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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. Here we request embeddings as base64
encoded strings, instead of the service default of lists of floats.
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_base64_encoding.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_base64_encoding():
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 EmbeddingEncodingFormat
from azure.core.credentials import AzureKeyCredential
client = EmbeddingsClient(endpoint=endpoint, credential=AzureKeyCredential(key))
# Request embeddings as base64 encoded strings
response = client.embed(
input=["first phrase", "second phrase", "third phrase"], encoding_format=EmbeddingEncodingFormat.BASE64
)
for item in response.data:
# Display the start and end of the resulting base64 string
print(
f"data[{item.index}] encoded (string length={len(item.embedding)}): "
f'"{item.embedding[:32]}...{item.embedding[-32:]}"'
)
# For display purposes, decode the string into a list of floating point numbers.
# Display the first and last two elements of the list.
decoded_embedding = decode_base64(item.embedding)
length = len(decoded_embedding)
print(
f"data[{item.index}] decoded (vector length={length}): "
f"[{decoded_embedding[0]}, {decoded_embedding[1]}, "
f"..., {decoded_embedding[length-2]}, {decoded_embedding[length-1]}]"
)
def decode_base64(base64_str):
"""
Helper function that decodes a base64 string and returns a list of floats.
Args:
base64_str (str): The base64 string to decode.
Returns:
List[float]: A list of floats decoded from the base64 string.
"""
import base64
import numpy
decoded_bytes = base64.b64decode(base64_str)
return numpy.frombuffer(decoded_bytes, dtype=numpy.float32)
if __name__ == "__main__":
sample_embeddings_with_base64_encoding()
|
