Perform batch text prediction using Gemini model and returns the output location.
Explore further
For detailed documentation that includes this code sample, see the following:
Code sample
Go
Before trying this sample, follow the Go setup instructions in the Vertex AI quickstart using client libraries . For more information, see the Vertex AI Go API reference documentation .
To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .
import
(
"context"
"fmt"
"io"
"time"
"google.golang.org/genai"
)
// generateBatchPredict runs a batch prediction job using GCS input/output.
func
generateBatchPredict
(
w
io
.
Writer
,
outputURI
string
)
error
{
// outputURI = "gs://your-bucket/your-prefix"
ctx
:=
context
.
Background
()
client
,
err
:=
genai
.
NewClient
(
ctx
,
& genai
.
ClientConfig
{
HTTPOptions
:
genai
.
HTTPOptions
{
APIVersion
:
"v1"
},
})
if
err
!=
nil
{
return
fmt
.
Errorf
(
"failed to create genai client: %w"
,
err
)
}
// Source file with prompts for prediction
src
:=
& genai
.
BatchJobSource
{
Format
:
"jsonl"
,
// Source link: https://storage.cloud.google.com/cloud-samples-data/batch/prompt_for_batch_gemini_predict.jsonl
GCSURI
:
[]
string
{
"gs://cloud-samples-data/batch/prompt_for_batch_gemini_predict.jsonl"
},
}
// Batch job config with output GCS location
config
:=
& genai
.
CreateBatchJobConfig
{
Dest
:
& genai
.
BatchJobDestination
{
Format
:
"jsonl"
,
GCSURI
:
outputURI
,
},
}
// To use a tuned model, set the model param to your tuned model using the following format:
// modelName:= "projects/{PROJECT_ID}/locations/{LOCATION}/models/{MODEL_ID}
modelName
:=
"gemini-2.5-flash"
// See the documentation: https://pkg.go.dev/google.golang.org/genai#Batches.Create
job
,
err
:=
client
.
Batches
.
Create
(
ctx
,
modelName
,
src
,
config
)
if
err
!=
nil
{
return
fmt
.
Errorf
(
"failed to create batch job: %w"
,
err
)
}
fmt
.
Fprintf
(
w
,
"Job name: %s\n"
,
job
.
Name
)
fmt
.
Fprintf
(
w
,
"Job state: %s\n"
,
job
.
State
)
// Example response:
// Job name: projects/{PROJECT_ID}/locations/us-central1/batchPredictionJobs/9876453210000000000
// Job state: JOB_STATE_PENDING
// See the documentation: https://pkg.go.dev/google.golang.org/genai#BatchJob
completedStates
:=
map
[
genai
.
JobState
]
bool
{
genai
.
JobStateSucceeded
:
true
,
genai
.
JobStateFailed
:
true
,
genai
.
JobStateCancelled
:
true
,
genai
.
JobStatePaused
:
true
,
}
for
!
completedStates
[
job
.
State
]
{
time
.
Sleep
(
30
*
time
.
Second
)
job
,
err
=
client
.
Batches
.
Get
(
ctx
,
job
.
Name
,
nil
)
if
err
!=
nil
{
return
fmt
.
Errorf
(
"failed to get batch job: %w"
,
err
)
}
fmt
.
Fprintf
(
w
,
"Job state: %s\n"
,
job
.
State
)
}
// Example response:
// Job state: JOB_STATE_PENDING
// Job state: JOB_STATE_RUNNING
// Job state: JOB_STATE_RUNNING
// ...
// Job state: JOB_STATE_SUCCEEDED
return
nil
}
Java
Before trying this sample, follow the Java setup instructions in the Vertex AI quickstart using client libraries . For more information, see the Vertex AI Java API reference documentation .
To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .
import static
com.google.genai.types.JobState.Known.JOB_STATE_CANCELLED
;
import static
com.google.genai.types.JobState.Known.JOB_STATE_FAILED
;
import static
com.google.genai.types.JobState.Known.JOB_STATE_PAUSED
;
import static
com.google.genai.types.JobState.Known.JOB_STATE_SUCCEEDED
;
import
com.google.genai.Client
;
import
com.google.genai.types.BatchJob
;
import
com.google.genai.types.BatchJobDestination
;
import
com.google.genai.types.BatchJobSource
;
import
com.google.genai.types.CreateBatchJobConfig
;
import
com.google.genai.types.GetBatchJobConfig
;
import
com.google.genai.types.HttpOptions
;
import
com.google.genai.types.JobState
;
import
java.util.EnumSet
;
import
java.util.Optional
;
import
java.util.Set
;
import
java.util.concurrent.TimeUnit
;
public
class
BatchPredictionWithGcs
{
public
static
void
main
(
String
[]
args
)
throws
InterruptedException
{
// TODO(developer): Replace these variables before running the sample.
// To use a tuned model, set the model param to your tuned model using the following format:
// modelId = "projects/{PROJECT_ID}/locations/{LOCATION}/models/{MODEL_ID}
String
modelId
=
"gemini-2.5-flash"
;
String
outputGcsUri
=
"gs://your-bucket/your-prefix"
;
createBatchJob
(
modelId
,
outputGcsUri
);
}
// Creates a batch prediction job with Google Cloud Storage.
public
static
JobState
createBatchJob
(
String
modelId
,
String
outputGcsUri
)
throws
InterruptedException
{
// Client Initialization. Once created, it can be reused for multiple requests.
try
(
Client
client
=
Client
.
builder
()
.
location
(
"global"
)
.
vertexAI
(
true
)
.
httpOptions
(
HttpOptions
.
builder
().
apiVersion
(
"v1"
).
build
())
.
build
())
{
// See the documentation:
// https://googleapis.github.io/java-genai/javadoc/com/google/genai/Batches.html
BatchJobSource
batchJobSource
=
BatchJobSource
.
builder
()
// Source link:
// https://storage.cloud.google.com/cloud-samples-data/batch/prompt_for_batch_gemini_predict.jsonl
.
gcsUri
(
"gs://cloud-samples-data/batch/prompt_for_batch_gemini_predict.jsonl"
)
.
format
(
"jsonl"
)
.
build
();
CreateBatchJobConfig
batchJobConfig
=
CreateBatchJobConfig
.
builder
()
.
displayName
(
"your-display-name"
)
.
dest
(
BatchJobDestination
.
builder
().
gcsUri
(
outputGcsUri
).
format
(
"jsonl"
).
build
())
.
build
();
BatchJob
batchJob
=
client
.
batches
.
create
(
modelId
,
batchJobSource
,
batchJobConfig
);
String
jobName
=
batchJob
.
name
().
orElseThrow
(()
-
>
new
IllegalStateException
(
"Missing job name"
));
JobState
jobState
=
batchJob
.
state
().
orElseThrow
(()
-
>
new
IllegalStateException
(
"Missing job state"
));
System
.
out
.
println
(
"Job name: "
+
jobName
);
System
.
out
.
println
(
"Job state: "
+
jobState
);
// Job name: projects/.../locations/.../batchPredictionJobs/6205497615459549184
// Job state: JOB_STATE_PENDING
// See the documentation:
// https://googleapis.github.io/java-genai/javadoc/com/google/genai/types/BatchJob.html
Set<JobState
.
Known
>
completedStates
=
EnumSet
.
of
(
JOB_STATE_SUCCEEDED
,
JOB_STATE_FAILED
,
JOB_STATE_CANCELLED
,
JOB_STATE_PAUSED
);
while
(
!
completedStates
.
contains
(
jobState
.
knownEnum
()))
{
TimeUnit
.
SECONDS
.
sleep
(
30
);
batchJob
=
client
.
batches
.
get
(
jobName
,
GetBatchJobConfig
.
builder
().
build
());
jobState
=
batchJob
.
state
()
.
orElseThrow
(()
-
>
new
IllegalStateException
(
"Missing job state during polling"
));
System
.
out
.
println
(
"Job state: "
+
jobState
);
}
// Example response:
// Job state: JOB_STATE_QUEUED
// Job state: JOB_STATE_RUNNING
// Job state: JOB_STATE_RUNNING
// ...
// Job state: JOB_STATE_SUCCEEDED
return
jobState
;
}
}
}
Node.js
Before trying this sample, follow the Node.js setup instructions in the Vertex AI quickstart using client libraries . For more information, see the Vertex AI Node.js API reference documentation .
To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .
const
{
GoogleGenAI
}
=
require
(
'@google/genai'
);
const
GOOGLE_CLOUD_PROJECT
=
process
.
env
.
GOOGLE_CLOUD_PROJECT
;
const
GOOGLE_CLOUD_LOCATION
=
process
.
env
.
GOOGLE_CLOUD_LOCATION
||
'us-central1'
;
const
OUTPUT_URI
=
'gs://your-bucket/your-prefix'
;
async
function
runBatchPredictionJob
(
outputUri
=
OUTPUT_URI
,
projectId
=
GOOGLE_CLOUD_PROJECT
,
location
=
GOOGLE_CLOUD_LOCATION
)
{
const
client
=
new
GoogleGenAI
({
vertexai
:
true
,
project
:
projectId
,
location
:
location
,
httpOptions
:
{
apiVersion
:
'v1'
,
},
});
// See the documentation: https://googleapis.github.io/js-genai/release_docs/classes/batches.Batches.html
let
job
=
await
client
.
batches
.
create
({
// To use a tuned model, set the model param to your tuned model using the following format:
// model="projects/{PROJECT_ID}/locations/{LOCATION}/models/{MODEL_ID}"
model
:
'gemini-2.5-flash'
,
// Source link: https://storage.cloud.google.com/cloud-samples-data/batch/prompt_for_batch_gemini_predict.jsonl
src
:
'gs://cloud-samples-data/batch/prompt_for_batch_gemini_predict.jsonl'
,
config
:
{
dest
:
outputUri
,
},
});
console
.
log
(
`Job name:
${
job
.
name
}
`
);
console
.
log
(
`Job state:
${
job
.
state
}
`
);
// Example response:
// Job name: projects/%PROJECT_ID%/locations/us-central1/batchPredictionJobs/9876453210000000000
// Job state: JOB_STATE_PENDING
const
completedStates
=
new
Set
([
'JOB_STATE_SUCCEEDED'
,
'JOB_STATE_FAILED'
,
'JOB_STATE_CANCELLED'
,
'JOB_STATE_PAUSED'
,
]);
while
(
!
completedStates
.
has
(
job
.
state
))
{
await
new
Promise
(
resolve
=
>
setTimeout
(
resolve
,
30000
));
job
=
await
client
.
batches
.
get
({
name
:
job
.
name
});
console
.
log
(
`Job state:
${
job
.
state
}
`
);
}
// Example response:
// Job state: JOB_STATE_PENDING
// Job state: JOB_STATE_RUNNING
// Job state: JOB_STATE_RUNNING
// ...
// Job state: JOB_STATE_SUCCEEDED
return
job
.
state
;
}
Python
Before trying this sample, follow the Python setup instructions in the Vertex AI quickstart using client libraries . For more information, see the Vertex AI Python API reference documentation .
To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .
import
time
from
google
import
genai
from
google.genai.types
import
CreateBatchJobConfig
,
JobState
,
HttpOptions
client
=
genai
.
Client
(
http_options
=
HttpOptions
(
api_version
=
"v1"
))
# TODO(developer): Update and un-comment below line
# output_uri = "gs://your-bucket/your-prefix"
# See the documentation: https://googleapis.github.io/python-genai/genai.html#genai.batches.Batches.create
job
=
client
.
batches
.
create
(
# To use a tuned model, set the model param to your tuned model using the following format:
# model="projects/{PROJECT_ID}/locations/{LOCATION}/models/{MODEL_ID}
model
=
"gemini-2.5-flash"
,
# Source link: https://storage.cloud.google.com/cloud-samples-data/batch/prompt_for_batch_gemini_predict.jsonl
src
=
"gs://cloud-samples-data/batch/prompt_for_batch_gemini_predict.jsonl"
,
config
=
CreateBatchJobConfig
(
dest
=
output_uri
),
)
print
(
f
"Job name:
{
job
.
name
}
"
)
print
(
f
"Job state:
{
job
.
state
}
"
)
# Example response:
# Job name: projects/.../locations/.../batchPredictionJobs/9876453210000000000
# Job state: JOB_STATE_PENDING
# See the documentation: https://googleapis.github.io/python-genai/genai.html#genai.types.BatchJob
completed_states
=
{
JobState
.
JOB_STATE_SUCCEEDED
,
JobState
.
JOB_STATE_FAILED
,
JobState
.
JOB_STATE_CANCELLED
,
JobState
.
JOB_STATE_PAUSED
,
}
while
job
.
state
not
in
completed_states
:
time
.
sleep
(
30
)
job
=
client
.
batches
.
get
(
name
=
job
.
name
)
print
(
f
"Job state:
{
job
.
state
}
"
)
# Example response:
# Job state: JOB_STATE_PENDING
# Job state: JOB_STATE_RUNNING
# Job state: JOB_STATE_RUNNING
# ...
# Job state: JOB_STATE_SUCCEEDED
What's next
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