mirror of https://github.com/langgenius/dify.git
fix(telemetry): add prompt generation trace emission for no_variable=false path
- The no_variable=false code path in generate_rule_config was missing trace emission - Added timing wrapper and _emit_prompt_generation_trace call to ensure metrics/logs are captured - Trace now emitted on both success and failure cases for consistency with no_variable=true path
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GareArc
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052f50805f
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@ -250,65 +250,98 @@ class LLMGenerator:
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model=model_config.get("name", ""),
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)
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try:
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llm_result = None
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with measure_time() as timer:
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try:
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# the first step to generate the task prompt
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prompt_content: LLMResult = model_instance.invoke_llm(
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prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
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try:
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# the first step to generate the task prompt
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prompt_content: LLMResult = model_instance.invoke_llm(
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prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
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)
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llm_result = prompt_content
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except InvokeError as e:
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error = str(e)
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error_step = "generate prefix prompt"
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rule_config["error"] = f"Failed to {error_step}. Error: {error}" if error else ""
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if user_id:
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cls._emit_prompt_generation_trace(
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tenant_id=tenant_id,
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user_id=user_id,
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app_id=app_id,
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operation_type="rule_generate",
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instruction=instruction,
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generated_output="",
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llm_result=llm_result,
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timer=timer,
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error=error,
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)
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return rule_config
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rule_config["prompt"] = cast(str, prompt_content.message.content)
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if not isinstance(prompt_content.message.content, str):
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raise NotImplementedError("prompt content is not a string")
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parameter_generate_prompt = parameter_template.format(
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inputs={
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"INPUT_TEXT": prompt_content.message.content,
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},
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remove_template_variables=False,
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)
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except InvokeError as e:
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error = str(e)
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error_step = "generate prefix prompt"
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rule_config["error"] = f"Failed to {error_step}. Error: {error}" if error else ""
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parameter_messages = [UserPromptMessage(content=parameter_generate_prompt)]
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return rule_config
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rule_config["prompt"] = cast(str, prompt_content.message.content)
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if not isinstance(prompt_content.message.content, str):
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raise NotImplementedError("prompt content is not a string")
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parameter_generate_prompt = parameter_template.format(
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inputs={
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"INPUT_TEXT": prompt_content.message.content,
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},
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remove_template_variables=False,
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)
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parameter_messages = [UserPromptMessage(content=parameter_generate_prompt)]
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# the second step to generate the task_parameter and task_statement
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statement_generate_prompt = statement_template.format(
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inputs={
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"TASK_DESCRIPTION": instruction,
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"INPUT_TEXT": prompt_content.message.content,
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},
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remove_template_variables=False,
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)
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statement_messages = [UserPromptMessage(content=statement_generate_prompt)]
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try:
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parameter_content: LLMResult = model_instance.invoke_llm(
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prompt_messages=list(parameter_messages), model_parameters=model_parameters, stream=False
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# the second step to generate the task_parameter and task_statement
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statement_generate_prompt = statement_template.format(
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inputs={
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"TASK_DESCRIPTION": instruction,
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"INPUT_TEXT": prompt_content.message.content,
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},
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remove_template_variables=False,
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)
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rule_config["variables"] = re.findall(r'"\s*([^"]+)\s*"', cast(str, parameter_content.message.content))
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except InvokeError as e:
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error = str(e)
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error_step = "generate variables"
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statement_messages = [UserPromptMessage(content=statement_generate_prompt)]
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try:
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statement_content: LLMResult = model_instance.invoke_llm(
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prompt_messages=list(statement_messages), model_parameters=model_parameters, stream=False
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)
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rule_config["opening_statement"] = cast(str, statement_content.message.content)
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except InvokeError as e:
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error = str(e)
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error_step = "generate conversation opener"
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try:
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parameter_content: LLMResult = model_instance.invoke_llm(
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prompt_messages=list(parameter_messages), model_parameters=model_parameters, stream=False
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)
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rule_config["variables"] = re.findall(
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r'"\s*([^"]+)\s*"', cast(str, parameter_content.message.content)
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)
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except InvokeError as e:
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error = str(e)
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error_step = "generate variables"
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except Exception as e:
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logger.exception("Failed to generate rule config, model: %s", model_config.get("name"))
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rule_config["error"] = str(e)
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try:
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statement_content: LLMResult = model_instance.invoke_llm(
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prompt_messages=list(statement_messages), model_parameters=model_parameters, stream=False
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)
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rule_config["opening_statement"] = cast(str, statement_content.message.content)
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except InvokeError as e:
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error = str(e)
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error_step = "generate conversation opener"
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except Exception as e:
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logger.exception("Failed to generate rule config, model: %s", model_config.get("name"))
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rule_config["error"] = str(e)
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error = str(e)
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rule_config["error"] = f"Failed to {error_step}. Error: {error}" if error else ""
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if user_id:
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generated_output = rule_config.get("prompt", "")
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cls._emit_prompt_generation_trace(
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tenant_id=tenant_id,
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user_id=user_id,
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app_id=app_id,
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operation_type="rule_generate",
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instruction=instruction,
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generated_output=str(generated_output) if generated_output else "",
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llm_result=llm_result,
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timer=timer,
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error=error or None,
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)
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return rule_config
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@classmethod
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