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test_client_tracing.py
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# pylint: disable=too-many-lines
# ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------
import json
import os
import azure.ai.inference as sdk
from azure.ai.inference.tracing import AIInferenceInstrumentor
from model_inference_test_base import (
ModelClientTestBase,
ServicePreparerChatCompletions,
)
from azure.core.settings import settings
from devtools_testutils import recorded_by_proxy
from memory_trace_exporter import MemoryTraceExporter
from gen_ai_trace_verifier import GenAiTraceVerifier
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import SimpleSpanProcessor
CONTENT_TRACING_ENV_VARIABLE = "AZURE_TRACING_GEN_AI_CONTENT_RECORDING_ENABLED"
content_tracing_initial_value = os.getenv(CONTENT_TRACING_ENV_VARIABLE)
# The test class name needs to start with "Test" to get collected by pytest
class TestClientTracing(ModelClientTestBase):
@classmethod
def teardown_class(cls):
if content_tracing_initial_value is not None:
os.environ[CONTENT_TRACING_ENV_VARIABLE] = content_tracing_initial_value
# **********************************************************************************
#
# TRACING TESTS - CHAT COMPLETIONS
#
# **********************************************************************************
def setup_memory_trace_exporter(self) -> MemoryTraceExporter:
# Setup Azure Core settings to use OpenTelemetry tracing
settings.tracing_implementation = "OpenTelemetry"
trace.set_tracer_provider(TracerProvider())
tracer = trace.get_tracer(__name__)
memoryExporter = MemoryTraceExporter()
span_processor = SimpleSpanProcessor(memoryExporter)
trace.get_tracer_provider().add_span_processor(span_processor)
return span_processor, memoryExporter
def modify_env_var(self, name, new_value):
current_value = os.getenv(name)
os.environ[name] = new_value
return current_value
@ServicePreparerChatCompletions()
@recorded_by_proxy
def test_instrumentation(self, **kwargs):
# Make sure code is not instrumented due to a previous test exception
try:
AIInferenceInstrumentor().uninstrument()
except RuntimeError as e:
pass
client = self._create_chat_client(**kwargs)
exception_caught = False
try:
assert AIInferenceInstrumentor().is_instrumented() == False
AIInferenceInstrumentor().instrument()
assert AIInferenceInstrumentor().is_instrumented() == True
AIInferenceInstrumentor().uninstrument()
assert AIInferenceInstrumentor().is_instrumented() == False
except RuntimeError as e:
exception_caught = True
print(e)
client.close()
assert exception_caught == False
@ServicePreparerChatCompletions()
@recorded_by_proxy
def test_instrumenting_twice_does_not_cause_exception(self, **kwargs):
# Make sure code is not instrumented due to a previous test exception
try:
AIInferenceInstrumentor().uninstrument()
except RuntimeError as e:
pass
client = self._create_chat_client(**kwargs)
exception_caught = False
try:
AIInferenceInstrumentor().instrument()
AIInferenceInstrumentor().instrument()
except RuntimeError as e:
exception_caught = True
print(e)
AIInferenceInstrumentor().uninstrument()
client.close()
assert exception_caught == False
@ServicePreparerChatCompletions()
@recorded_by_proxy
def test_uninstrumenting_uninstrumented_does_not_cause_exception(self, **kwargs):
# Make sure code is not instrumented due to a previous test exception
try:
AIInferenceInstrumentor().uninstrument()
except RuntimeError as e:
pass
client = self._create_chat_client(**kwargs)
exception_caught = False
try:
AIInferenceInstrumentor().uninstrument()
except RuntimeError as e:
exception_caught = True
print(e)
client.close()
assert exception_caught == False
@ServicePreparerChatCompletions()
@recorded_by_proxy
def test_uninstrumenting_twice_does_not_cause_exception(self, **kwargs):
# Make sure code is not instrumented due to a previous test exception
try:
AIInferenceInstrumentor().uninstrument()
except RuntimeError as e:
pass
client = self._create_chat_client(**kwargs)
exception_caught = False
uninstrumented_once = False
try:
AIInferenceInstrumentor().instrument()
AIInferenceInstrumentor().uninstrument()
AIInferenceInstrumentor().uninstrument()
except RuntimeError as e:
exception_caught = True
print(e)
client.close()
assert exception_caught == False
@ServicePreparerChatCompletions()
@recorded_by_proxy
def test_is_content_recording_enabled(self, **kwargs):
# Make sure code is not instrumented due to a previous test exception
try:
AIInferenceInstrumentor().uninstrument()
except RuntimeError as e:
pass
self.modify_env_var(CONTENT_TRACING_ENV_VARIABLE, "False")
client = self._create_chat_client(**kwargs)
exception_caught = False
uninstrumented_once = False
try:
# From environment variable instrumenting from uninstrumented
AIInferenceInstrumentor().instrument()
self.modify_env_var(CONTENT_TRACING_ENV_VARIABLE, "False")
AIInferenceInstrumentor().instrument()
content_recording_enabled = AIInferenceInstrumentor().is_content_recording_enabled()
assert content_recording_enabled == False
AIInferenceInstrumentor().uninstrument()
self.modify_env_var(CONTENT_TRACING_ENV_VARIABLE, "True")
AIInferenceInstrumentor().instrument()
content_recording_enabled = AIInferenceInstrumentor().is_content_recording_enabled()
assert content_recording_enabled == True
AIInferenceInstrumentor().uninstrument()
self.modify_env_var(CONTENT_TRACING_ENV_VARIABLE, "invalid")
AIInferenceInstrumentor().instrument()
content_recording_enabled = AIInferenceInstrumentor().is_content_recording_enabled()
assert content_recording_enabled == False
# From environment variable instrumenting from instrumented
self.modify_env_var(CONTENT_TRACING_ENV_VARIABLE, "True")
AIInferenceInstrumentor().instrument()
content_recording_enabled = AIInferenceInstrumentor().is_content_recording_enabled()
assert content_recording_enabled == True
self.modify_env_var(CONTENT_TRACING_ENV_VARIABLE, "True")
AIInferenceInstrumentor().instrument()
content_recording_enabled = AIInferenceInstrumentor().is_content_recording_enabled()
assert content_recording_enabled == True
self.modify_env_var(CONTENT_TRACING_ENV_VARIABLE, "invalid")
AIInferenceInstrumentor().instrument()
content_recording_enabled = AIInferenceInstrumentor().is_content_recording_enabled()
assert content_recording_enabled == False
# From parameter instrumenting from uninstrumented
AIInferenceInstrumentor().uninstrument()
self.modify_env_var(CONTENT_TRACING_ENV_VARIABLE, "True")
AIInferenceInstrumentor().instrument(enable_content_recording=False)
content_recording_enabled = AIInferenceInstrumentor().is_content_recording_enabled()
assert content_recording_enabled == False
AIInferenceInstrumentor().uninstrument()
self.modify_env_var(CONTENT_TRACING_ENV_VARIABLE, "False")
AIInferenceInstrumentor().instrument(enable_content_recording=True)
content_recording_enabled = AIInferenceInstrumentor().is_content_recording_enabled()
assert content_recording_enabled == True
# From parameter instrumenting from instrumented
self.modify_env_var(CONTENT_TRACING_ENV_VARIABLE, "True")
AIInferenceInstrumentor().instrument(enable_content_recording=False)
content_recording_enabled = AIInferenceInstrumentor().is_content_recording_enabled()
assert content_recording_enabled == False
self.modify_env_var(CONTENT_TRACING_ENV_VARIABLE, "False")
AIInferenceInstrumentor().instrument(enable_content_recording=True)
content_recording_enabled = AIInferenceInstrumentor().is_content_recording_enabled()
assert content_recording_enabled == True
except RuntimeError as e:
exception_caught = True
print(e)
client.close()
assert exception_caught == False
@ServicePreparerChatCompletions()
@recorded_by_proxy
def test_chat_completion_tracing_content_recording_disabled(self, **kwargs):
# Make sure code is not instrumented due to a previous test exception
try:
AIInferenceInstrumentor().uninstrument()
except RuntimeError as e:
pass
self.modify_env_var(CONTENT_TRACING_ENV_VARIABLE, "False")
client = self._create_chat_client(**kwargs)
model = kwargs.pop("azure_ai_chat_model").lower()
processor, exporter = self.setup_memory_trace_exporter()
AIInferenceInstrumentor().instrument()
response = client.complete(
messages=[
sdk.models.SystemMessage(content="You are a helpful assistant."),
sdk.models.UserMessage(content="What is the capital of France?"),
],
)
processor.force_flush()
spans = exporter.get_spans_by_name_starts_with("chat ")
if len(spans) == 0:
spans = exporter.get_spans_by_name("chat")
assert len(spans) == 1
span = spans[0]
expected_attributes = [
("gen_ai.operation.name", "chat"),
("gen_ai.system", "az.ai.inference"),
("gen_ai.request.model", "chat"),
("server.address", ""),
("gen_ai.response.id", ""),
("gen_ai.response.model", model),
("gen_ai.usage.input_tokens", "+"),
("gen_ai.usage.output_tokens", "+"),
("gen_ai.response.finish_reasons", ("stop",)),
]
attributes_match = GenAiTraceVerifier().check_span_attributes(span, expected_attributes)
assert attributes_match == True
expected_events = [
{
"name": "gen_ai.choice",
"attributes": {
"gen_ai.system": "az.ai.inference",
"gen_ai.event.content": '{"finish_reason": "stop", "index": 0}',
},
}
]
events_match = GenAiTraceVerifier().check_span_events(span, expected_events)
assert events_match == True
AIInferenceInstrumentor().uninstrument()
@ServicePreparerChatCompletions()
@recorded_by_proxy
def test_chat_completion_tracing_content_recording_enabled(self, **kwargs):
# Make sure code is not instrumented due to a previous test exception
try:
AIInferenceInstrumentor().uninstrument()
except RuntimeError as e:
pass
self.modify_env_var(CONTENT_TRACING_ENV_VARIABLE, "True")
client = self._create_chat_client(**kwargs)
model = kwargs.pop("azure_ai_chat_model").lower()
processor, exporter = self.setup_memory_trace_exporter()
AIInferenceInstrumentor().instrument()
response = client.complete(
messages=[
sdk.models.SystemMessage(content="You are a helpful assistant."),
sdk.models.UserMessage(content="What is the capital of France?"),
],
)
processor.force_flush()
spans = exporter.get_spans_by_name_starts_with("chat ")
if len(spans) == 0:
spans = exporter.get_spans_by_name("chat")
assert len(spans) == 1
span = spans[0]
expected_attributes = [
("gen_ai.operation.name", "chat"),
("gen_ai.system", "az.ai.inference"),
("gen_ai.request.model", "chat"),
("server.address", ""),
("gen_ai.response.id", ""),
("gen_ai.response.model", model),
("gen_ai.usage.input_tokens", "+"),
("gen_ai.usage.output_tokens", "+"),
("gen_ai.response.finish_reasons", ("stop",)),
]
attributes_match = GenAiTraceVerifier().check_span_attributes(span, expected_attributes)
assert attributes_match == True
expected_events = [
{
"name": "gen_ai.system.message",
"attributes": {
"gen_ai.system": "az.ai.inference",
"gen_ai.event.content": '{"role": "system", "content": "You are a helpful assistant."}',
},
},
{
"name": "gen_ai.user.message",
"attributes": {
"gen_ai.system": "az.ai.inference",
"gen_ai.event.content": '{"role": "user", "content": "What is the capital of France?"}',
},
},
{
"name": "gen_ai.choice",
"attributes": {
"gen_ai.system": "az.ai.inference",
"gen_ai.event.content": '{"message": {"content": "*"}, "finish_reason": "stop", "index": 0}',
},
},
]
events_match = GenAiTraceVerifier().check_span_events(span, expected_events)
assert events_match == True
AIInferenceInstrumentor().uninstrument()
@ServicePreparerChatCompletions()
@recorded_by_proxy
def test_chat_completion_tracing_content_unicode(self, **kwargs):
# Make sure code is not instrumented due to a previous test exception
try:
AIInferenceInstrumentor().uninstrument()
except RuntimeError as e:
pass
self.modify_env_var(CONTENT_TRACING_ENV_VARIABLE, "True")
client = self._create_chat_client(**kwargs)
processor, exporter = self.setup_memory_trace_exporter()
AIInferenceInstrumentor().instrument()
response = client.complete(
messages=[
sdk.models.SystemMessage(content="You are a helpful assistant."),
sdk.models.UserMessage(content="将“hello world”翻译成中文和乌克兰语"),
],
)
processor.force_flush()
spans = exporter.get_spans_by_name_starts_with("chat")
assert len(spans) == 1
expected_events = [
{
"name": "gen_ai.system.message",
"attributes": {
"gen_ai.system": "az.ai.inference",
"gen_ai.event.content": '{"role": "system", "content": "You are a helpful assistant."}',
},
},
{
"name": "gen_ai.user.message",
"attributes": {
"gen_ai.system": "az.ai.inference",
"gen_ai.event.content": '{"role": "user", "content": "将“hello world”翻译成中文和乌克兰语"}',
},
},
{
"name": "gen_ai.choice",
"attributes": {
"gen_ai.system": "az.ai.inference",
"gen_ai.event.content": '{"message": {"content": "*"}, "finish_reason": "stop", "index": 0}',
},
},
]
events_match = GenAiTraceVerifier().check_span_events(spans[0], expected_events)
assert events_match == True
completion_event_content = json.loads(spans[0].events[2].attributes["gen_ai.event.content"])
assert False == completion_event_content["message"]["content"].isascii()
assert response.choices[0].message.content == completion_event_content["message"]["content"]
AIInferenceInstrumentor().uninstrument()
@ServicePreparerChatCompletions()
@recorded_by_proxy
def test_chat_completion_streaming_tracing_content_recording_disabled(self, **kwargs):
# Make sure code is not instrumented due to a previous test exception
try:
AIInferenceInstrumentor().uninstrument()
except RuntimeError as e:
pass
self.modify_env_var(CONTENT_TRACING_ENV_VARIABLE, "False")
client = self._create_chat_client(**kwargs)
model = kwargs.pop("azure_ai_chat_model").lower()
processor, exporter = self.setup_memory_trace_exporter()
AIInferenceInstrumentor().instrument()
response = client.complete(
messages=[
sdk.models.SystemMessage(content="You are a helpful assistant."),
sdk.models.UserMessage(content="What is the capital of France?"),
],
stream=True,
)
response_content = ""
for update in response:
if update.choices and update.choices[0].delta.content:
response_content = response_content + update.choices[0].delta.content
client.close()
processor.force_flush()
spans = exporter.get_spans_by_name_starts_with("chat")
assert len(spans) == 1
span = spans[0]
expected_attributes = [
("gen_ai.operation.name", "chat"),
("gen_ai.system", "az.ai.inference"),
("gen_ai.request.model", "chat"),
("server.address", ""),
("gen_ai.response.id", ""),
("gen_ai.response.model", model),
("gen_ai.usage.input_tokens", "+"),
("gen_ai.usage.output_tokens", "+"),
("gen_ai.response.finish_reasons", ("stop",)),
]
attributes_match = GenAiTraceVerifier().check_span_attributes(span, expected_attributes)
assert attributes_match == True
expected_events = [
{
"name": "gen_ai.choice",
"attributes": {
"gen_ai.system": "az.ai.inference",
"gen_ai.event.content": '{"finish_reason": "stop", "index": 0}',
},
}
]
events_match = GenAiTraceVerifier().check_span_events(span, expected_events)
assert events_match == True
AIInferenceInstrumentor().uninstrument()
@ServicePreparerChatCompletions()
@recorded_by_proxy
def test_chat_completion_streaming_tracing_content_recording_enabled(self, **kwargs):
# Make sure code is not instrumented due to a previous test exception
try:
AIInferenceInstrumentor().uninstrument()
except RuntimeError as e:
pass
self.modify_env_var(CONTENT_TRACING_ENV_VARIABLE, "True")
client = self._create_chat_client(**kwargs)
model = kwargs.pop("azure_ai_chat_model").lower()
processor, exporter = self.setup_memory_trace_exporter()
AIInferenceInstrumentor().instrument()
response = client.complete(
messages=[
sdk.models.SystemMessage(content="You are a helpful assistant."),
sdk.models.UserMessage(content="What is the capital of France?"),
],
stream=True,
)
response_content = ""
for update in response:
if update.choices and update.choices[0].delta.content:
response_content = response_content + update.choices[0].delta.content
client.close()
processor.force_flush()
spans = exporter.get_spans_by_name_starts_with("chat ")
if len(spans) == 0:
spans = exporter.get_spans_by_name("chat")
assert len(spans) == 1
span = spans[0]
expected_attributes = [
("gen_ai.operation.name", "chat"),
("gen_ai.system", "az.ai.inference"),
("gen_ai.request.model", "chat"),
("server.address", ""),
("gen_ai.response.id", ""),
("gen_ai.response.model", model),
("gen_ai.usage.input_tokens", "+"),
("gen_ai.usage.output_tokens", "+"),
("gen_ai.response.finish_reasons", ("stop",)),
]
attributes_match = GenAiTraceVerifier().check_span_attributes(span, expected_attributes)
assert attributes_match == True
expected_events = [
{
"name": "gen_ai.system.message",
"attributes": {
"gen_ai.system": "az.ai.inference",
"gen_ai.event.content": '{"role": "system", "content": "You are a helpful assistant."}',
},
},
{
"name": "gen_ai.user.message",
"attributes": {
"gen_ai.system": "az.ai.inference",
"gen_ai.event.content": '{"role": "user", "content": "What is the capital of France?"}',
},
},
{
"name": "gen_ai.choice",
"attributes": {
"gen_ai.system": "az.ai.inference",
"gen_ai.event.content": '{"message": {"content": "*"}, "finish_reason": "stop", "index": 0}',
},
},
]
events_match = GenAiTraceVerifier().check_span_events(span, expected_events)
assert events_match == True
AIInferenceInstrumentor().uninstrument()
@ServicePreparerChatCompletions()
@recorded_by_proxy
def test_chat_completion_with_function_call_tracing_content_recording_enabled(self, **kwargs):
# Make sure code is not instrumented due to a previous test exception
try:
AIInferenceInstrumentor().uninstrument()
except RuntimeError as e:
pass
import json
from azure.ai.inference.models import (
CompletionsFinishReason,
ToolMessage,
AssistantMessage,
ChatCompletionsToolCall,
ChatCompletionsToolDefinition,
FunctionDefinition,
)
from azure.ai.inference import ChatCompletionsClient
self.modify_env_var(CONTENT_TRACING_ENV_VARIABLE, "True")
client = self._create_chat_client(**kwargs)
model = kwargs.pop("azure_ai_chat_model").lower()
processor, exporter = self.setup_memory_trace_exporter()
AIInferenceInstrumentor().instrument()
def get_weather(city: str) -> str:
if city == "Seattle":
return "Nice weather"
elif city == "New York City":
return "Good weather"
else:
return "Unavailable"
weather_description = ChatCompletionsToolDefinition(
function=FunctionDefinition(
name="get_weather",
description="Returns description of the weather in the specified city",
parameters={
"type": "object",
"properties": {
"city": {
"type": "string",
"description": "The name of the city for which weather info is requested",
},
},
"required": ["city"],
},
)
)
messages = [
sdk.models.SystemMessage(content="You are a helpful assistant."),
sdk.models.UserMessage(content="What is the weather in Seattle?"),
]
response = client.complete(messages=messages, tools=[weather_description])
if response.choices[0].finish_reason == CompletionsFinishReason.TOOL_CALLS:
# Append the previous model response to the chat history
messages.append(AssistantMessage(tool_calls=response.choices[0].message.tool_calls))
# The tool should be of type function call.
if response.choices[0].message.tool_calls is not None and len(response.choices[0].message.tool_calls) > 0:
for tool_call in response.choices[0].message.tool_calls:
if type(tool_call) is ChatCompletionsToolCall:
function_args = json.loads(tool_call.function.arguments.replace("'", '"'))
print(f"Calling function `{tool_call.function.name}` with arguments {function_args}")
callable_func = locals()[tool_call.function.name]
function_response = callable_func(**function_args)
print(f"Function response = {function_response}")
# Provide the tool response to the model, by appending it to the chat history
messages.append(ToolMessage(tool_call_id=tool_call.id, content=function_response))
# With the additional tools information on hand, get another response from the model
response = client.complete(messages=messages, tools=[weather_description])
processor.force_flush()
spans = exporter.get_spans_by_name_starts_with("chat")
assert len(spans) == 2
expected_attributes = [
("gen_ai.operation.name", "chat"),
("gen_ai.system", "az.ai.inference"),
("gen_ai.request.model", "chat"),
("server.address", ""),
("gen_ai.response.id", ""),
("gen_ai.response.model", model),
("gen_ai.usage.input_tokens", "+"),
("gen_ai.usage.output_tokens", "+"),
("gen_ai.response.finish_reasons", ("tool_calls",)),
]
attributes_match = GenAiTraceVerifier().check_span_attributes(spans[0], expected_attributes)
assert attributes_match == True
expected_attributes = [
("gen_ai.operation.name", "chat"),
("gen_ai.system", "az.ai.inference"),
("gen_ai.request.model", "chat"),
("server.address", ""),
("gen_ai.response.id", ""),
("gen_ai.response.model", model),
("gen_ai.usage.input_tokens", "+"),
("gen_ai.usage.output_tokens", "+"),
("gen_ai.response.finish_reasons", ("stop",)),
]
attributes_match = GenAiTraceVerifier().check_span_attributes(spans[1], expected_attributes)
assert attributes_match == True
expected_events = [
{
"name": "gen_ai.system.message",
"timestamp": "*",
"attributes": {
"gen_ai.system": "az.ai.inference",
"gen_ai.event.content": '{"role": "system", "content": "You are a helpful assistant."}',
},
},
{
"name": "gen_ai.user.message",
"timestamp": "*",
"attributes": {
"gen_ai.system": "az.ai.inference",
"gen_ai.event.content": '{"role": "user", "content": "What is the weather in Seattle?"}',
},
},
{
"name": "gen_ai.choice",
"timestamp": "*",
"attributes": {
"gen_ai.system": "az.ai.inference",
"gen_ai.event.content": '{"message": {"content": "", "tool_calls": [{"function": {"arguments": "{\\"city\\":\\"Seattle\\"}", "call_id": null, "name": "get_weather"}, "id": "*", "type": "function"}]}, "finish_reason": "tool_calls", "index": 0}',
},
},
]
events_match = GenAiTraceVerifier().check_span_events(spans[0], expected_events)
assert events_match == True
expected_events = [
{
"name": "gen_ai.system.message",
"timestamp": "*",
"attributes": {
"gen_ai.system": "az.ai.inference",
"gen_ai.event.content": '{"role": "system", "content": "You are a helpful assistant."}',
},
},
{
"name": "gen_ai.user.message",
"timestamp": "*",
"attributes": {
"gen_ai.system": "az.ai.inference",
"gen_ai.event.content": '{"role": "user", "content": "What is the weather in Seattle?"}',
},
},
{
"name": "gen_ai.assistant.message",
"timestamp": "*",
"attributes": {
"gen_ai.system": "az.ai.inference",
"gen_ai.event.content": '{"role": "assistant", "tool_calls": [{"function": {"arguments": "{\\"city\\": \\"Seattle\\"}", "call_id": null, "name": "get_weather"}, "id": "*", "type": "function"}]}',
},
},
{
"name": "gen_ai.tool.message",
"timestamp": "*",
"attributes": {
"gen_ai.system": "az.ai.inference",
"gen_ai.event.content": '{"role": "tool", "tool_call_id": "*", "content": "Nice weather"}',
},
},
{
"name": "gen_ai.choice",
"timestamp": "*",
"attributes": {
"gen_ai.system": "az.ai.inference",
"gen_ai.event.content": '{"message": {"content": "*"}, "finish_reason": "stop", "index": 0}',
},
},
]
events_match = GenAiTraceVerifier().check_span_events(spans[1], expected_events)
assert events_match == True
AIInferenceInstrumentor().uninstrument()
@ServicePreparerChatCompletions()
@recorded_by_proxy
def test_chat_completion_with_function_call_tracing_content_recording_disabled(self, **kwargs):
# Make sure code is not instrumented due to a previous test exception
try:
AIInferenceInstrumentor().uninstrument()
except RuntimeError as e:
pass
import json
from azure.ai.inference.models import (
CompletionsFinishReason,
ToolMessage,
AssistantMessage,
ChatCompletionsToolCall,
ChatCompletionsToolDefinition,
FunctionDefinition,
)
from azure.ai.inference import ChatCompletionsClient
self.modify_env_var(CONTENT_TRACING_ENV_VARIABLE, "False")
client = self._create_chat_client(**kwargs)
model = kwargs.pop("azure_ai_chat_model").lower()
processor, exporter = self.setup_memory_trace_exporter()
AIInferenceInstrumentor().instrument()
def get_weather(city: str) -> str:
if city == "Seattle":
return "Nice weather"
elif city == "New York City":
return "Good weather"
else:
return "Unavailable"
weather_description = ChatCompletionsToolDefinition(
function=FunctionDefinition(
name="get_weather",
description="Returns description of the weather in the specified city",
parameters={
"type": "object",
"properties": {
"city": {
"type": "string",
"description": "The name of the city for which weather info is requested",
},
},
"required": ["city"],
},
)
)
messages = [
sdk.models.SystemMessage(content="You are a helpful assistant."),
sdk.models.UserMessage(content="What is the weather in Seattle?"),
]
response = client.complete(messages=messages, tools=[weather_description])
if response.choices[0].finish_reason == CompletionsFinishReason.TOOL_CALLS:
# Append the previous model response to the chat history
messages.append(AssistantMessage(tool_calls=response.choices[0].message.tool_calls))
# The tool should be of type function call.
if response.choices[0].message.tool_calls is not None and len(response.choices[0].message.tool_calls) > 0:
for tool_call in response.choices[0].message.tool_calls:
if type(tool_call) is ChatCompletionsToolCall:
function_args = json.loads(tool_call.function.arguments.replace("'", '"'))
print(f"Calling function `{tool_call.function.name}` with arguments {function_args}")
callable_func = locals()[tool_call.function.name]
function_response = callable_func(**function_args)
print(f"Function response = {function_response}")
# Provide the tool response to the model, by appending it to the chat history
messages.append(ToolMessage(tool_call_id=tool_call.id, content=function_response))
# With the additional tools information on hand, get another response from the model
response = client.complete(messages=messages, tools=[weather_description])
processor.force_flush()
spans = exporter.get_spans_by_name_starts_with("chat ")
if len(spans) == 0:
spans = exporter.get_spans_by_name("chat")
assert len(spans) == 2
expected_attributes = [
("gen_ai.operation.name", "chat"),
("gen_ai.system", "az.ai.inference"),
("gen_ai.request.model", "chat"),
("server.address", ""),
("gen_ai.response.id", ""),
("gen_ai.response.model", model),
("gen_ai.usage.input_tokens", "+"),
("gen_ai.usage.output_tokens", "+"),
("gen_ai.response.finish_reasons", ("tool_calls",)),
]
attributes_match = GenAiTraceVerifier().check_span_attributes(spans[0], expected_attributes)
assert attributes_match == True
expected_attributes = [
("gen_ai.operation.name", "chat"),
("gen_ai.system", "az.ai.inference"),
("gen_ai.request.model", "chat"),
("server.address", ""),
("gen_ai.response.id", ""),
("gen_ai.response.model", model),
("gen_ai.usage.input_tokens", "+"),
("gen_ai.usage.output_tokens", "+"),
("gen_ai.response.finish_reasons", ("stop",)),
]
attributes_match = GenAiTraceVerifier().check_span_attributes(spans[1], expected_attributes)
assert attributes_match == True
expected_events = [
{
"name": "gen_ai.choice",
"timestamp": "*",
"attributes": {
"gen_ai.system": "az.ai.inference",
"gen_ai.event.content": '{"finish_reason": "tool_calls", "index": 0, "message": {"tool_calls": [{"function": {"call_id": null}, "id": "*", "type": "function"}]}}',
},
}
]
events_match = GenAiTraceVerifier().check_span_events(spans[0], expected_events)
assert events_match == True
expected_events = [
{
"name": "gen_ai.choice",
"timestamp": "*",
"attributes": {
"gen_ai.system": "az.ai.inference",
"gen_ai.event.content": '{"finish_reason": "stop", "index": 0}',
},
}
]
events_match = GenAiTraceVerifier().check_span_events(spans[1], expected_events)
assert events_match == True
AIInferenceInstrumentor().uninstrument()
@ServicePreparerChatCompletions()
@recorded_by_proxy
def test_chat_completion_with_function_call_streaming_tracing_content_recording_enabled(self, **kwargs):
# Make sure code is not instrumented due to a previous test exception
try:
AIInferenceInstrumentor().uninstrument()
except RuntimeError as e:
pass
import json
from azure.ai.inference.models import (
FunctionCall,
ToolMessage,
AssistantMessage,
ChatCompletionsToolCall,
ChatCompletionsToolDefinition,
FunctionDefinition,
)
from azure.ai.inference import ChatCompletionsClient
self.modify_env_var(CONTENT_TRACING_ENV_VARIABLE, "True")
client = self._create_chat_client(**kwargs)
model = kwargs.pop("azure_ai_chat_model").lower()
processor, exporter = self.setup_memory_trace_exporter()
AIInferenceInstrumentor().instrument()
def get_weather(city: str) -> str:
if city == "Seattle":
return "Nice weather"
elif city == "New York City":
return "Good weather"
else:
return "Unavailable"
weather_description = ChatCompletionsToolDefinition(
function=FunctionDefinition(
name="get_weather",
description="Returns description of the weather in the specified city",
parameters={
"type": "object",
"properties": {
"city": {
"type": "string",
"description": "The name of the city for which weather info is requested",
},
},
"required": ["city"],
},
)
)
messages = [
sdk.models.SystemMessage(content="You are a helpful AI assistant."),
sdk.models.UserMessage(content="What is the weather in Seattle?"),
]
response = client.complete(messages=messages, tools=[weather_description], stream=True)
# At this point we expect a function tool call in the model response
tool_call_id: str = ""
function_name: str = ""
function_args: str = ""
for update in response:
if update.choices[0].delta.tool_calls is not None:
if update.choices[0].delta.tool_calls[0].function.name is not None:
function_name = update.choices[0].delta.tool_calls[0].function.name
if update.choices[0].delta.tool_calls[0].id is not None:
tool_call_id = update.choices[0].delta.tool_calls[0].id
function_args += update.choices[0].delta.tool_calls[0].function.arguments or ""
# Append the previous model response to the chat history
messages.append(
AssistantMessage(
tool_calls=[
ChatCompletionsToolCall(
id=tool_call_id, function=FunctionCall(name=function_name, arguments=function_args)
)
]
)
)
# Make the function call
callable_func = locals()[function_name]
function_args_mapping = json.loads(function_args.replace("'", '"'))
function_response = callable_func(**function_args_mapping)
# Append the function response as a tool message to the chat history
messages.append(ToolMessage(tool_call_id=tool_call_id, content=function_response))
# With the additional tools information on hand, get another streaming response from the model
response = client.complete(messages=messages, tools=[weather_description], stream=True)
content = ""
for update in response:
content = content + update.choices[0].delta.content
processor.force_flush()
spans = exporter.get_spans_by_name_starts_with("chat ")
if len(spans) == 0:
spans = exporter.get_spans_by_name("chat")
assert len(spans) == 2
expected_attributes = [
("gen_ai.operation.name", "chat"),
("gen_ai.system", "az.ai.inference"),
("gen_ai.request.model", "chat"),
("server.address", ""),
("gen_ai.response.id", ""),
("gen_ai.response.model", model),
("gen_ai.usage.input_tokens", "+"),
("gen_ai.usage.output_tokens", "+"),
("gen_ai.response.finish_reasons", ("tool_calls",)),
]
attributes_match = GenAiTraceVerifier().check_span_attributes(spans[0], expected_attributes)
assert attributes_match == True
expected_attributes = [
("gen_ai.operation.name", "chat"),
("gen_ai.system", "az.ai.inference"),
("gen_ai.request.model", "chat"),
("server.address", ""),
("gen_ai.response.id", ""),
("gen_ai.response.model", model),
("gen_ai.usage.input_tokens", "+"),
("gen_ai.usage.output_tokens", "+"),
("gen_ai.response.finish_reasons", ("stop",)),
]
attributes_match = GenAiTraceVerifier().check_span_attributes(spans[1], expected_attributes)
assert attributes_match == True
expected_events = [
{
"name": "gen_ai.system.message",
"timestamp": "*",
"attributes": {
"gen_ai.system": "az.ai.inference",
"gen_ai.event.content": '{"role": "system", "content": "You are a helpful AI assistant."}',
},
},
{
"name": "gen_ai.user.message",
"timestamp": "*",
"attributes": {
"gen_ai.system": "az.ai.inference",
"gen_ai.event.content": '{"role": "user", "content": "What is the weather in Seattle?"}',
},
},
{
"name": "gen_ai.choice",
"timestamp": "*",
"attributes": {
"gen_ai.system": "az.ai.inference",
"gen_ai.event.content": '{"finish_reason": "tool_calls", "message": {"tool_calls": [{"id": "*", "type": "function", "function": {"name": "get_weather", "arguments": "{\\"city\\": \\"Seattle\\"}"}}]}, "index": 0}',
},
},
]
events_match = GenAiTraceVerifier().check_span_events(spans[0], expected_events)
assert events_match == True
expected_events = [
{
"name": "gen_ai.system.message",
"timestamp": "*",
"attributes": {
"gen_ai.system": "az.ai.inference",
"gen_ai.event.content": '{"role": "system", "content": "You are a helpful AI assistant."}',
},
},
{
"name": "gen_ai.user.message",
"timestamp": "*",
"attributes": {
"gen_ai.system": "az.ai.inference",
"gen_ai.event.content": '{"role": "user", "content": "What is the weather in Seattle?"}',
},
},
{
"name": "gen_ai.assistant.message",
"timestamp": "*",
"attributes": {
"gen_ai.system": "az.ai.inference",
"gen_ai.event.content": '{"role": "assistant", "tool_calls": [{"id": "*", "function": {"name": "get_weather", "arguments": "{\\"city\\": \\"Seattle\\"}"}, "type": "function"}]}',
},
},
{
"name": "gen_ai.tool.message",
"timestamp": "*",