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https://github.com/langbot-app/LangBot.git
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This commit is contained in:
Typer_Body
@@ -3,11 +3,13 @@ Monitoring helper for recording events during workflow execution.
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This module provides convenient methods to record monitoring data
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without cluttering the main workflow code.
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New logging scheme:
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- Trigger log: adapter → workflow_name → local-workflow (with original message)
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- LLM call log: adapter → workflow_name → local-workflow (with LLM info)
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- LLM response log: adapter → workflow_name → local-workflow (with response message)
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- Reply log: adapter → workflow_name → local-workflow (with reply content)
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Logging scheme (aligned with pipeline monitoring):
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- Trigger log: stores original user message content directly
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- LLM call log: uses record_llm_call only (no additional message record)
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- LLM response log: stores response message content directly
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- Reply log: stores reply content directly
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Fields are extracted from WorkflowQuery object when available, with fallback to context_vars.
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"""
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from __future__ import annotations
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@@ -25,65 +27,102 @@ class WorkflowMonitoringHelper:
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"""Helper class for workflow monitoring operations"""
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@staticmethod
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def _get_adapter_name(query: WorkflowQuery) -> str:
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"""Get adapter name from query"""
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if query.adapter and hasattr(query.adapter, 'name'):
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return query.adapter.name
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if query.adapter and hasattr(query.adapter, 'adapter_name'):
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return query.adapter.adapter_name
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return 'WebChat'
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def _get_session_id(query, context_vars: dict | None = None) -> str:
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"""Build session_id from query or context_vars"""
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# Try to get from query first
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if not isinstance(query, str) and query.launcher_type:
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launcher_type = query.launcher_type.value if hasattr(query.launcher_type, 'value') else str(query.launcher_type)
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launcher_id = query.launcher_id or 'unknown'
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return f'{launcher_type}_{launcher_id}'
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# Fallback to context_vars
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if context_vars and context_vars.get('_launcher_type') and context_vars.get('_launcher_id'):
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return f"{context_vars['_launcher_type']}_{context_vars['_launcher_id']}"
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return 'workflow_session'
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@staticmethod
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def _get_session_id(query: WorkflowQuery) -> str:
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"""Build session_id from launcher info"""
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launcher_type = query.launcher_type.value if query.launcher_type else 'unknown'
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launcher_id = query.launcher_id or 'unknown'
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return f'{launcher_type}_{launcher_id}'
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def _get_platform(query, context_vars: dict | None = None) -> str:
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"""Get platform name from query or context_vars"""
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if not isinstance(query, str) and query.launcher_type:
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if hasattr(query.launcher_type, 'value'):
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return query.launcher_type.value
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return str(query.launcher_type)
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return 'workflow'
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@staticmethod
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def _get_sender_name(query, context_vars: dict | None = None) -> str | None:
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"""Get sender name from query or context_vars"""
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# Try query first
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if not isinstance(query, str):
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if query.sender_name:
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return query.sender_name
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if query.message_event and hasattr(query.message_event, 'sender'):
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sender = query.message_event.sender
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if hasattr(sender, 'nickname'):
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return sender.nickname
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if hasattr(sender, 'member_name'):
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return sender.member_name
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# Fallback to context_vars
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if context_vars:
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return context_vars.get('_sender_name')
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return None
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@staticmethod
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async def record_trigger_log(
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ap: app.Application,
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query: WorkflowQuery,
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query,
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workflow_id: str,
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workflow_name: str,
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bot_name: str = 'Workflow',
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context_vars: dict | None = None,
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) -> str:
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"""Record trigger node log
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"""Record trigger node log (stores original user message content directly)
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Format: adapter → workflow_name → local-workflow
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Contains: original message content
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Aligned with pipeline monitoring: record_query_start
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"""
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try:
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adapter_name = WorkflowMonitoringHelper._get_adapter_name(query)
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session_id = WorkflowMonitoringHelper._get_session_id(query)
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session_id = WorkflowMonitoringHelper._get_session_id(query, context_vars)
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platform = WorkflowMonitoringHelper._get_platform(query, context_vars)
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sender_name = WorkflowMonitoringHelper._get_sender_name(query, context_vars)
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# Get message content
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# Get message content - store original content directly
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message_content = ''
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if query.message_context and hasattr(query.message_context, 'message_content'):
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if isinstance(query, str):
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message_content = query
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elif not isinstance(query, str) and query.message_context:
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message_content = query.message_context.message_content
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elif query.message_chain and hasattr(query.message_chain, 'model_dump'):
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elif not isinstance(query, str) and query.message_chain and hasattr(query.message_chain, 'model_dump'):
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message_content = json.dumps(query.message_chain.model_dump(), ensure_ascii=False)
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elif not isinstance(query, str) and query.user_message:
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message_content = str(query.user_message)
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# Build pipeline_name: workflow_name/local-workflow
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pipeline_name = f'{workflow_name}/local-workflow' if workflow_name else 'local-workflow'
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# Build log message: adapter → workflow_name → local-workflow
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log_message = f'{adapter_name} → {workflow_name} → local-workflow'
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if message_content:
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log_message += f'\n{message_content}'
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# Get bot_id and user_id
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bot_id = ''
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user_id = None
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if not isinstance(query, str):
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bot_id = query.bot_uuid or ''
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user_id = query.sender_id
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elif context_vars:
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bot_id = context_vars.get('_bot_id', '') or ''
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user_id = context_vars.get('_user_id')
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message_id = await ap.monitoring_service.record_message(
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bot_id=query.bot_uuid or '',
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bot_name=workflow_name or 'Workflow',
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bot_id=bot_id,
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bot_name=bot_name,
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pipeline_id=workflow_id,
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pipeline_name=pipeline_name,
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message_content=log_message,
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pipeline_name=workflow_name or 'Workflow',
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message_content=message_content,
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session_id=session_id,
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status='success',
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level='info',
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platform='workflow',
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user_id=query.sender_id,
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user_name=query.sender_name,
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platform=platform,
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user_id=user_id,
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user_name=sender_name,
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role='user',
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runner_name='local-workflow',
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)
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return message_id
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@@ -94,7 +133,7 @@ class WorkflowMonitoringHelper:
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@staticmethod
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async def record_llm_call_log(
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ap: app.Application,
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query: WorkflowQuery,
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query,
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workflow_id: str,
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workflow_name: str,
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node_name: str,
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@@ -104,37 +143,32 @@ class WorkflowMonitoringHelper:
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duration_ms: int,
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status: str = 'success',
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error_message: str | None = None,
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bot_name: str = 'Workflow',
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context_vars: dict | None = None,
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input_message: str | None = None,
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message_id: str | None = None,
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):
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"""Record LLM call log (with LLM info)
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"""Record LLM call log with message_id association
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Format: adapter → workflow_name → local-workflow
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Contains: LLM call statistics
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Aligned with pipeline monitoring: record_llm_call with message_id
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LLM calls are aggregated under the trigger log via message_id.
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"""
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try:
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adapter_name = WorkflowMonitoringHelper._get_adapter_name(query)
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session_id = WorkflowMonitoringHelper._get_session_id(query)
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session_id = WorkflowMonitoringHelper._get_session_id(query, context_vars)
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# Build pipeline_name: workflow_name/local-workflow
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pipeline_name = f'{workflow_name}/local-workflow' if workflow_name else 'local-workflow'
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# Build log message with LLM info
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log_message = f'{adapter_name} → {workflow_name} → local-workflow\n'
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log_message += f'LLM Call: {node_name}\n'
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log_message += f'Model: {model_name}\n'
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log_message += f'Status: {status}\n'
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log_message += f'Duration: {duration_ms}ms\n'
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log_message += f'Input Tokens: {input_tokens}\n'
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log_message += f'Output Tokens: {output_tokens}\n'
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log_message += f'Total Tokens: {input_tokens + output_tokens}'
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if error_message:
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log_message += f'\nError: {error_message}'
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# Get bot_id
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bot_id = ''
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if not isinstance(query, str):
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bot_id = query.bot_uuid or ''
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elif context_vars:
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bot_id = context_vars.get('_bot_id', '') or ''
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# Record LLM call with message_id for association
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await ap.monitoring_service.record_llm_call(
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bot_id=query.bot_uuid or '',
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bot_name=workflow_name or 'Workflow',
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bot_id=bot_id,
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bot_name=bot_name,
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pipeline_id=workflow_id,
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pipeline_name=pipeline_name,
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pipeline_name=workflow_name or 'Workflow',
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session_id=session_id,
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model_name=model_name,
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input_tokens=input_tokens,
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@@ -142,22 +176,7 @@ class WorkflowMonitoringHelper:
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duration=duration_ms,
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status=status,
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error_message=error_message,
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)
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# Also record as message for display
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await ap.monitoring_service.record_message(
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bot_id=query.bot_uuid or '',
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bot_name=workflow_name or 'Workflow',
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pipeline_id=workflow_id,
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pipeline_name=pipeline_name,
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message_content=log_message,
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session_id=session_id,
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status=status,
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level='info',
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platform='workflow',
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user_id=query.sender_id,
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user_name=query.sender_name,
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role='system',
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message_id=message_id,
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)
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except Exception as e:
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ap.logger.error(f'Failed to record LLM call log: {e}')
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@@ -165,42 +184,48 @@ class WorkflowMonitoringHelper:
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@staticmethod
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async def record_llm_response_log(
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ap: app.Application,
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query: WorkflowQuery,
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query,
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workflow_id: str,
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workflow_name: str,
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node_name: str,
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response_content: str,
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bot_name: str = 'Workflow',
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context_vars: dict | None = None,
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):
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"""Record LLM response log (without LLM info, with response message)
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"""Record LLM response log (stores response content directly)
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Format: adapter → workflow_name → local-workflow
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Contains: response message content
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Aligned with pipeline monitoring: record_query_response
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"""
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try:
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adapter_name = WorkflowMonitoringHelper._get_adapter_name(query)
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session_id = WorkflowMonitoringHelper._get_session_id(query)
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session_id = WorkflowMonitoringHelper._get_session_id(query, context_vars)
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platform = WorkflowMonitoringHelper._get_platform(query, context_vars)
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sender_name = WorkflowMonitoringHelper._get_sender_name(query, context_vars)
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# Build pipeline_name: workflow_name/local-workflow
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pipeline_name = f'{workflow_name}/local-workflow' if workflow_name else 'local-workflow'
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# Build log message
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log_message = f'{adapter_name} → {workflow_name} → local-workflow\n'
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log_message += f'Node: {node_name}\n'
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log_message += f'Response: {response_content[:500]}' # Limit length
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# Get bot_id and user_id
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bot_id = ''
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user_id = None
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if not isinstance(query, str):
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bot_id = query.bot_uuid or ''
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user_id = query.sender_id
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elif context_vars:
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bot_id = context_vars.get('_bot_id', '') or ''
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user_id = context_vars.get('_user_id')
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# Store response content directly, no prefix
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await ap.monitoring_service.record_message(
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bot_id=query.bot_uuid or '',
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bot_name=workflow_name or 'Workflow',
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bot_id=bot_id,
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bot_name=bot_name,
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pipeline_id=workflow_id,
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pipeline_name=pipeline_name,
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message_content=log_message,
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pipeline_name=workflow_name or 'Workflow',
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message_content=response_content[:2000], # Limit length
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session_id=session_id,
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status='success',
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level='info',
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platform='workflow',
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user_id=query.sender_id,
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user_name=query.sender_name,
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platform=platform,
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user_id=user_id,
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user_name=sender_name,
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role='assistant',
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runner_name='local-workflow',
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)
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except Exception as e:
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ap.logger.error(f'Failed to record LLM response log: {e}')
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@@ -208,42 +233,48 @@ class WorkflowMonitoringHelper:
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@staticmethod
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async def record_reply_log(
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ap: app.Application,
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query: WorkflowQuery,
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query,
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workflow_id: str,
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workflow_name: str,
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node_name: str,
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reply_content: str,
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bot_name: str = 'Workflow',
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context_vars: dict | None = None,
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):
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"""Record reply message log
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"""Record reply message log (stores reply content directly)
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Format: adapter → workflow_name → local-workflow
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Contains: reply message content
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Aligned with pipeline monitoring: record_query_response
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"""
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try:
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adapter_name = WorkflowMonitoringHelper._get_adapter_name(query)
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session_id = WorkflowMonitoringHelper._get_session_id(query)
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session_id = WorkflowMonitoringHelper._get_session_id(query, context_vars)
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platform = WorkflowMonitoringHelper._get_platform(query, context_vars)
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sender_name = WorkflowMonitoringHelper._get_sender_name(query, context_vars)
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# Build pipeline_name: workflow_name/local-workflow
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pipeline_name = f'{workflow_name}/local-workflow' if workflow_name else 'local-workflow'
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# Build log message
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log_message = f'{adapter_name} → {workflow_name} → local-workflow\n'
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log_message += f'Node: {node_name}\n'
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log_message += f'Reply: {reply_content[:500]}' # Limit length
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# Get bot_id and user_id
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bot_id = ''
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user_id = None
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if not isinstance(query, str):
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bot_id = query.bot_uuid or ''
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user_id = query.sender_id
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elif context_vars:
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bot_id = context_vars.get('_bot_id', '') or ''
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user_id = context_vars.get('_user_id')
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# Store reply content directly, no prefix
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await ap.monitoring_service.record_message(
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bot_id=query.bot_uuid or '',
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bot_name=workflow_name or 'Workflow',
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bot_id=bot_id,
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bot_name=bot_name,
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pipeline_id=workflow_id,
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pipeline_name=pipeline_name,
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message_content=log_message,
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pipeline_name=workflow_name or 'Workflow',
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message_content=reply_content[:2000], # Limit length
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session_id=session_id,
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status='success',
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level='info',
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platform='workflow',
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user_id=query.sender_id,
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user_name=query.sender_name,
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platform=platform,
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user_id=user_id,
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user_name=sender_name,
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role='assistant',
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runner_name='local-workflow',
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)
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except Exception as e:
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ap.logger.error(f'Failed to record reply log: {e}')
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@@ -255,13 +286,14 @@ class LLMCallMonitor:
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def __init__(
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self,
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ap: app.Application,
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query: WorkflowQuery,
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query,
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bot_id: str,
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bot_name: str,
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workflow_id: str,
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workflow_name: str,
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node_name: str,
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model_name: str,
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context_vars: dict | None = None,
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):
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self.ap = ap
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self.query = query
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@@ -271,6 +303,7 @@ class LLMCallMonitor:
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self.workflow_name = workflow_name
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self.node_name = node_name
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self.model_name = model_name
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self.context_vars = context_vars
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self.start_time = None
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self.input_tokens = 0
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self.output_tokens = 0
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@@ -295,6 +328,8 @@ class LLMCallMonitor:
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duration_ms=duration_ms,
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status='error',
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error_message=str(exc_val) if exc_val else None,
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bot_name=self.bot_name,
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context_vars=self.context_vars,
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)
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else:
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await WorkflowMonitoringHelper.record_llm_call_log(
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@@ -308,6 +343,8 @@ class LLMCallMonitor:
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output_tokens=self.output_tokens,
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duration_ms=duration_ms,
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status='success',
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bot_name=self.bot_name,
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context_vars=self.context_vars,
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)
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return False
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