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Azure OpenAI

Integrate Azure-hosted OpenAI models through the Adaline Proxy to automatically capture telemetry — requests, responses, token usage, latency, and costs — with minimal code changes.

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Supported Models

Azure OpenAI supports any OpenAI model deployed in your Azure resource. Chat Models
ModelDescription
gpt-4oMultimodal with vision support
gpt-4o-miniFast and cost-effective multimodal
gpt-4-turboHigh capability with 128k context
gpt-4Original GPT-4 model
gpt-3.5-turboFast and cost-effective
Embedding Models
ModelDescription
text-embedding-3-largeHighest quality embeddings
text-embedding-3-smallBalanced quality and cost
text-embedding-ada-002Legacy embedding model
Models must be deployed in your Azure OpenAI resource before use. The model name corresponds to your Azure deployment name.

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Proxy Base URL

https://gateway.adaline.ai/v1/azure/

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Prerequisites

  1. An Azure OpenAI resource with deployed models
  2. Your Azure OpenAI API key, resource name, and deployment name
  3. An Adaline API key, project ID, and prompt ID

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Chat Completions

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Complete Chat

from openai import AzureOpenAI

client = AzureOpenAI(
    api_key="your-azure-openai-api-key",
    api_version="2024-02-01",
    azure_endpoint="https://gateway.adaline.ai/v1/azure/",
    azure_deployment="your-deployment-name"
)

headers = {
    "adaline-api-key": "your-adaline-api-key",
    "adaline-project-id": "your-project-id",
    "adaline-prompt-id": "your-prompt-id",
    "adaline-azure-resource-name": "your-resource-name"
}

response = client.chat.completions.create(
    model="gpt-4",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is cloud computing?"}
    ],
    extra_headers=headers
)

print(response.choices[0].message.content)
The adaline-azure-resource-name header is required for Azure OpenAI. This is the name of your Azure OpenAI resource (not the deployment name).

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Stream Chat

from openai import AzureOpenAI

client = AzureOpenAI(
    api_key="your-azure-openai-api-key",
    api_version="2024-02-01",
    azure_endpoint="https://gateway.adaline.ai/v1/azure/",
    azure_deployment="your-deployment-name"
)

headers = {
    "adaline-api-key": "your-adaline-api-key",
    "adaline-project-id": "your-project-id",
    "adaline-prompt-id": "your-prompt-id",
    "adaline-azure-resource-name": "your-resource-name"
}

stream = client.chat.completions.create(
    model="gpt-4",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain Azure services in detail."}
    ],
    stream=True,
    extra_headers=headers
)

for chunk in stream:
    if chunk.choices[0].delta.content is not None:
        print(chunk.choices[0].delta.content, end="")

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Embeddings

from openai import AzureOpenAI

client = AzureOpenAI(
    api_key="your-azure-openai-api-key",
    api_version="2024-02-01",
    azure_endpoint="https://gateway.adaline.ai/v1/azure/",
    azure_deployment="your-embedding-deployment-name"
)

headers = {
    "adaline-api-key": "your-adaline-api-key",
    "adaline-project-id": "your-project-id",
    "adaline-prompt-id": "your-prompt-id",
    "adaline-azure-resource-name": "your-resource-name"
}

response = client.embeddings.create(
    model="text-embedding-ada-002",
    input="The quick brown fox jumps over the lazy dog",
    extra_headers=headers
)

embedding = response.data[0].embedding
print(f"Embedding dimension: {len(embedding)}")

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Next Steps

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