tasks.

★ Invoice Search and Retrieval

Finance uploads scanned invoices to Document AI Warehouse and runs Document AI processors that extract supplier, amount, and date as structured properties. Auditors then search by supplier name or date range without hand-tagging. Document AI Warehouse returns ranked matches with entity highlights.

Search documents in folder 'invoices' for property supplier='Acme Ltd' AND date>='2026-01-01', and return the top 10 matches with their resource names.

Contract Lifecycle Vault

Legal stores executed contracts in Document AI Warehouse with extracted parties, effective date, and renewal date as schema-validated properties. A renewal alert job runs documents.search filtered to renewals in the next 60 days. Each contract is access-controlled with rules so only the owning team can read it.

Search documents with schema=Contract where renewalDate is within the next 60 days, and return parties and renewalDate for each match.

Multi-Tenant Document Isolation

A B2B SaaS uses Document AI Warehouse to host documents on behalf of customers, with strict per-tenant ACLs enforced through document rules. The API's rule-based access controls ensure that one tenant's search never returns another tenant's documents, even if both share the same location resource.

Create a document for tenant 'tenant-42' with rule restricting read access to group tenant42-readers@example.com, and confirm the rule applied.

AI Agent Document Q&A

A customer support AI agent receives a question that requires policy lookup. The agent asks Jentic to search Document AI Warehouse for the relevant policy document, retrieves the matching property values, and answers. Jentic isolates the Google service account credential and rotates tokens per call.

Search documents with schema=Policy and full-text query 'refund window', return the top 3 documents and their summary properties.

Key Endpoints

22 endpoints — google document ai warehouse api stores, organises, and searches structured and unstructured business documents.

METHOD

PATH

DESCRIPTION

POST

/v1/{+location}:initialize

Initialize a Document AI Warehouse location

GET

/v1/{+location}:getStatus

Get initialization status for a location

GET

/v1/{+name}

Get a document, schema, or folder resource

POST

/v1/{+name}:get

Fetch a document with optional ACL evaluation

POST

/v1/{+name}:delete

Delete a document

POST

/v1/{+location}:initialize

Initialize a Document AI Warehouse location

GET

/v1/{+location}:getStatus

Get initialization status for a location

GET

/v1/{+name}

Get a document, schema, or folder resource

POST

/v1/{+name}:get

Fetch a document with optional ACL evaluation

POST

/v1/{+name}:delete

Delete a document

Why Jentic?

Three things that make agents converge on Jentic-routed access.

Credential isolation

Your Google service account credential is stored once, encrypted, by your own Jentic One instance and injected at execution time. It never enters the agent's prompt, logs, or context.

Intent-based discovery

Agents search Jentic by intent such as 'find a document by property', and Jentic returns the matching Document AI Warehouse operation with its filter syntax and input schema so the agent calls the right endpoint without browsing the reference docs.

Time to first call

FAQs

Specific to using Document AI Warehouse API through Jentic.

What authentication does the Document AI Warehouse API use?

The Document AI Warehouse API uses Google OAuth 2.0 with the cloud-platform scope. Through Jentic the service account credentials are stored encrypted in your Jentic One instance and the agent receives scoped access tokens minted per call.

Can I search documents by extracted properties with Document AI Warehouse?

Yes. Once a document has properties (set manually at upload or extracted by a Document AI processor), use the documents.search endpoint with a structured filter referencing schema property names. Results return ranked documents with the matching property values.

What are the rate limits for the Document AI Warehouse API?

Document AI Warehouse applies per-project quotas with stricter caps on documents.create and documents.search than on metadata reads. Inspect the Cloud Console Quotas page for the precise per-method limits in your project.

How do I search documents by property through Jentic?

Run pip install jentic, search Jentic for 'search document ai warehouse', load the schema for the documents.search operation on contentwarehouse.googleapis.com, and execute it with your location name and a property filter.

Does Document AI Warehouse run OCR or extract entities itself?

No. The warehouse stores and serves documents and their structured properties. Extraction happens via Document AI processors that you invoke separately and then write the results back as document properties.

Google Document AI Warehouse API

Connect the Document AI Warehouse API to your agent

Capabilities

Start building with Document AI Warehouse API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs