Summarize text content using a publisher text model.
Code sample
Java
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import
com.google.cloud.aiplatform.v1. EndpointName
;
import
com.google.cloud.aiplatform.v1. PredictResponse
;
import
com.google.cloud.aiplatform.v1. PredictionServiceClient
;
import
com.google.cloud.aiplatform.v1. PredictionServiceSettings
;
import
com.google.protobuf. Value
;
import
com.google.protobuf.util. JsonFormat
;
import
java.io.IOException
;
import
java.util.ArrayList
;
import
java.util.List
;
// Text Summarization with a Large Language Model
public
class
PredictTextSummarizationSample
{
public
static
void
main
(
String
[]
args
)
throws
IOException
{
// TODO(developer): Replace these variables before running the sample.
// Designing prompts for text summerization with supported large language models:
// https://cloud.google.com/vertex-ai/docs/generative-ai/text/summarization-prompts
String
instance
=
"{ \"content\": \"Background: There is evidence that there have been significant changes \n"
+
"in Amazon rainforest vegetation over the last 21,000 years through the Last \n"
+
"Glacial Maximum (LGM) and subsequent deglaciation. Analyses of sediment \n"
+
"deposits from Amazon basin paleo lakes and from the Amazon Fan indicate that \n"
+
"rainfall in the basin during the LGM was lower than for the present, and this \n"
+
"was almost certainly associated with reduced moist tropical vegetation cover \n"
+
"in the basin. There is debate, however, over how extensive this reduction \n"
+
"was. Some scientists argue that the rainforest was reduced to small, isolated \n"
+
"refugia separated by open forest and grassland; other scientists argue that \n"
+
"the rainforest remained largely intact but extended less far to the north, \n"
+
"south, and east than is seen today. This debate has proved difficult to \n"
+
"resolve because the practical limitations of working in the rainforest mean \n"
+
"that data sampling is biased away from the center of the Amazon basin, and \n"
+
"both explanations are reasonably well supported by the available data.\n"
+
"\n"
+
"Q: What does LGM stands for?\n"
+
"A: Last Glacial Maximum.\n"
+
"\n"
+
"Q: What did the analysis from the sediment deposits indicate?\n"
+
"A: Rainfall in the basin during the LGM was lower than for the present.\n"
+
"\n"
+
"Q: What are some of scientists arguments?\n"
+
"A: The rainforest was reduced to small, isolated refugia separated by open forest"
+
" and grassland.\n"
+
"\n"
+
"Q: There have been major changes in Amazon rainforest vegetation over the last how"
+
" many years?\n"
+
"A: 21,000.\n"
+
"\n"
+
"Q: What caused changes in the Amazon rainforest vegetation?\n"
+
"A: The Last Glacial Maximum (LGM) and subsequent deglaciation\n"
+
"\n"
+
"Q: What has been analyzed to compare Amazon rainfall in the past and present?\n"
+
"A: Sediment deposits.\n"
+
"\n"
+
"Q: What has the lower rainfall in the Amazon during the LGM been attributed to?\n"
+
"A:\"}"
;
String
parameters
=
"{\n"
+
" \"temperature\": 0,\n"
+
" \"maxOutputTokens\": 32,\n"
+
" \"topP\": 0,\n"
+
" \"topK\": 1\n"
+
"}"
;
String
project
=
"YOUR_PROJECT_ID"
;
String
location
=
"us-central1"
;
String
publisher
=
"google"
;
String
model
=
"text-bison@001"
;
predictTextSummarization
(
instance
,
parameters
,
project
,
location
,
publisher
,
model
);
}
// Get summarization from a supported text model
public
static
void
predictTextSummarization
(
String
instance
,
String
parameters
,
String
project
,
String
location
,
String
publisher
,
String
model
)
throws
IOException
{
String
endpoint
=
String
.
format
(
"%s-aiplatform.googleapis.com:443"
,
location
);
PredictionServiceSettings
predictionServiceSettings
=
PredictionServiceSettings
.
newBuilder
()
.
setEndpoint
(
endpoint
)
.
build
();
// Initialize client that will be used to send requests. This client only needs to be created
// once, and can be reused for multiple requests.
try
(
PredictionServiceClient
predictionServiceClient
=
PredictionServiceClient
.
create
(
predictionServiceSettings
))
{
final
EndpointName
endpointName
=
EndpointName
.
ofProjectLocationPublisherModelName
(
project
,
location
,
publisher
,
model
);
// Use Value.Builder to convert instance to a dynamically typed value that can be
// processed by the service.
Value
.
Builder
instanceValue
=
Value
.
newBuilder
();
JsonFormat
.
parser
().
merge
(
instance
,
instanceValue
);
List<Value>
instances
=
new
ArrayList
<> ();
instances
.
add
(
instanceValue
.
build
());
// Use Value.Builder to convert parameter to a dynamically typed value that can be
// processed by the service.
Value
.
Builder
parameterValueBuilder
=
Value
.
newBuilder
();
JsonFormat
.
parser
().
merge
(
parameters
,
parameterValueBuilder
);
Value
parameterValue
=
parameterValueBuilder
.
build
();
PredictResponse
predictResponse
=
predictionServiceClient
.
predict
(
endpointName
,
instances
,
parameterValue
);
System
.
out
.
println
(
"Predict Response"
);
System
.
out
.
println
(
predictResponse
);
}
}
}
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