Get text embeddings

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REST

To get text embeddings, send a POST request by specifying the model endpoint.

Follow these steps to make a request:

1. Save your request content in a JSON file named request.json. The file must look like the following example:

   {
     "instances": [
       {
         "content": "What is life?",
         "task_type": "",
         "title": ""
       }
     ]
   }
   

2. Make the request using the curl tool:

   curl -X POST \
   -H "Authorization: Bearer TOKEN"\
   -H "Content-Type: application/json; charset=utf-8" \
   -d @request.json \
   "https://ENDPOINT:443/v1/projects/PROJECT/locations/PROJECT/endpoints/MODEL:predict"
   

   Replace the following:

   • TOKEN: the authentication token you obtained.
   • ENDPOINT: the Text Embedding or Text Embedding Multilingual endpoint that you use for your organization. For more information, view service status and endpoints.
   • PROJECT: your project name.

   • MODEL: the model you want to use. The following are the available values:

     • endpoint-text-embedding for the Text Embedding model.
     • endpoint-text-embedding-multilingual for the Text Embedding Multilingual model.

You must obtain a JSON response similar to the following:

{"predictions":[[-0.00668720435,3.20804138e-05,-0.0281705819,-0.00954890903,-0.0818724185,0.0150693133,-0.00677698106, …. ,0.0167487375,-0.0534791686,0.00208711182,0.032938987,-0.01491543]],"deployedModelId":"text-embedding","model":"models/text-embedding/1","modelDisplayName":"text-embedding","modelVersionId":"1"}

Python

Follow these steps to get text embeddings from a Python script:

1. Install the Vertex AI Platform client library.

2. Save your request content in a JSON file named request.json. The file must look like the following example:

   {
     "instances": [
       {
         "content": "What is life?",
         "task_type": "",
         "title": ""
       }
     ]
   }
   

3. Install the required Python libraries:

   pip install absl-py
   

4. Create a Python file named client.py. The file must look like the following example:

   import json
   import os
   from typing import Sequence
   
   import grpc
   from absl import app
   from absl import flags
   
   from google.protobuf import json_format
   from google.protobuf.struct_pb2 import Value
   from google.cloud.aiplatform_v1.services import prediction_service
   
   _INPUT = flags.DEFINE_string("input", None, "input", required=True)
   _HOST = flags.DEFINE_string("host", None, "Prediction endpoint", required=True)
   _ENDPOINT_ID = flags.DEFINE_string("endpoint_id", None, "endpoint id", required=True)
   _TOKEN = flags.DEFINE_string("token", None, "STS token", required=True)
   
   # ENDPOINT_RESOURCE_NAME is a placeholder value that doesn't affect prediction behavior.
   ENDPOINT_RESOURCE_NAME="projects/PROJECT/locations/PROJECT/endpoints/MODEL"
   
   os.environ["GRPC_DEFAULT_SSL_ROOTS_FILE_PATH"] = CERT_NAME
   
   # predict_client_secure builds a client that requires TLS
   def predict_client_secure(host):
     with open(os.environ["GRPC_DEFAULT_SSL_ROOTS_FILE_PATH"], 'rb') as f:
         creds = grpc.ssl_channel_credentials(f.read())
   
     channel_opts = ()
     channel_opts += (('grpc.ssl_target_name_override', host),)
     client = prediction_service.PredictionServiceClient(
         transport=prediction_service.transports.grpc.PredictionServiceGrpcTransport(
             channel=grpc.secure_channel(target=host+":443", credentials=creds, options=channel_opts)))
     return client
   
   def predict_func(client, instances, token):
     resp = client.predict(
         endpoint=ENDPOINT_RESOURCE_NAME,
         instances=instances,
         metadata=[ ("x-vertex-ai-endpoint-id", _ENDPOINT_ID.value), ("authorization", "Bearer " + token),])
     print(resp)
   
   def main(argv: Sequence[str]):
     del argv  # Unused.
     with open(_INPUT.value) as json_file:
         data = json.load(json_file)
         instances = [json_format.ParseDict(s, Value()) for s in data["instances"]]
   
     client = predict_client_secure(_HOST.value,)
   
     predict_func(client=client, instances=instances, token=_TOKEN.value)
   
   if __name__=="__main__":
     app.run(main)
   

   Replace the following:

   • PROJECT: your project name.
   • MODEL: the model you want to use. The following are the available values:
     • endpoint-text-embedding for the Text Embedding model.
     • endpoint-text-embedding-multilingual for the Text Embedding Multilingual model.
   • CERT_NAME: the name of the Certificate Authority (CA) certificate file, such as org-1-trust-bundle-ca.cert. You only need this value if you are in a development environment. Otherwise, omit it.

5. Send a request:

   python client.py --token=TOKEN --host=ENDPOINT --input=request.json --endpoint_id=MODEL
   

   Replace the following:

   • TOKEN: the authentication token you obtained.
   • ENDPOINT: the Text Embedding or Text Embedding Multilingual endpoint that you use for your organization. For more information, view service status and endpoints.

   • MODEL: the model you want to use. The following are the available values:

     • endpoint-text-embedding for the Text Embedding model.
     • endpoint-text-embedding-multilingual for the Text Embedding Multilingual model.

You must obtain a JSON response similar to the following:

{"predictions":[[-0.00668720435,3.20804138e-05,-0.0281705819,-0.00954890903,-0.0818724185,0.0150693133,-0.00677698106, …. ,0.0167487375,-0.0534791686,0.00208711182,0.032938987,-0.01491543]],"deployedModelId":"text-embedding","model":"models/text-embedding/1","modelDisplayName":"text-embedding","modelVersionId":"1"}