import os
from openai.azure import AzureOpenAIClient
from azure.identity import DefaultAzureCredential

endpoint = os.environ["AZURE_OPENAI_ENDPOINT"]
deployment_id = "gpt-35-turbo-16k"

client = AzureOpenAIClient(
    credential=DefaultAzureCredential(),
    base_url=f"{endpoint}/openai/deployments/{deployment_id}/",
)

completion = client.chat.completions.create(
    model=deployment_id,
    messages=[
        {
            "role": "user",
            "content": "How do I output all files in a directory using Python?",
        },
    ]
)
response = completion.model_dump_json()  # Access response as dict
print(completion["choices"][0]["message"]["content"])

import os
from openai.azure import AzureOpenAIClient
from azure.identity import DefaultAzureCredential

endpoint = os.environ["AZURE_OPENAI_ENDPOINT"]
deployment_id = "gpt-35-turbo-16k"

client = AzureOpenAIClient(
    credential=DefaultAzureCredential(),
    base_url=f"{endpoint}/openai/deployments/{deployment_id}/extensions",
)

completion = client.chat.completions.create(
    model=deployment_id,
    messages=[
        {
            "role": "user",
            "content": "How is Azure machine learning different than Azure OpenAI?",
        },
    ],
    extra_body={
        "dataSources": [
            {
                "type": "AzureCognitiveSearch",
                "parameters": {
                    "endpoint": os.environ["AZURE_SEARCH_ENDPOINT"],
                    "key": os.environ["AZURE_SEARCH_KEY"],
                    "indexName": os.environ["AZURE_SEARCH_INDEX"]
                }
            }
        ]
    }
)
# Access response as dict (example above) or access by attribute with Azure-only properties stored in `model_extra`
assert completion.id
assert completion.object == "extensions.chat.completion"
assert completion.created
assert completion.model
assert len(completion.choices) == 1
assert completion.choices[0].finish_reason
assert completion.choices[0].index is not None
assert completion.choices[0].message.content
assert completion.choices[0].message.role

# model_extra for context
assert completion.choices[0].message.model_extra["context"]["messages"][0]["role"] == "tool"
assert completion.choices[0].message.model_extra["context"]["messages"][0]["content"]

import os
from openai.azure import AzureOpenAIClient

endpoint = os.environ["AZURE_OPENAI_ENDPOINT"]
api_key = os.environ["AZURE_OPENAI_KEY"]
deployment_id = "text-davinci-003"

client = AzureOpenAIClient(
    api_key=api_key,
    base_url=f"{endpoint}/openai/deployments/{deployment_id}/",
)

completion = client.completions.create(
    model=deployment_id,
    prompt="How do I bake a chocolate cake?",
)
assert completion.id
assert completion.object == "text_completion"
assert completion.created
assert completion.model
assert completion.usage.completion_tokens is not None
assert completion.usage.prompt_tokens is not None
assert completion.usage.total_tokens == completion.usage.completion_tokens + completion.usage.prompt_tokens
assert len(completion.choices) == 1
assert completion.choices[0].finish_reason
assert completion.choices[0].index is not None
assert completion.choices[0].text

# model extra for content filter results
prompt_filter_result = completion.model_extra["prompt_filter_results"][0]["content_filter_results"]
assert prompt_filter_result["hate"]["filtered"] is False
assert prompt_filter_result["hate"]["severity"] == "safe"
assert prompt_filter_result["self_harm"]["filtered"] is False
assert prompt_filter_result["self_harm"]["severity"] == "safe"
assert prompt_filter_result["sexual"]["filtered"] is False
assert prompt_filter_result["sexual"]["severity"] == "safe"
assert prompt_filter_result["violence"]["filtered"] is False
assert prompt_filter_result["violence"]["severity"] == "safe"

import os
from openai.azure import AzureOpenAIClient

endpoint = os.environ["AZURE_OPENAI_ENDPOINT"]
api_key = os.environ["AZURE_OPENAI_KEY"]
deployment_id = "text-embedding-ada-002"

client = AzureOpenAIClient(
    api_key=api_key,
    base_url=f"{endpoint}/openai/deployments/{deployment_id}/",
)

embeddings = client.embeddings.create(
    model=deployment_id,
    input="hello world"
)
assert embedding.object == "list"
assert embedding.model
assert embedding.usage.prompt_tokens is not None
assert embedding.usage.total_tokens is not None
assert len(embedding.data) == 1
assert embedding.data[0].object == "embedding"
assert embedding.data[0].index is not None
assert len(embedding.data[0].embedding) > 0

import os
from openai.azure import AzureOpenAIClient

endpoint = os.environ["AZURE_OPENAI_ENDPOINT"]
api_key = os.environ["AZURE_OPENAI_KEY"]
deployment_id = "whisper-1"

client = AzureOpenAIClient(
    api_key=api_key,
    base_url=f"{endpoint}/openai/deployments/{deployment_id}/",
)

audio = client.audio.transcriptions.create(
    model=deployment_id,
    file=open("hello.m4a", "rb").read()
)
assert result.text == "Hello."