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# ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------
"""
DESCRIPTION:
This sample demonstrates how to create a client from a given endpoint URL using
the load_client() function, imported from azure.ai.inference.
In this sample, we get a synchronous chat completions client and do one
chat completions call.
The load_client() function only works with Serverless API or Managed Compute endpoints.
USAGE:
python sample_load_client.py
Set these two environment variables before running the sample:
1) AZURE_AI_CHAT_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_CHAT_KEY - Your model key. Keep it secret.
"""
def sample_load_client():
import os
try:
endpoint = os.environ["AZURE_AI_CHAT_ENDPOINT"]
key = os.environ["AZURE_AI_CHAT_KEY"]
except KeyError:
print("Missing environment variable 'AZURE_AI_CHAT_ENDPOINT' or 'AZURE_AI_CHAT_KEY'")
print("Set them before running this sample.")
exit()
# [START load_client]
from azure.ai.inference import load_client, ChatCompletionsClient
from azure.ai.inference.models import SystemMessage, UserMessage
from azure.core.credentials import AzureKeyCredential
client = load_client(endpoint=endpoint, credential=AzureKeyCredential(key))
# This should create a client of type `ChatCompletionsClient`
print(f"Created client of type `{type(client).__name__}`.")
if type(client) is ChatCompletionsClient:
response = client.complete(
messages=[
SystemMessage("You are a helpful assistant."),
UserMessage("How many feet are in a mile?"),
]
)
print(response.choices[0].message.content)
# [END load_client]
if __name__ == "__main__":
sample_load_client()
|
