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fix: azure openai stream response usage missing (#1998)
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c9e4147b11
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@ -257,6 +257,9 @@ class AppRunner:
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if not usage and result.delta.usage:
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usage = result.delta.usage
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if not usage:
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usage = LLMUsage.empty_usage()
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llm_result = LLMResult(
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model=model,
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prompt_messages=prompt_messages,
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@ -322,8 +322,11 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
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response: Stream[ChatCompletionChunk],
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prompt_messages: list[PromptMessage],
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tools: Optional[list[PromptMessageTool]] = None) -> Generator:
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index = 0
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full_assistant_content = ''
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real_model = model
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system_fingerprint = None
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completion = ''
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for chunk in response:
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if len(chunk.choices) == 0:
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continue
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@ -349,40 +352,44 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
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full_assistant_content += delta.delta.content if delta.delta.content else ''
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if delta.finish_reason is not None:
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# calculate num tokens
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prompt_tokens = self._num_tokens_from_messages(credentials, prompt_messages, tools)
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real_model = chunk.model
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system_fingerprint = chunk.system_fingerprint
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completion += delta.delta.content if delta.delta.content else ''
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full_assistant_prompt_message = AssistantPromptMessage(
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content=full_assistant_content,
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tool_calls=tool_calls
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yield LLMResultChunk(
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model=real_model,
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prompt_messages=prompt_messages,
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system_fingerprint=system_fingerprint,
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delta=LLMResultChunkDelta(
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index=index,
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message=assistant_prompt_message,
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)
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completion_tokens = self._num_tokens_from_messages(credentials, [full_assistant_prompt_message])
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)
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# transform usage
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usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)
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index += 0
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yield LLMResultChunk(
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model=chunk.model,
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prompt_messages=prompt_messages,
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system_fingerprint=chunk.system_fingerprint,
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delta=LLMResultChunkDelta(
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index=delta.index,
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message=assistant_prompt_message,
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finish_reason=delta.finish_reason,
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usage=usage
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)
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)
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else:
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yield LLMResultChunk(
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model=chunk.model,
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prompt_messages=prompt_messages,
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system_fingerprint=chunk.system_fingerprint,
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delta=LLMResultChunkDelta(
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index=delta.index,
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message=assistant_prompt_message,
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)
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)
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# calculate num tokens
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prompt_tokens = self._num_tokens_from_messages(credentials, prompt_messages, tools)
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full_assistant_prompt_message = AssistantPromptMessage(
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content=completion
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)
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completion_tokens = self._num_tokens_from_messages(credentials, [full_assistant_prompt_message])
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# transform usage
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usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)
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yield LLMResultChunk(
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model=real_model,
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prompt_messages=prompt_messages,
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system_fingerprint=system_fingerprint,
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delta=LLMResultChunkDelta(
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index=index,
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message=AssistantPromptMessage(content=''),
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finish_reason='stop',
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usage=usage
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)
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)
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@staticmethod
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def _extract_response_tool_calls(response_tool_calls: list[ChatCompletionMessageToolCall | ChoiceDeltaToolCall]) \
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@ -190,7 +190,6 @@ def test_invoke_stream_chat_model(setup_openai_mock):
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assert isinstance(chunk, LLMResultChunk)
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assert isinstance(chunk.delta, LLMResultChunkDelta)
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assert isinstance(chunk.delta.message, AssistantPromptMessage)
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assert len(chunk.delta.message.content) > 0 if chunk.delta.finish_reason is None else True
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if chunk.delta.finish_reason is not None:
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assert chunk.delta.usage is not None
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assert chunk.delta.usage.completion_tokens > 0
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