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https://github.com/langgenius/dify.git
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feat(telemetry): add enterprise OTEL telemetry with gateway, traces, metrics, and logs
This commit is contained in:
GareArc
@ -6,8 +6,6 @@ from typing import Protocol, cast
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import json_repair
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from core.app.app_config.entities import ModelConfig
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from core.llm_generator.entities import RuleCodeGeneratePayload, RuleGeneratePayload, RuleStructuredOutputPayload
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from core.llm_generator.output_parser.rule_config_generator import RuleConfigGeneratorOutputParser
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from core.llm_generator.output_parser.suggested_questions_after_answer import SuggestedQuestionsAfterAnswerOutputParser
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from core.llm_generator.prompts import (
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@ -27,10 +25,10 @@ from core.model_runtime.entities.llm_entities import LLMResult
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from core.model_runtime.entities.message_entities import PromptMessage, SystemPromptMessage, UserPromptMessage
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from core.model_runtime.entities.model_entities import ModelType
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from core.model_runtime.errors.invoke import InvokeAuthorizationError, InvokeError
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from core.ops.entities.trace_entity import TraceTaskName
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from core.ops.ops_trace_manager import TraceQueueManager, TraceTask
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from core.ops.utils import measure_time
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from core.prompt.utils.prompt_template_parser import PromptTemplateParser
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from core.telemetry import TelemetryContext, TelemetryEvent, TraceTaskName
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from core.telemetry import emit as telemetry_emit
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from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionMetadataKey
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from extensions.ext_database import db
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from extensions.ext_storage import storage
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@ -73,8 +71,8 @@ class LLMGenerator:
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response: LLMResult = model_instance.invoke_llm(
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prompt_messages=list(prompts), model_parameters={"max_tokens": 500, "temperature": 1}, stream=False
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)
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answer = response.message.get_text_content()
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if answer == "":
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answer = cast(str, response.message.content)
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if answer is None:
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return ""
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try:
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result_dict = json.loads(answer)
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@ -96,15 +94,17 @@ class LLMGenerator:
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name = name[:75] + "..."
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# get tracing instance
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trace_manager = TraceQueueManager(app_id=app_id)
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trace_manager.add_trace_task(
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TraceTask(
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TraceTaskName.GENERATE_NAME_TRACE,
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conversation_id=conversation_id,
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generate_conversation_name=name,
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inputs=prompt,
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timer=timer,
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tenant_id=tenant_id,
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telemetry_emit(
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TelemetryEvent(
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name=TraceTaskName.GENERATE_NAME_TRACE,
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context=TelemetryContext(tenant_id=tenant_id, app_id=app_id),
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payload={
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"conversation_id": conversation_id,
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"generate_conversation_name": name,
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"inputs": prompt,
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"timer": timer,
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"tenant_id": tenant_id,
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},
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)
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)
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@ -153,19 +153,27 @@ class LLMGenerator:
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return questions
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@classmethod
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def generate_rule_config(cls, tenant_id: str, args: RuleGeneratePayload):
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def generate_rule_config(
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cls,
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tenant_id: str,
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instruction: str,
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model_config: dict,
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no_variable: bool,
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user_id: str | None = None,
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app_id: str | None = None,
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):
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output_parser = RuleConfigGeneratorOutputParser()
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error = ""
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error_step = ""
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rule_config = {"prompt": "", "variables": [], "opening_statement": "", "error": ""}
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model_parameters = args.model_config_data.completion_params
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if args.no_variable:
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model_parameters = model_config.get("completion_params", {})
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if no_variable:
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prompt_template = PromptTemplateParser(WORKFLOW_RULE_CONFIG_PROMPT_GENERATE_TEMPLATE)
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prompt_generate = prompt_template.format(
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inputs={
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"TASK_DESCRIPTION": args.instruction,
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"TASK_DESCRIPTION": instruction,
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},
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remove_template_variables=False,
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)
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@ -177,26 +185,45 @@ class LLMGenerator:
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model_instance = model_manager.get_model_instance(
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tenant_id=tenant_id,
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model_type=ModelType.LLM,
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provider=args.model_config_data.provider,
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model=args.model_config_data.name,
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provider=model_config.get("provider", ""),
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model=model_config.get("name", ""),
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)
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try:
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response: 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 = None
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with measure_time() as timer:
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try:
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llm_result = 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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rule_config["prompt"] = response.message.get_text_content()
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rule_config["prompt"] = cast(str, llm_result.message.content)
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except InvokeError as e:
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error = str(e)
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error_step = "generate rule config"
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except Exception as e:
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logger.exception("Failed to generate rule config, model: %s", args.model_config_data.name)
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rule_config["error"] = str(e)
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except InvokeError as e:
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error = str(e)
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error_step = "generate rule config"
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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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prompt_value = rule_config.get("prompt", "")
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generated_output = str(prompt_value) if prompt_value else ""
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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=generated_output,
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llm_result=llm_result,
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model_config=model_config,
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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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# get rule config prompt, parameter and statement
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@ -211,7 +238,7 @@ class LLMGenerator:
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# format the prompt_generate_prompt
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prompt_generate_prompt = prompt_template.format(
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inputs={
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"TASK_DESCRIPTION": args.instruction,
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"TASK_DESCRIPTION": instruction,
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},
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remove_template_variables=False,
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)
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@ -222,84 +249,125 @@ class LLMGenerator:
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model_instance = model_manager.get_model_instance(
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tenant_id=tenant_id,
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model_type=ModelType.LLM,
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provider=args.model_config_data.provider,
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model=args.model_config_data.name,
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provider=model_config.get("provider", ""),
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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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model_config=model_config,
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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"] = prompt_content.message.get_text_content()
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parameter_generate_prompt = parameter_template.format(
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inputs={
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"INPUT_TEXT": prompt_content.message.get_text_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": args.instruction,
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"INPUT_TEXT": prompt_content.message.get_text_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*"', parameter_content.message.get_text_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"] = statement_content.message.get_text_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", args.model_config_data.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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model_config=model_config,
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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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def generate_code(
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cls,
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tenant_id: str,
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args: RuleCodeGeneratePayload,
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instruction: str,
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model_config: dict,
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code_language: str = "javascript",
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user_id: str | None = None,
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app_id: str | None = None,
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):
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if args.code_language == "python":
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if code_language == "python":
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prompt_template = PromptTemplateParser(PYTHON_CODE_GENERATOR_PROMPT_TEMPLATE)
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else:
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prompt_template = PromptTemplateParser(JAVASCRIPT_CODE_GENERATOR_PROMPT_TEMPLATE)
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prompt = prompt_template.format(
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inputs={
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"INSTRUCTION": args.instruction,
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"CODE_LANGUAGE": args.code_language,
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"INSTRUCTION": instruction,
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"CODE_LANGUAGE": code_language,
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},
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remove_template_variables=False,
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)
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@ -308,28 +376,49 @@ class LLMGenerator:
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model_instance = model_manager.get_model_instance(
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tenant_id=tenant_id,
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model_type=ModelType.LLM,
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provider=args.model_config_data.provider,
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model=args.model_config_data.name,
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provider=model_config.get("provider", ""),
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model=model_config.get("name", ""),
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)
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prompt_messages = [UserPromptMessage(content=prompt)]
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model_parameters = args.model_config_data.completion_params
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try:
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response: LLMResult = model_instance.invoke_llm(
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prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
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model_parameters = model_config.get("completion_params", {})
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llm_result = None
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error = None
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with measure_time() as timer:
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try:
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llm_result = 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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generated_code = cast(str, llm_result.message.content)
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result = {"code": generated_code, "language": code_language, "error": ""}
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except InvokeError as e:
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error = str(e)
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result = {"code": "", "language": code_language, "error": f"Failed to generate code. Error: {error}"}
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except Exception as e:
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logger.exception(
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"Failed to invoke LLM model, model: %s, language: %s", model_config.get("name"), code_language
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)
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error = str(e)
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result = {"code": "", "language": code_language, "error": f"An unexpected error occurred: {str(e)}"}
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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="code_generate",
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instruction=instruction,
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generated_output=result.get("code", ""),
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llm_result=llm_result,
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model_config=model_config,
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timer=timer,
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error=error,
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)
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generated_code = response.message.get_text_content()
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return {"code": generated_code, "language": args.code_language, "error": ""}
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except InvokeError as e:
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error = str(e)
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return {"code": "", "language": args.code_language, "error": f"Failed to generate code. Error: {error}"}
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except Exception as e:
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logger.exception(
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"Failed to invoke LLM model, model: %s, language: %s", args.model_config_data.name, args.code_language
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)
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return {"code": "", "language": args.code_language, "error": f"An unexpected error occurred: {str(e)}"}
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return result
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@classmethod
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def generate_qa_document(cls, tenant_id: str, query, document_language: str):
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@ -355,49 +444,76 @@ class LLMGenerator:
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raise TypeError("Expected LLMResult when stream=False")
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response = result
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answer = response.message.get_text_content()
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answer = cast(str, response.message.content)
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return answer.strip()
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@classmethod
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def generate_structured_output(cls, tenant_id: str, args: RuleStructuredOutputPayload):
|
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def generate_structured_output(
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cls, tenant_id: str, instruction: str, model_config: dict, user_id: str | None = None, app_id: str | None = None
|
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):
|
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model_manager = ModelManager()
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model_instance = model_manager.get_model_instance(
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tenant_id=tenant_id,
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model_type=ModelType.LLM,
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provider=args.model_config_data.provider,
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model=args.model_config_data.name,
|
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provider=model_config.get("provider", ""),
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model=model_config.get("name", ""),
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)
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prompt_messages = [
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SystemPromptMessage(content=SYSTEM_STRUCTURED_OUTPUT_GENERATE),
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UserPromptMessage(content=args.instruction),
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UserPromptMessage(content=instruction),
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]
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model_parameters = args.model_config_data.completion_params
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model_parameters = model_config.get("model_parameters", {})
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|
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try:
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response: LLMResult = model_instance.invoke_llm(
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prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
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llm_result = None
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error = None
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result = {"output": "", "error": ""}
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|
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with measure_time() as timer:
|
||||
try:
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llm_result = 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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|
||||
raw_content = llm_result.message.content
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|
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if not isinstance(raw_content, str):
|
||||
raise ValueError(f"LLM response content must be a string, got: {type(raw_content)}")
|
||||
|
||||
try:
|
||||
parsed_content = json.loads(raw_content)
|
||||
except json.JSONDecodeError:
|
||||
parsed_content = json_repair.loads(raw_content)
|
||||
|
||||
if not isinstance(parsed_content, dict | list):
|
||||
raise ValueError(f"Failed to parse structured output from llm: {raw_content}")
|
||||
|
||||
generated_json_schema = json.dumps(parsed_content, indent=2, ensure_ascii=False)
|
||||
result = {"output": generated_json_schema, "error": ""}
|
||||
|
||||
except InvokeError as e:
|
||||
error = str(e)
|
||||
result = {"output": "", "error": f"Failed to generate JSON Schema. Error: {error}"}
|
||||
except Exception as e:
|
||||
logger.exception("Failed to invoke LLM model, model: %s", model_config.get("name"))
|
||||
error = str(e)
|
||||
result = {"output": "", "error": f"An unexpected error occurred: {str(e)}"}
|
||||
|
||||
if user_id:
|
||||
cls._emit_prompt_generation_trace(
|
||||
tenant_id=tenant_id,
|
||||
user_id=user_id,
|
||||
app_id=app_id,
|
||||
operation_type="structured_output",
|
||||
instruction=instruction,
|
||||
generated_output=result.get("output", ""),
|
||||
llm_result=llm_result,
|
||||
model_config=model_config,
|
||||
timer=timer,
|
||||
error=error,
|
||||
)
|
||||
|
||||
raw_content = response.message.get_text_content()
|
||||
|
||||
try:
|
||||
parsed_content = json.loads(raw_content)
|
||||
except json.JSONDecodeError:
|
||||
parsed_content = json_repair.loads(raw_content)
|
||||
|
||||
if not isinstance(parsed_content, dict | list):
|
||||
raise ValueError(f"Failed to parse structured output from llm: {raw_content}")
|
||||
|
||||
generated_json_schema = json.dumps(parsed_content, indent=2, ensure_ascii=False)
|
||||
return {"output": generated_json_schema, "error": ""}
|
||||
|
||||
except InvokeError as e:
|
||||
error = str(e)
|
||||
return {"output": "", "error": f"Failed to generate JSON Schema. Error: {error}"}
|
||||
except Exception as e:
|
||||
logger.exception("Failed to invoke LLM model, model: %s", args.model_config_data.name)
|
||||
return {"output": "", "error": f"An unexpected error occurred: {str(e)}"}
|
||||
return result
|
||||
|
||||
@staticmethod
|
||||
def instruction_modify_legacy(
|
||||
@ -405,14 +521,16 @@ class LLMGenerator:
|
||||
flow_id: str,
|
||||
current: str,
|
||||
instruction: str,
|
||||
model_config: ModelConfig,
|
||||
model_config: dict,
|
||||
ideal_output: str | None,
|
||||
user_id: str | None = None,
|
||||
app_id: str | None = None,
|
||||
):
|
||||
last_run: Message | None = (
|
||||
db.session.query(Message).where(Message.app_id == flow_id).order_by(Message.created_at.desc()).first()
|
||||
)
|
||||
if not last_run:
|
||||
return LLMGenerator.__instruction_modify_common(
|
||||
result = LLMGenerator.__instruction_modify_common(
|
||||
tenant_id=tenant_id,
|
||||
model_config=model_config,
|
||||
last_run=None,
|
||||
@ -421,22 +539,28 @@ class LLMGenerator:
|
||||
instruction=instruction,
|
||||
node_type="llm",
|
||||
ideal_output=ideal_output,
|
||||
user_id=user_id,
|
||||
app_id=app_id,
|
||||
)
|
||||
last_run_dict = {
|
||||
"query": last_run.query,
|
||||
"answer": last_run.answer,
|
||||
"error": last_run.error,
|
||||
}
|
||||
return LLMGenerator.__instruction_modify_common(
|
||||
tenant_id=tenant_id,
|
||||
model_config=model_config,
|
||||
last_run=last_run_dict,
|
||||
current=current,
|
||||
error_message=str(last_run.error),
|
||||
instruction=instruction,
|
||||
node_type="llm",
|
||||
ideal_output=ideal_output,
|
||||
)
|
||||
else:
|
||||
last_run_dict = {
|
||||
"query": last_run.query,
|
||||
"answer": last_run.answer,
|
||||
"error": last_run.error,
|
||||
}
|
||||
result = LLMGenerator.__instruction_modify_common(
|
||||
tenant_id=tenant_id,
|
||||
model_config=model_config,
|
||||
last_run=last_run_dict,
|
||||
current=current,
|
||||
error_message=str(last_run.error),
|
||||
instruction=instruction,
|
||||
node_type="llm",
|
||||
ideal_output=ideal_output,
|
||||
user_id=user_id,
|
||||
app_id=app_id,
|
||||
)
|
||||
return result
|
||||
|
||||
@staticmethod
|
||||
def instruction_modify_workflow(
|
||||
@ -445,9 +569,11 @@ class LLMGenerator:
|
||||
node_id: str,
|
||||
current: str,
|
||||
instruction: str,
|
||||
model_config: ModelConfig,
|
||||
model_config: dict,
|
||||
ideal_output: str | None,
|
||||
workflow_service: WorkflowServiceInterface,
|
||||
user_id: str | None = None,
|
||||
app_id: str | None = None,
|
||||
):
|
||||
session = db.session()
|
||||
|
||||
@ -478,6 +604,8 @@ class LLMGenerator:
|
||||
instruction=instruction,
|
||||
node_type=node_type,
|
||||
ideal_output=ideal_output,
|
||||
user_id=user_id,
|
||||
app_id=app_id,
|
||||
)
|
||||
|
||||
def agent_log_of(node_execution: WorkflowNodeExecutionModel) -> Sequence:
|
||||
@ -511,18 +639,22 @@ class LLMGenerator:
|
||||
instruction=instruction,
|
||||
node_type=last_run.node_type,
|
||||
ideal_output=ideal_output,
|
||||
user_id=user_id,
|
||||
app_id=app_id,
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def __instruction_modify_common(
|
||||
tenant_id: str,
|
||||
model_config: ModelConfig,
|
||||
model_config: dict,
|
||||
last_run: dict | None,
|
||||
current: str | None,
|
||||
error_message: str | None,
|
||||
instruction: str,
|
||||
node_type: str,
|
||||
ideal_output: str | None,
|
||||
user_id: str | None = None,
|
||||
app_id: str | None = None,
|
||||
):
|
||||
LAST_RUN = "{{#last_run#}}"
|
||||
CURRENT = "{{#current#}}"
|
||||
@ -537,8 +669,8 @@ class LLMGenerator:
|
||||
model_instance = ModelManager().get_model_instance(
|
||||
tenant_id=tenant_id,
|
||||
model_type=ModelType.LLM,
|
||||
provider=model_config.provider,
|
||||
model=model_config.name,
|
||||
provider=model_config.get("provider", ""),
|
||||
model=model_config.get("name", ""),
|
||||
)
|
||||
match node_type:
|
||||
case "llm" | "agent":
|
||||
@ -562,24 +694,122 @@ class LLMGenerator:
|
||||
]
|
||||
model_parameters = {"temperature": 0.4}
|
||||
|
||||
try:
|
||||
response: LLMResult = model_instance.invoke_llm(
|
||||
prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
|
||||
llm_result = None
|
||||
error = None
|
||||
result = {}
|
||||
|
||||
with measure_time() as timer:
|
||||
try:
|
||||
llm_result = model_instance.invoke_llm(
|
||||
prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
|
||||
)
|
||||
|
||||
generated_raw = llm_result.message.get_text_content()
|
||||
first_brace = generated_raw.find("{")
|
||||
last_brace = generated_raw.rfind("}")
|
||||
if first_brace == -1 or last_brace == -1 or last_brace < first_brace:
|
||||
raise ValueError(f"Could not find a valid JSON object in response: {generated_raw}")
|
||||
json_str = generated_raw[first_brace : last_brace + 1]
|
||||
data = json_repair.loads(json_str)
|
||||
if not isinstance(data, dict):
|
||||
raise TypeError(f"Expected a JSON object, but got {type(data).__name__}")
|
||||
result = data
|
||||
except InvokeError as e:
|
||||
error = str(e)
|
||||
result = {"error": f"Failed to generate code. Error: {error}"}
|
||||
except Exception as e:
|
||||
logger.exception(
|
||||
"Failed to invoke LLM model, model: %s", json.dumps(model_config.get("name")), exc_info=True
|
||||
)
|
||||
error = str(e)
|
||||
result = {"error": f"An unexpected error occurred: {str(e)}"}
|
||||
|
||||
if user_id:
|
||||
generated_output = ""
|
||||
if isinstance(result, dict):
|
||||
for key in ["prompt", "code", "output", "modified"]:
|
||||
if result.get(key):
|
||||
generated_output = str(result[key])
|
||||
break
|
||||
|
||||
LLMGenerator._emit_prompt_generation_trace(
|
||||
tenant_id=tenant_id,
|
||||
user_id=user_id,
|
||||
app_id=app_id,
|
||||
operation_type="instruction_modify",
|
||||
instruction=instruction,
|
||||
generated_output=generated_output,
|
||||
llm_result=llm_result,
|
||||
model_config=model_config,
|
||||
timer=timer,
|
||||
error=error,
|
||||
)
|
||||
|
||||
generated_raw = response.message.get_text_content()
|
||||
first_brace = generated_raw.find("{")
|
||||
last_brace = generated_raw.rfind("}")
|
||||
if first_brace == -1 or last_brace == -1 or last_brace < first_brace:
|
||||
raise ValueError(f"Could not find a valid JSON object in response: {generated_raw}")
|
||||
json_str = generated_raw[first_brace : last_brace + 1]
|
||||
data = json_repair.loads(json_str)
|
||||
if not isinstance(data, dict):
|
||||
raise TypeError(f"Expected a JSON object, but got {type(data).__name__}")
|
||||
return data
|
||||
except InvokeError as e:
|
||||
error = str(e)
|
||||
return {"error": f"Failed to generate code. Error: {error}"}
|
||||
except Exception as e:
|
||||
logger.exception("Failed to invoke LLM model, model: %s", json.dumps(model_config.name), exc_info=True)
|
||||
return {"error": f"An unexpected error occurred: {str(e)}"}
|
||||
return result
|
||||
|
||||
@classmethod
|
||||
def _emit_prompt_generation_trace(
|
||||
cls,
|
||||
tenant_id: str,
|
||||
user_id: str,
|
||||
app_id: str | None,
|
||||
operation_type: str,
|
||||
instruction: str,
|
||||
generated_output: str,
|
||||
llm_result: LLMResult | None,
|
||||
model_config: dict | None = None,
|
||||
timer=None,
|
||||
error: str | None = None,
|
||||
):
|
||||
if llm_result:
|
||||
prompt_tokens = llm_result.usage.prompt_tokens
|
||||
completion_tokens = llm_result.usage.completion_tokens
|
||||
total_tokens = llm_result.usage.total_tokens
|
||||
model_name = llm_result.model
|
||||
# Extract provider from model_config if available, otherwise fall back to parsing model name
|
||||
if model_config and model_config.get("provider"):
|
||||
model_provider = model_config.get("provider", "")
|
||||
else:
|
||||
model_provider = model_name.split("/")[0] if "/" in model_name else ""
|
||||
latency = llm_result.usage.latency
|
||||
total_price = float(llm_result.usage.total_price) if llm_result.usage.total_price else None
|
||||
currency = llm_result.usage.currency
|
||||
else:
|
||||
prompt_tokens = 0
|
||||
completion_tokens = 0
|
||||
total_tokens = 0
|
||||
model_provider = model_config.get("provider", "") if model_config else ""
|
||||
model_name = model_config.get("name", "") if model_config else ""
|
||||
latency = 0.0
|
||||
if timer:
|
||||
start_time = timer.get("start")
|
||||
end_time = timer.get("end")
|
||||
if start_time and end_time:
|
||||
latency = (end_time - start_time).total_seconds()
|
||||
total_price = None
|
||||
currency = None
|
||||
|
||||
telemetry_emit(
|
||||
TelemetryEvent(
|
||||
name=TraceTaskName.PROMPT_GENERATION_TRACE,
|
||||
context=TelemetryContext(tenant_id=tenant_id, user_id=user_id, app_id=app_id),
|
||||
payload={
|
||||
"tenant_id": tenant_id,
|
||||
"user_id": user_id,
|
||||
"app_id": app_id,
|
||||
"operation_type": operation_type,
|
||||
"instruction": instruction,
|
||||
"generated_output": generated_output,
|
||||
"prompt_tokens": prompt_tokens,
|
||||
"completion_tokens": completion_tokens,
|
||||
"total_tokens": total_tokens,
|
||||
"model_provider": model_provider,
|
||||
"model_name": model_name,
|
||||
"latency": latency,
|
||||
"total_price": total_price,
|
||||
"currency": currency,
|
||||
"timer": timer,
|
||||
"error": error,
|
||||
},
|
||||
)
|
||||
)
|
||||
|
||||
Reference in New Issue
Block a user