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|
# ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------
import os
import json
import azure.ai.inference as sdk
import azure.ai.inference.aio as async_sdk
from model_inference_test_base import (
ModelClientTestBase,
ServicePreparerChatCompletions,
ServicePreparerAOAIChatCompletions,
)
from devtools_testutils.aio import recorded_by_proxy_async
from azure.core.exceptions import ServiceRequestError
from azure.core.credentials import AzureKeyCredential
# The test class name needs to start with "Test" to get collected by pytest
class TestChatCompletionsClientAsync(ModelClientTestBase):
# **********************************************************************************
#
# CHAT COMPLETIONS REGRESSION TESTS - NO SERVICE RESPONSE REQUIRED
#
# **********************************************************************************
# Regression test. Send a request that includes all supported types of input objects. Make sure the resulting
# JSON payload that goes up to the service (including headers) is the correct one after hand-inspection.
@ServicePreparerChatCompletions() # Not sure why this is needed. It errors out if not present. We don't use the env variables in this test.
async def test_async_chat_completions_request_payload(self, **kwargs):
client = async_sdk.ChatCompletionsClient(
endpoint="http://does.not.exist",
credential=AzureKeyCredential("key-value"),
headers={"some_header": "some_header_value"},
user_agent="MyAppId",
)
for _ in range(2):
try:
_ = await client.complete(
messages=[
sdk.models.SystemMessage(content="system prompt"),
sdk.models.UserMessage(content="user prompt 1"),
sdk.models.AssistantMessage(
tool_calls=[
sdk.models.ChatCompletionsToolCall(
function=sdk.models.FunctionCall(
name="my-first-function-name",
arguments={"first_argument": "value1", "second_argument": "value2"},
),
id="some-id",
),
sdk.models.ChatCompletionsToolCall(
function=sdk.models.FunctionCall(
name="my-second-function-name", arguments={"first_argument": "value1"}
),
id="some-other-id",
),
]
),
sdk.models.ToolMessage(tool_call_id="some id", content="function response"),
sdk.models.AssistantMessage(content="assistant prompt"),
sdk.models.UserMessage(
content=[
sdk.models.TextContentItem(text="user prompt 2"),
sdk.models.ImageContentItem(
image_url=sdk.models.ImageUrl(
url="https://does.not.exit/image.png",
detail=sdk.models.ImageDetailLevel.HIGH,
),
),
],
),
],
model_extras={
"key1": 1,
"key2": True,
"key3": "Some value",
"key4": [1, 2, 3],
"key5": {"key6": 2, "key7": False, "key8": "Some other value", "key9": [4, 5, 6, 7]},
},
frequency_penalty=0.123,
max_tokens=321,
model="some-model-id",
presence_penalty=4.567,
response_format="json_object",
seed=654,
stop=["stop1", "stop2"],
stream=True,
temperature=8.976,
tool_choice=sdk.models.ChatCompletionsToolChoicePreset.AUTO,
tools=[ModelClientTestBase.TOOL1, ModelClientTestBase.TOOL2],
top_p=9.876,
raw_request_hook=self.request_callback,
)
await client.close()
assert False
except ServiceRequestError as _:
# The test should throw this exception!
self._validate_chat_completions_json_request_payload()
continue
await client.close()
# Regression test. Send a request that includes all supported types of input objects, with chat settings
# specified in the constructor. Make sure the resulting JSON payload that goes up to the service
# is the correct one after hand-inspection.
@ServicePreparerChatCompletions() # Not sure why this is needed. It errors out if not present. We don't use the env variables in this test.
async def test_async_chat_completions_request_payload_with_defaults(self, **kwargs):
client = async_sdk.ChatCompletionsClient(
endpoint="http://does.not.exist",
credential=AzureKeyCredential("key-value"),
headers={"some_header": "some_header_value"},
user_agent="MyAppId",
model_extras={
"key1": 1,
"key2": True,
"key3": "Some value",
"key4": [1, 2, 3],
"key5": {"key6": 2, "key7": False, "key8": "Some other value", "key9": [4, 5, 6, 7]},
},
frequency_penalty=0.123,
max_tokens=321,
model="some-model-id",
presence_penalty=4.567,
response_format="json_object",
seed=654,
stop=["stop1", "stop2"],
temperature=8.976,
tool_choice=sdk.models.ChatCompletionsToolChoicePreset.AUTO,
tools=[ModelClientTestBase.TOOL1, ModelClientTestBase.TOOL2],
top_p=9.876,
)
for _ in range(2):
try:
_ = await client.complete(
messages=[
sdk.models.SystemMessage("system prompt"),
sdk.models.UserMessage("user prompt 1"),
sdk.models.AssistantMessage(
tool_calls=[
sdk.models.ChatCompletionsToolCall(
function=sdk.models.FunctionCall(
name="my-first-function-name",
arguments={"first_argument": "value1", "second_argument": "value2"},
),
id="some-id",
),
sdk.models.ChatCompletionsToolCall(
function=sdk.models.FunctionCall(
name="my-second-function-name", arguments={"first_argument": "value1"}
),
id="some-other-id",
),
]
),
sdk.models.ToolMessage("function response", tool_call_id="some id"),
sdk.models.AssistantMessage("assistant prompt"),
sdk.models.UserMessage(
[
sdk.models.TextContentItem(text="user prompt 2"),
sdk.models.ImageContentItem(
image_url=sdk.models.ImageUrl(
url="https://does.not.exit/image.png",
detail=sdk.models.ImageDetailLevel.HIGH,
),
),
],
),
],
stream=True,
raw_request_hook=self.request_callback,
)
await client.close()
assert False
except ServiceRequestError as _:
# The test should throw this exception!
self._validate_chat_completions_json_request_payload()
continue
await client.close()
# Regression test. Send a request that includes all supported types of input objects, with chat settings
# specified in the constructor and all of them overwritten in the 'complete' call.
# Make sure the resulting JSON payload that goes up to the service is the correct one after hand-inspection.
@ServicePreparerChatCompletions() # Not sure why this is needed. It errors out if not present. We don't use the env variables in this test.
async def test_async_chat_completions_request_payload_with_defaults_and_overrides(self, **kwargs):
client = async_sdk.ChatCompletionsClient(
endpoint="http://does.not.exist",
credential=AzureKeyCredential("key-value"),
headers={"some_header": "some_header_value"},
user_agent="MyAppId",
model_extras={
"key1": 2,
"key3": False,
"key4": "Some other value",
"key9": "Yet another value",
},
frequency_penalty=0.456,
max_tokens=768,
model="some-other-model-id",
presence_penalty=1.234,
response_format="text",
seed=987,
stop=["stop3", "stop5"],
temperature=5.432,
tool_choice=sdk.models.ChatCompletionsToolChoicePreset.REQUIRED,
tools=[ModelClientTestBase.TOOL2],
top_p=3.456,
)
for _ in range(2):
try:
_ = await client.complete(
messages=[
sdk.models.SystemMessage(content="system prompt"),
sdk.models.UserMessage(content="user prompt 1"),
sdk.models.AssistantMessage(
tool_calls=[
sdk.models.ChatCompletionsToolCall(
function=sdk.models.FunctionCall(
name="my-first-function-name",
arguments={"first_argument": "value1", "second_argument": "value2"},
),
id="some-id",
),
sdk.models.ChatCompletionsToolCall(
function=sdk.models.FunctionCall(
name="my-second-function-name", arguments={"first_argument": "value1"}
),
id="some-other-id",
),
]
),
sdk.models.ToolMessage(tool_call_id="some id", content="function response"),
sdk.models.AssistantMessage(content="assistant prompt"),
sdk.models.UserMessage(
content=[
sdk.models.TextContentItem(text="user prompt 2"),
sdk.models.ImageContentItem(
image_url=sdk.models.ImageUrl(
url="https://does.not.exit/image.png",
detail=sdk.models.ImageDetailLevel.HIGH,
),
),
],
),
],
model_extras={
"key1": 1,
"key2": True,
"key3": "Some value",
"key4": [1, 2, 3],
"key5": {"key6": 2, "key7": False, "key8": "Some other value", "key9": [4, 5, 6, 7]},
},
frequency_penalty=0.123,
max_tokens=321,
model="some-model-id",
presence_penalty=4.567,
response_format="json_object",
seed=654,
stop=["stop1", "stop2"],
stream=True,
temperature=8.976,
tool_choice=sdk.models.ChatCompletionsToolChoicePreset.AUTO,
tools=[ModelClientTestBase.TOOL1, ModelClientTestBase.TOOL2],
top_p=9.876,
raw_request_hook=self.request_callback,
)
await client.close()
assert False
except ServiceRequestError as _:
# The test should throw this exception!
self._validate_chat_completions_json_request_payload()
continue
await client.close()
# **********************************************************************************
#
# HAPPY PATH TESTS - CHAT COMPLETIONS
#
# **********************************************************************************
@ServicePreparerChatCompletions()
@recorded_by_proxy_async
async def test_async_load_chat_completions_client(self, **kwargs):
client = await self._load_async_chat_client(**kwargs)
assert isinstance(client, async_sdk.ChatCompletionsClient)
assert client._model_info
response1 = await client.get_model_info()
self._print_model_info_result(response1)
self._validate_model_info_result(
response1, "chat-completion" # TODO: This should be chat_completions based on REST API spec...
) # TODO: This should be ModelType.CHAT_COMPLETION once the model is fixed
await client.close()
@ServicePreparerChatCompletions()
@recorded_by_proxy_async
async def test_async_get_model_info_on_chat_client(self, **kwargs):
client = self._create_async_chat_client(**kwargs)
assert not client._model_info # pylint: disable=protected-access
response1 = await client.get_model_info()
assert client._model_info # pylint: disable=protected-access
self._print_model_info_result(response1)
self._validate_model_info_result(
response1, "chat-completion"
) # TODO: This should be ModelType.CHAT_COMPLETION once the model is fixed
# Get the model info again. No network calls should be made here,
# as the response is cached in the client.
response2 = await client.get_model_info()
self._print_model_info_result(response2)
assert response1 == response2
await client.close()
@ServicePreparerChatCompletions()
@recorded_by_proxy_async
async def test_async_chat_completions_with_entra_id_auth(self, **kwargs):
async with self._create_async_chat_client(key_auth=False, **kwargs) as client:
messages = [
sdk.models.SystemMessage(
content="You are a helpful assistant answering questions regarding length units."
),
sdk.models.UserMessage(content="How many feet are in a mile?"),
]
response = await client.complete(messages=messages)
self._print_chat_completions_result(response)
self._validate_chat_completions_result(response, ["5280", "5,280"])
assert json.dumps(response.as_dict(), indent=2) == response.__str__()
@ServicePreparerChatCompletions()
@recorded_by_proxy_async
async def test_async_chat_completions_multi_turn(self, **kwargs):
messages = [
sdk.models.SystemMessage(content="You are a helpful assistant answering questions regarding length units."),
sdk.models.UserMessage(content="How many feet are in a mile?"),
]
async with self._create_async_chat_client(**kwargs) as client:
response = await client.complete(messages=messages)
self._print_chat_completions_result(response)
self._validate_chat_completions_result(response, ["5280", "5,280"])
assert json.dumps(response.as_dict(), indent=2) == response.__str__()
messages.append(sdk.models.AssistantMessage(content=response.choices[0].message.content))
messages.append(sdk.models.UserMessage(content="and how many yards?"))
response = await client.complete(messages=messages)
self._print_chat_completions_result(response)
self._validate_chat_completions_result(response, ["1760", "1,760"])
@ServicePreparerChatCompletions()
@recorded_by_proxy_async
async def test_async_chat_completions_with_model_extras(self, **kwargs):
client = self._create_async_chat_client(**kwargs)
response = await client.complete(
messages=[sdk.models.UserMessage(content="How many feet are in a mile?")],
model_extras={"n": 1},
raw_request_hook=self.request_callback,
)
self._print_chat_completions_result(response)
self._validate_chat_completions_result(response, ["5280", "5,280"])
self._validate_model_extras(self.pipeline_request.http_request.data, self.pipeline_request.http_request.headers)
await client.close()
@ServicePreparerChatCompletions()
@recorded_by_proxy_async
async def test_async_chat_completions_streaming(self, **kwargs):
async with self._create_async_chat_client(Sync=False, **kwargs) as client:
async with await client.complete(
stream=True,
messages=[
sdk.models.SystemMessage(content="You are a helpful assistant."),
sdk.models.UserMessage(content="Give me 3 good reasons why I should exercise every day."),
],
) as response:
await self._validate_async_chat_completions_streaming_result(response)
@ServicePreparerChatCompletions()
@recorded_by_proxy_async
async def test_async_chat_completions_with_json_input(self, **kwargs):
client = self._create_async_chat_client(**kwargs)
request_body = {
"messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "How many feet are in a mile?"},
]
}
response = await client.complete(request_body)
self._validate_chat_completions_result(response, ["5280", "5,280"])
await client.close()
@ServicePreparerChatCompletions()
@recorded_by_proxy_async
async def test_async_chat_completions_with_bytes_input(self, **kwargs):
client = self._create_async_chat_client(**kwargs)
response = await client.complete(self._read_text_file("chat.test.json"))
self._validate_chat_completions_result(response, ["5280", "5,280"])
await client.close()
@ServicePreparerChatCompletions()
@recorded_by_proxy_async
async def test_async_chat_completions_streaming_with_json_input(self, **kwargs):
client = self._create_async_chat_client(**kwargs)
request_body = {
"messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Give me 3 good reasons why I should exercise every day."},
],
"stream": True,
}
response = await client.complete(request_body)
await self._validate_async_chat_completions_streaming_result(response)
await client.close()
# We use AOAI endpoint here because at the moment there is no MaaS model that supports
# input image.
@ServicePreparerAOAIChatCompletions()
@recorded_by_proxy_async
async def test_async_chat_completions_with_input_image_file(self, **kwargs):
client = self._create_async_aoai_chat_client(**kwargs)
# Construct the full path to the image file
script_dir = os.path.dirname(os.path.abspath(__file__))
image_file_path = os.path.join(script_dir, "test_image1.png")
response = await client.complete(
messages=[
sdk.models.SystemMessage(content="You are an AI assistant that describes images in details."),
sdk.models.UserMessage(
content=[
sdk.models.TextContentItem(text="What's in this image?"),
sdk.models.ImageContentItem(
image_url=sdk.models.ImageUrl.load(
image_file=image_file_path,
image_format="png",
detail=sdk.models.ImageDetailLevel.HIGH,
),
),
],
),
],
)
self._print_chat_completions_result(response)
self._validate_chat_completions_result(
response, ["juggling", "balls", "blue", "red", "green", "yellow"], is_aoai=True
)
await client.close()
# We use AOAI endpoint here because at the moment there is no MaaS model that supports
# input image.
@ServicePreparerAOAIChatCompletions()
@recorded_by_proxy_async
async def test_async_chat_completions_with_input_image_url(self, **kwargs):
url = "https://raw.githubusercontent.com/Azure/azure-sdk-for-python/main/sdk/ai/azure-ai-inference/tests/test_image1.png"
client = self._create_async_aoai_chat_client(**kwargs)
response = await client.complete(
messages=[
sdk.models.SystemMessage(content="You are an AI assistant that describes images in details."),
sdk.models.UserMessage(
content=[
sdk.models.TextContentItem(text="What's in this image?"),
sdk.models.ImageContentItem(
image_url=sdk.models.ImageUrl(url=url, detail=sdk.models.ImageDetailLevel.AUTO)
),
],
),
],
)
self._print_chat_completions_result(response)
self._validate_chat_completions_result(
response, ["juggling", "balls", "blue", "red", "green", "yellow"], is_aoai=True
)
await client.close()
# We use AOAI endpoint here because at the moment MaaS does not support Entra ID auth.
@ServicePreparerAOAIChatCompletions()
@recorded_by_proxy_async
async def test_async_aoai_chat_completions_with_entra_id_auth(self, **kwargs):
client = self._create_async_aoai_chat_client(key_auth=False, **kwargs)
messages = [
sdk.models.SystemMessage(content="You are a helpful assistant answering questions regarding length units."),
sdk.models.UserMessage(content="How many feet are in a mile?"),
]
response = await client.complete(messages=messages)
self._print_chat_completions_result(response)
self._validate_chat_completions_result(response, ["5280", "5,280"], is_aoai=True)
await client.close()
@ServicePreparerAOAIChatCompletions()
@recorded_by_proxy_async
async def test_async_aoai_chat_completions_with_structured_output(self, **kwargs):
client = self._create_async_aoai_chat_client(key_auth=True, **kwargs)
response_format = sdk.models.JsonSchemaFormat(
name="Test_JSON_Schema",
schema=ModelClientTestBase.OUTPUT_FORMAT_JSON_SCHEMA,
description="Describes a set of distances between locations",
strict=True,
)
print(type(response_format))
messages = [
sdk.models.SystemMessage(content="You are a helpful assistant answering questions on US geography"),
sdk.models.UserMessage(content="What's the distance between Seattle and Portland, as the crow flies?"),
]
response = await client.complete(messages=messages, response_format=response_format)
self._print_chat_completions_result(response)
self._validate_chat_completions_result(
response, ["distances", "location1", "Seattle", "location2", "Portland"], is_aoai=True, is_json=True
)
await client.close()
# We use AOAI endpoint here because at the moment there is no MaaS model that supports
# input audio.
@ServicePreparerAOAIChatCompletions()
@recorded_by_proxy_async
async def test_chat_completions_with_audio_input(self, **kwargs):
client = self._create_async_aoai_audio_chat_client(**kwargs)
# Construct the full path to the image file
script_dir = os.path.dirname(os.path.abspath(__file__))
audio_file_path = os.path.join(script_dir, "hello_how_are_you.mp3")
response = await client.complete(
messages=[
sdk.models.SystemMessage(
content="You are an AI assistant for translating and transcribing audio clips."
),
sdk.models.UserMessage(
content=[
sdk.models.TextContentItem(text="Please translate this audio snippet to spanish."),
sdk.models.AudioContentItem(
input_audio=sdk.models.InputAudio.load(
audio_file=audio_file_path, audio_format=sdk.models.AudioContentFormat.MP3
)
),
],
),
],
)
self._print_chat_completions_result(response)
self._validate_chat_completions_result(response, ["Hola", "cómo", "estás"], is_aoai=True)
await client.close()
|
