sample_embeddings.py

# ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------
"""
DESCRIPTION:
 This sample demonstrates how to get text embeddings for a list of sentences
 using a synchronous client. Here we use the service default of returning
 embeddings as a list of floating point values.

 This sample assumes the AI model is hosted on a Serverless API or
 Managed Compute endpoint. For GitHub Models or Azure OpenAI endpoints,
 the client constructor needs to be modified. See package documentation:
 https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/ai/azure-ai-inference/README.md#key-concepts

USAGE:
 python sample_embeddings.py

 Set these two environment variables before running the sample:
 1) AZURE_AI_EMBEDDINGS_ENDPOINT - Your endpoint URL, in the form 
 https://<your-deployment-name>.<your-azure-region>.models.ai.azure.com
 where `your-deployment-name` is your unique AI Model deployment name, and
 `your-azure-region` is the Azure region where your model is deployed.
 2) AZURE_AI_EMBEDDINGS_KEY - Your model key. Keep it secret.
"""


defsample_embeddings():
importos

try:
endpoint=os.environ["AZURE_AI_EMBEDDINGS_ENDPOINT"]
key=os.environ["AZURE_AI_EMBEDDINGS_KEY"]
exceptKeyError:
print("Missing environment variable 'AZURE_AI_EMBEDDINGS_ENDPOINT' or 'AZURE_AI_EMBEDDINGS_KEY'")
print("Set them before running this sample.")
exit()

# [START embeddings]
fromazure.ai.inferenceimportEmbeddingsClient
fromazure.core.credentialsimportAzureKeyCredential

client=EmbeddingsClient(endpoint=endpoint, credential=AzureKeyCredential(key))

response=client.embed(input=["first phrase", "second phrase", "third phrase"])

foriteminresponse.data:
length=len(item.embedding)
print(
f"data[{item.index}]: length={length}, [{item.embedding[0]}, {item.embedding[1]}, "
f"..., {item.embedding[length-2]}, {item.embedding[length-1]}]"
 )
# [END embeddings]


if__name__=="__main__":
sample_embeddings()