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" # Deployed resource name in of the OpenAI LLM in Azure
}

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)

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" # Deployed resource name in of the OpenAI LLM in Azure
}

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="")

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" # Deployed resource name in of the OpenAI embedding model in Azure
}

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)}")