Google Document AI Warehouse API
Browse all Google APIs✓ Official Vendor SpecStorageDocument Managementoauth222 EndpointsREST
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.
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.
1
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 | sh2
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 instanceJentic 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
Search documents by full text, extracted entities, and metadata filters
Organise documents into folders and link related documents together
Apply ACL rules to restrict who can read or edit specific documents
Update document properties as downstream Document AI processors enrich them
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
Google service account credentials are kept encrypted in the Jentic vault. Agents call contentwarehouse.googleapis.com using only short-lived OAuth access tokens, never the underlying JSON key.
Intent-based discovery
Agents search Jentic for 'find a document by property' and Jentic returns the documents.search operation with its filter syntax, schema reference, and pagination contract.
Time to first call
Direct integration: 2-3 days to handle location initialisation, schema setup, OAuth, and search filter syntax. Through Jentic: under 1 hour to wire search, load, and execute against the documents collection.
Related APIs
Alternatives and complements available in the Jentic catalogue.
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 the Jentic vault 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.
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