Get text embeddings for a snippet of text using an embedding model.
Code sample
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
java.util.stream.Collectors.toList
;
import
com.google.cloud.aiplatform.v1. EndpointName
;
import
com.google.cloud.aiplatform.v1. PredictRequest
;
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. Struct
;
import
com.google.protobuf. Value
;
import
java.io.IOException
;
import
java.util.ArrayList
;
import
java.util.List
;
import
java.util.OptionalInt
;
import
java.util.regex.Matcher
;
import
java.util.regex.Pattern
;
public
class
PredictTextEmbeddingsSample
{
public
static
void
main
(
String
[]
args
)
throws
IOException
{
// TODO(developer): Replace these variables before running the sample.
// Details about text embedding request structure and supported models are available in:
// https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-text-embeddings
String
endpoint
=
"us-central1-aiplatform.googleapis.com:443"
;
String
project
=
"YOUR_PROJECT_ID"
;
String
model
=
"gemini-embedding-001"
;
predictTextEmbeddings
(
endpoint
,
project
,
model
,
List
.
of
(
"banana bread?"
,
"banana muffins?"
),
"QUESTION_ANSWERING"
,
OptionalInt
.
of
(
3072
));
}
// Gets text embeddings from a pretrained, foundational model.
public
static
List<List<Float>
>
predictTextEmbeddings
(
String
endpoint
,
String
project
,
String
model
,
List<String>
texts
,
String
task
,
OptionalInt
outputDimensionality
)
throws
IOException
{
PredictionServiceSettings
settings
=
PredictionServiceSettings
.
newBuilder
().
setEndpoint
(
endpoint
).
build
();
Matcher
matcher
=
Pattern
.
compile
(
"^(?<Location>\\w+-\\w+)"
).
matcher
(
endpoint
);
String
location
=
matcher
.
matches
()
?
matcher
.
group
(
"Location"
)
:
"us-central1"
;
EndpointName
endpointName
=
EndpointName
.
ofProjectLocationPublisherModelName
(
project
,
location
,
"google"
,
model
);
List<List<Float>
>
floats
=
new
ArrayList
<> ();
// You can use this prediction service client for multiple requests.
try
(
PredictionServiceClient
client
=
PredictionServiceClient
.
create
(
settings
))
{
// gemini-embedding-001 takes one input at a time.
for
(
int
i
=
0
;
i
<
texts
.
size
();
i
++
)
{
PredictRequest
.
Builder
request
=
PredictRequest
.
newBuilder
().
setEndpoint
(
endpointName
.
toString
());
if
(
outputDimensionality
.
isPresent
())
{
request
.
setParameters
(
Value
.
newBuilder
()
.
setStructValue
(
Struct
.
newBuilder
()
.
putFields
(
"outputDimensionality"
,
valueOf
(
outputDimensionality
.
getAsInt
()))
.
build
()));
}
request
.
addInstances
(
Value
.
newBuilder
()
.
setStructValue
(
Struct
.
newBuilder
()
.
putFields
(
"content"
,
valueOf
(
texts
.
get
(
i
)))
.
putFields
(
"task_type"
,
valueOf
(
task
))
.
build
()));
PredictResponse
response
=
client
.
predict
(
request
.
build
());
for
(
Value
prediction
:
response
.
getPredictionsList
())
{
Value
embeddings
=
prediction
.
getStructValue
().
getFieldsOrThrow
(
"embeddings"
);
Value
values
=
embeddings
.
getStructValue
().
getFieldsOrThrow
(
"values"
);
floats
.
add
(
values
.
getListValue
().
getValuesList
().
stream
()
.
map
(
Value
::
getNumberValue
)
.
map
(
Double
::
floatValue
)
.
collect
(
toList
()));
}
}
return
floats
;
}
}
private
static
Value
valueOf
(
String
s
)
{
return
Value
.
newBuilder
().
setStringValue
(
s
).
build
();
}
private
static
Value
valueOf
(
int
n
)
{
return
Value
.
newBuilder
().
setNumberValue
(
n
).
build
();
}
}
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 .
async
function
main
(
project
,
model
=
'gemini-embedding-001'
,
texts
=
'banana bread?;banana muffins?'
,
task
=
'QUESTION_ANSWERING'
,
dimensionality
=
0
,
apiEndpoint
=
'us-central1-aiplatform.googleapis.com'
)
{
const
aiplatform
=
require
(
' @google-cloud/aiplatform
'
);
const
{
PredictionServiceClient
}
=
aiplatform
.
v1
;
const
{
helpers
}
=
aiplatform
;
// helps construct protobuf.Value objects.
const
clientOptions
=
{
apiEndpoint
:
apiEndpoint
};
const
location
=
'us-central1'
;
const
endpoint
=
`projects/
${
project
}
/locations/
${
location
}
/publishers/google/models/
${
model
}
`
;
async
function
callPredict
()
{
const
instances
=
texts
.
split
(
';'
)
.
map
(
e
=
>
helpers
.
toValue
({
content
:
e
,
task_type
:
task
}));
const
client
=
new
PredictionServiceClient
(
clientOptions
);
const
parameters
=
helpers
.
toValue
(
dimensionality
>
0
?
{
outputDimensionality
:
parseInt
(
dimensionality
)}
:
{}
);
const
allEmbeddings
=
[]
// gemini-embedding-001 takes one input at a time.
for
(
const
instance
of
instances
)
{
const
request
=
{
endpoint
,
instances
:
[
instance
],
parameters
};
const
[
response
]
=
await
client
.
predict
(
request
);
const
predictions
=
response
.
predictions
;
const
embeddings
=
predictions
.
map
(
p
=
>
{
const
embeddingsProto
=
p
.
structValue
.
fields
.
embeddings
;
const
valuesProto
=
embeddingsProto
.
structValue
.
fields
.
values
;
return
valuesProto
.
listValue
.
values
.
map
(
v
=
>
v
.
numberValue
);
});
allEmbeddings
.
push
(
embeddings
[
0
])
}
console
.
log
(
'Got embeddings: \n'
+
JSON
.
stringify
(
allEmbeddings
));
}
callPredict
();
}
What's next
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