Layout parser Quickstart
Use layout parser to extract elements from a document, such as text, tables, and lists.
To follow step-by-step guidance for this task directly in the Google Cloud console, click Guide me :
Before you begin
- Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.
-
In the Google Cloud console, on the project selector page, select or create a Google Cloud project.
Roles required to select or create a project
- Select a project : Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
- Create a project
: To create a project, you need the Project Creator role
(
roles/resourcemanager.projectCreator), which contains theresourcemanager.projects.createpermission. Learn how to grant roles .
-
Verify that billing is enabled for your Google Cloud project .
-
Enable the Document AI, Cloud Storage APIs.
Roles required to enable APIs
To enable APIs, you need the Service Usage Admin IAM role (
roles/serviceusage.serviceUsageAdmin), which contains theserviceusage.services.enablepermission. Learn how to grant roles . -
In the Google Cloud console, on the project selector page, select or create a Google Cloud project.
Roles required to select or create a project
- Select a project : Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
- Create a project
: To create a project, you need the Project Creator role
(
roles/resourcemanager.projectCreator), which contains theresourcemanager.projects.createpermission. Learn how to grant roles .
-
Verify that billing is enabled for your Google Cloud project .
-
Enable the Document AI, Cloud Storage APIs.
Roles required to enable APIs
To enable APIs, you need the Service Usage Admin IAM role (
roles/serviceusage.serviceUsageAdmin), which contains theserviceusage.services.enablepermission. Learn how to grant roles .
Create a processor
-
In the Google Cloud console, in the Document AI section, and select Processor Gallery.
-
In the Processor Gallery,
search for Layout parserand select Create.
-
In the side window, enter a Processor name, such as
quickstart-layout-processor. -
Select the region closest to you.
-
Click the Createbutton.
You're taken to the Processor Detailspage of your new form parser processor.
- Optional: Select a default processor by clicking
Manage versions , and selecting a processor from the Versionstable. Then clickMark as default and confirm by entering the processor name.
Test processor
After creating your processor, you can send annotation requests to it.
-
Click the
Upload Test Document button and select the document you just downloaded. -
You should now be on the layout parser analysispage. You can view the blocks or chunks parsed from the document, organized by detected types.
Process a document
REST
This example shows how to send a document stored in Cloud Storage to the layout parser for processing. This process enables image and table annotation by default.
REST
Before using any of the request data, make the following replacements:
- PROJECT_ID : Your Google Cloud project ID.
- LOCATION
: your processor's location
, for example:
-
us- United States -
eu- European Union
-
- PROCESSOR_ID : the ID of your custom processor.
- MIME_TYPE
: Layout parser supports
application/pdfandtext/html. - GCS_FILE_PATH : The file path for the Cloud Storage bucket with your document.
- CHUNK_SIZE : Optional. The chunk size, in tokens, to use when splitting documents.
- INCLUDE_ANCESTOR_HEADINGS : Optional. Boolean. Whether or not to include ancestor headings when splitting documents.
HTTP method and URL:
POST https:// LOCATION -documentai.googleapis.com/v1beta3/projects/ PROJECT_ID /locations/ LOCATION /processors/ PROCESSOR_ID /processorVersions/ pretrained-layout-parser-v1.5-2025-08-25 :process
Request JSON body:
{ "gcsDocument": { "gcsUri": " GCS_FILE_PATH ", "mimeType": " MIME_TYPE " }, "processOptions": { "layoutConfig": { "enableTableAnnotation": "true", "enableImageAnnotation": "true", "chunkingConfig": { "chunkSize": " CHUNK_SIZE ", "includeAncestorHeadings": " INCLUDE_ANCESTOR_HEADINGS ", } } } }
To send your request, choose one of these options:
curl
Save the request body in a file named request.json
,
and execute the following command:
curl -X POST \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json; charset=utf-8" \
-d @request.json \
"https:// LOCATION -documentai.googleapis.com/v1beta3/projects/ PROJECT_ID /locations/ LOCATION /processors/ PROCESSOR_ID /processorVersions/ pretrained-layout-parser-v1.5-2025-08-25 :process"
PowerShell
Save the request body in a file named request.json
,
and execute the following command:
$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }
Invoke-WebRequest `
-Method POST `
-Headers $headers `
-ContentType: "application/json; charset=utf-8" `
-InFile request.json `
-Uri "https:// LOCATION -documentai.googleapis.com/v1beta3/projects/ PROJECT_ID /locations/ LOCATION /processors/ PROCESSOR_ID /processorVersions/ pretrained-layout-parser-v1.5-2025-08-25 :process" | Select-Object -Expand Content
You should receive a successful status code (2xx) and an empty response.
Review the output
A successful request returns a document object in JSON. The most important fields
for Retrieval Augmented Generation (RAG) is document.chunked_document.chunks
.
The following is the output form parsing the third page of "Winnie the Pooh" by A.A. Milne.
{
"document"
:
{
docume
nt
_layou
t
{
blocks
{
block_id
:
"1"
te
x
t
_block
{
te
x
t
:
"WE ARE INTRODUCED 3"
t
ype_
:
"header"
}
page_spa
n
{
page_s
tart
:
1
page_e
n
d
:
1
}
}
blocks
{
block_id
:
"2"
page_spa
n
{
page_s
tart
:
1
page_e
n
d
:
1
}
image_block
{
mime_
t
ype
:
"image/png"
a
nn
o
tat
io
ns
{
descrip
t
io
n
:
"This is an ink drawing depicting Winnie-the-Pooh sitting outside his house.\n\nHere are the facts and conclusions that can be derived from the image:\n\n* **Character:** The central figure is a bear, identifiable as Winnie-the-Pooh, sitting on a log.\n* **Location:** He is positioned outside what appears to be a small, rustic shelter or house.\n* **Signage:** Above the doorway of the shelter, there is a sign that reads \"MR SANDERZ\". Below this sign, there is another partial sign visible, where the letters \"RNIG\" and \"ALSO\" can be seen.\n* **Doorbell:** To the left of the doorway, a bell is hanging, indicating a doorbell mechanism.\n* **Setting:** The dwelling is surrounded by what looks like brush, trees, and general wilderness, suggested by the lines representing foliage and twigs.\n* **Log:** Pooh is seated on a cut log or tree trunk. To the left of this log, there are other smaller logs or branches piled up.\n* **Style:** The image is a black and white line drawing, characteristic of classic book illustrations."
}
blob_asse
t
_id
:
"blob_1"
}
}
blocks
{
block_id
:
"3"
te
x
t
_block
{
te
x
t
:
""
Wi
nn
ie
-
t
he
-
Pooh
was
n
'
t
qui
te
sure
,
" said Christopher Robin. "
Now
I
am
,
" said a growly voice. "
The
n
I
will
go
o
n
,
"said I.) One day when he was out walking, he came to an open place in the middle of the forest, and in the middle of this place was a large oak-tree, and, from the top of the tree, there came a loud buzzing-noise. Winnie-the-Pooh sat down at the foot of the tree,put his head between his paws and began to think."
t
ype_
:
"paragraph"
}
page_spa
n
{
page_s
tart
:
1
page_e
n
d
:
1
}
}
blocks
{
block_id
:
"4"
te
x
t
_block
{
te
x
t
:
"Digitized by Google"
t
ype_
:
"footer"
}
page_spa
n
{
page_s
tart
:
1
page_e
n
d
:
1
}
}
}
chu
n
ked_docume
nt
{
chu
n
ks
{
chu
n
k_id
:
"c1"
source_block_ids
:
"2"
source_block_ids
:
"3"
co
ntent
:
"__START_OF_ANNOTATION__This is an ink drawing depicting Winnie-the-Pooh sitting outside his house.\n\nHere are the facts and conclusions that can be derived from the image:\n\n* **Character:** The central figure is a bear, identifiable as Winnie-the-Pooh, sitting on a log.\n* **Location:** He is positioned outside what appears to be a small, rustic shelter or house.\n* **Signage:** Above the doorway of the shelter, there is a sign that reads \"MR SANDERZ\". Below this sign, there is another partial sign visible, where the letters \"RNIG\" and \"ALSO\" can be seen.\n* **Doorbell:** To the left of the doorway, a bell is hanging, indicating a doorbell mechanism.\n* **Setting:** The dwelling is surrounded by what looks like brush, trees, and general wilderness, suggested by the lines representing foliage and twigs.\n* **Log:** Pooh is seated on a cut log or tree trunk. To the left of this log, there are other smaller logs or branches piled up.\n* **Style:** The image is a black and white line drawing, characteristic of classic book illustrations.__END_OF_ANNOTATION__"
Wi
nn
ie
-
t
he
-
Pooh
was
n
'
t
qui
te
sure
,
" said Christopher Robin. "
Now
I
am
,
" said a growly voice. "
The
n
I
will
go
o
n
,
" said I.) One day when he was out walking, he came to an open place in the middle of the forest, and in the middle of this place was a large oak-tree, and, from the top of the tree, there came a loud buzzing-noise. Winnie-the-Pooh sat down at the foot of the tree,put his head between his paws and began to think."
page_spa
n
{
page_s
tart
:
1
page_e
n
d
:
1
}
page_headers
{
te
x
t
:
"WE ARE INTRODUCED 3"
page_spa
n
{
page_s
tart
:
1
page_e
n
d
:
1
}
}
page_
f
oo
ters
{
te
x
t
:
"Digitized by Google"
page_spa
n
{
page_s
tart
:
1
page_e
n
d
:
1
}
}
chu
n
k_
f
ields
{
image_chu
n
k_
f
ield
{
blob_asse
t
_id
:
"blob_1"
a
nn
o
tat
io
ns
{
descrip
t
io
n
:
"This is an ink drawing depicting Winnie-the-Pooh sitting outside his house.\n\nHere are the facts and conclusions that can be derived from the image:\n\n* **Character:** The central figure is a bear, identifiable as Winnie-the-Pooh, sitting on a log.\n* **Location:** He is positioned outside what appears to be a small, rustic shelter or house.\n* **Signage:** Above the doorway of the shelter, there is a sign that reads \"MR SANDERZ\". Below this sign, there is another partial sign visible, where the letters \"RNIG\" and \"ALSO\" can be seen.\n* **Doorbell:** To the left of the doorway, a bell is hanging, indicating a doorbell mechanism.\n* **Setting:** The dwelling is surrounded by what looks like brush, trees, and general wilderness, suggested by the lines representing foliage and twigs.\n* **Log:** Pooh is seated on a cut log or tree trunk. To the left of this log, there are other smaller logs or branches piled up.\n* **Style:** The image is a black and white line drawing, characteristic of classic book illustrations."
}
}
}
}
}
blob_asse
ts
{
asse
t
_id
:
"blob_1"
co
ntent
:
"image_bytes"
mime_
t
ype
:
"image/png"
}
}
Python
This guide shows how to use the Python client library to process a document. Using this code enables image and table annotation by default.
-
Install the client library.
! pip install --upgrade --quiet google-cloud-documentai -
Run the processor.
def process_layout_parser ( project_id : str , location : str , processor_id : str , gcs_uri : str , mime_type : str ): """ Processes a document with the layout parser and prints chunk text. """ client = documentai . DocumentProcessorServiceClient () # The full resource name of the processor processor_version_id = 'pretrained-layout-parser-v1.5-2025-08-25' name = client . processor_path ( project_id , location , processor_id , processor_version_id ) # Configure the Cloud Storage document gcs_document = documentai . GcsDocument ( gcs_uri = gcs_uri , mime_type = mime_type ) # Configure processing options for RAG # This enables annotation and context-aware chunking. process_options = documentai . ProcessOptions ( # Process only specific pages layout_config = documentai . ProcessOptions . LayoutConfig ( enable_table_annotation = True , enable_image_annotation = True , chunking_config = documentai . ProcessOptions . LayoutConfig . ChunkingConfig ( chunk_size = 1024 , include_ancestor_headings = True , ), ), ) # Build the request request = documentai . ProcessRequest ( name = name , gcs_document = gcs_document , process_options = process_options , ) # Process the document result = client . process_document ( request = request ) document = result . document print ( f "Document processing complete. \n " ) print ( "--- RAG-Ready Chunks (with context) ---" ) for i , chunk in enumerate ( document . chunked_document . chunks ): print ( f " \n --- Chunk {i} ---" ) # Print the chunk's content print ( f "Text: {chunk.content}" ) return result
Batch process documents with layout parser
Use the following procedure to parse and chunk multiple documents in a single request.
-
Input documents to layout parser to parse and chunk.
-
Follow the instructions for batch processing requests in Send a processing request .
-
Configure fields in
ProcessOptions.layoutConfigwhen making abatchProcessrequest.Input
The following example JSON configures
ProcessOptions.layoutConfig."processOptions" : { "layoutConfig" : { "enableTableAnnotation" : "true" , "enableImageAnnotation" : "true" , "chunkingConfig" : { "chunkSize" : " CHUNK_SIZE " , "includeAncestorHeadings" : " INCLUDE_ANCESTOR_HEADINGS_BOOLEAN " } } }Replace the following:
-
CHUNK_SIZE: The maximum chunk size, in number of tokens, to use when splitting documents. -
INCLUDE_ANCESTOR_HEADINGS_BOOLEAN: Whether to include ancestor headings when splitting documents. Ancestor headings are the parents of subheadings in the original document. They can provide a chunk with additional context about its position in the original document. Up to two levels of headings can be included with a chunk.
-
