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# ------------------------------------
# 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. The sample also shows how to set default embeddings
configuration in the client constructor, which will be applied to all `embed` calls
to the service.
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_with_defaults.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.
"""
def sample_embeddings_with_defaults():
import os
try:
endpoint = os.environ["AZURE_AI_EMBEDDINGS_ENDPOINT"]
key = os.environ["AZURE_AI_EMBEDDINGS_KEY"]
except KeyError:
print("Missing environment variable 'AZURE_AI_EMBEDDINGS_ENDPOINT' or 'AZURE_AI_EMBEDDINGS_KEY'")
print("Set them before running this sample.")
exit()
from azure.ai.inference import EmbeddingsClient
from azure.ai.inference.models import EmbeddingInputType
from azure.core.credentials import AzureKeyCredential
# Create a client with default embeddings settings
client = EmbeddingsClient(
endpoint=endpoint,
credential=AzureKeyCredential(key),
dimensions=1024,
input_type=EmbeddingInputType.QUERY,
)
# Call the service with the defaults specified above
response = client.embed(input=["first phrase", "second phrase", "third phrase"])
for item in response.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]}]"
)
# You can always override one or more of the defaults for a specific call, as shown here
response = client.embed(
input=["some other phrase", "yet another phrase", "one more phrase"],
input_type=EmbeddingInputType.TEXT,
)
for item in response.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]}]"
)
if __name__ == "__main__":
sample_embeddings_with_defaults()
|
