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Feat/monitor (#1928)
* feat: add monitor * feat: fix tab * feat: work * feat: not reliable monitor * feat: enhance monitoring page layout with integrated filters and refresh button * feat: add support for runner recording * feat: add jump button & alignment * feat: new * fix: not show query variables in local agent * fix: pnpm lint and python ruff check * fix: ruff fromat * chore: remove unnecessary migration * style: optimize monitoring page layout and fix sticky filter issues - Enhanced metric cards with gradient backgrounds and hover effects - Increased traffic chart height from 200px to 300px - Adjusted grid layout and spacing for better visual appeal - Fixed sticky filter area to properly cover parent padding without transparent gaps - Used negative margins and positioning to eliminate scrolling artifacts - Matched padding/margins with other pages (pipelines, bots) for consistency - Removed duplicate title/subtitle from page content - Added cursor-pointer styling to tab triggers - Removed border between tab list and tab content Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com> * fix: apply prettier formatting to monitoring components - Fixed indentation and spacing in MetricCard.tsx - Fixed formatting in TrafficChart.tsx - Applied prettier formatting to page.tsx Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com> * feat: update HomeSidebar to trigger action on child selection and localize monitoring titles * refactor: streamline LLM and embedding invocation methods * feat: add embedding model monitor * fix: database version * chore: simplify pnpm-lock.yaml formatting --------- Co-authored-by: Junyan Qin <rockchinq@gmail.com> Co-authored-by: Claude Sonnet 4.5 <noreply@anthropic.com>
This commit is contained in:
Guanchao Wang
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"""
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Monitoring helper for recording events during pipeline execution.
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This module provides convenient methods to record monitoring data
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without cluttering the main pipeline code.
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"""
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from __future__ import annotations
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import traceback
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import typing
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import time
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import json
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if typing.TYPE_CHECKING:
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from ..core import app
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import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
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class MonitoringHelper:
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"""Helper class for monitoring operations"""
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@staticmethod
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async def record_query_start(
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ap: app.Application,
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query: pipeline_query.Query,
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bot_id: str,
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bot_name: str,
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pipeline_id: str,
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pipeline_name: str,
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runner_name: str | None = None,
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) -> str:
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"""Record the start of query processing, returns message_id"""
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try:
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# Check if session exists, if not, record session start
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session_id = f'{query.launcher_type}_{query.launcher_id}'
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# Try to record message
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# Use JSON serialization to preserve message chain structure (including image URLs, etc.)
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if hasattr(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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else:
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message_content = str(query)
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# Variables will be updated in record_query_success after preproc stage sets them
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# Here we just record None, the full variables will be set when query completes
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message_id = await ap.monitoring_service.record_message(
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bot_id=bot_id,
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bot_name=bot_name,
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pipeline_id=pipeline_id,
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pipeline_name=pipeline_name,
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message_content=message_content,
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session_id=session_id,
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status='pending',
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level='info',
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platform=query.launcher_type.value
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if hasattr(query.launcher_type, 'value')
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else str(query.launcher_type),
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user_id=query.sender_id,
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runner_name=runner_name,
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variables=None, # Will be updated in record_query_success
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)
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# Update session activity or create new session if it doesn't exist
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# Always pass pipeline info to handle pipeline switches
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session_updated = await ap.monitoring_service.update_session_activity(
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session_id,
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pipeline_id=pipeline_id,
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pipeline_name=pipeline_name,
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)
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if not session_updated:
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# Session doesn't exist, create it
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await ap.monitoring_service.record_session_start(
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session_id=session_id,
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bot_id=bot_id,
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bot_name=bot_name,
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pipeline_id=pipeline_id,
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pipeline_name=pipeline_name,
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platform=query.launcher_type.value
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if hasattr(query.launcher_type, 'value')
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else str(query.launcher_type),
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user_id=query.sender_id,
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)
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return message_id
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except Exception as e:
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ap.logger.error(f'Failed to record query start: {e}')
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return ''
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@staticmethod
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async def record_query_success(
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ap: app.Application,
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message_id: str,
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query: pipeline_query.Query | None = None,
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):
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"""Record successful query processing by updating message status and variables"""
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try:
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if message_id:
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# Serialize query.variables (filtering out internal variables)
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query_variables_str = None
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if query and hasattr(query, 'variables') and query.variables:
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filtered_vars = {k: v for k, v in query.variables.items() if not k.startswith('_')}
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if filtered_vars:
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try:
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query_variables_str = json.dumps(filtered_vars, ensure_ascii=False, default=str)
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except Exception:
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pass
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await ap.monitoring_service.update_message_status(
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message_id=message_id,
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status='success',
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variables=query_variables_str,
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)
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except Exception as e:
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ap.logger.error(f'Failed to record query success: {e}')
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@staticmethod
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async def record_query_error(
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ap: app.Application,
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query: pipeline_query.Query,
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bot_id: str,
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bot_name: str,
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pipeline_id: str,
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pipeline_name: str,
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error: Exception,
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runner_name: str | None = None,
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) -> str:
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"""Record query processing error, returns message_id"""
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try:
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session_id = f'{query.launcher_type}_{query.launcher_id}'
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# Record error message
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message_id = await ap.monitoring_service.record_message(
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bot_id=bot_id,
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bot_name=bot_name,
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pipeline_id=pipeline_id,
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pipeline_name=pipeline_name,
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message_content=f'Error: {str(error)}',
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session_id=session_id,
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status='error',
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level='error',
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platform=query.launcher_type.value
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if hasattr(query.launcher_type, 'value')
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else str(query.launcher_type),
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user_id=query.sender_id,
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runner_name=runner_name,
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)
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# Record error log
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await ap.monitoring_service.record_error(
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bot_id=bot_id,
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bot_name=bot_name,
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pipeline_id=pipeline_id,
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pipeline_name=pipeline_name,
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error_type=type(error).__name__,
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error_message=str(error),
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session_id=session_id,
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stack_trace=traceback.format_exc(),
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message_id=message_id,
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)
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return message_id
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except Exception as e:
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ap.logger.error(f'Failed to record query error: {e}')
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return ''
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@staticmethod
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async def record_llm_call(
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ap: app.Application,
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query: pipeline_query.Query,
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bot_id: str,
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bot_name: str,
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pipeline_id: str,
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pipeline_name: str,
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model_name: str,
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input_tokens: int,
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output_tokens: int,
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duration_ms: int,
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status: str = 'success',
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cost: float | None = None,
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error_message: str | None = None,
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message_id: str | None = None,
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):
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"""Record LLM call"""
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try:
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session_id = f'{query.launcher_type}_{query.launcher_id}'
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await ap.monitoring_service.record_llm_call(
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bot_id=bot_id,
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bot_name=bot_name,
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pipeline_id=pipeline_id,
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pipeline_name=pipeline_name,
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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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output_tokens=output_tokens,
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duration=duration_ms,
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status=status,
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cost=cost,
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error_message=error_message,
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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: {e}')
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class LLMCallMonitor:
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"""Context manager for monitoring LLM calls"""
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def __init__(
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self,
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ap: app.Application,
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query: pipeline_query.Query,
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bot_id: str,
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bot_name: str,
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pipeline_id: str,
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pipeline_name: str,
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model_name: str,
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):
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self.ap = ap
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self.query = query
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self.bot_id = bot_id
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self.bot_name = bot_name
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self.pipeline_id = pipeline_id
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self.pipeline_name = pipeline_name
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self.model_name = model_name
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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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async def __aenter__(self):
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self.start_time = time.time()
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return self
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async def __aexit__(self, exc_type, exc_val, exc_tb):
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duration_ms = int((time.time() - self.start_time) * 1000)
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if exc_type is not None:
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# Error occurred
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await MonitoringHelper.record_llm_call(
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ap=self.ap,
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query=self.query,
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bot_id=self.bot_id,
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bot_name=self.bot_name,
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pipeline_id=self.pipeline_id,
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pipeline_name=self.pipeline_name,
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model_name=self.model_name,
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input_tokens=self.input_tokens,
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output_tokens=self.output_tokens,
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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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)
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else:
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# Success
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await MonitoringHelper.record_llm_call(
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ap=self.ap,
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query=self.query,
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bot_id=self.bot_id,
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bot_name=self.bot_name,
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pipeline_id=self.pipeline_id,
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pipeline_name=self.pipeline_name,
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model_name=self.model_name,
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input_tokens=self.input_tokens,
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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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)
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return False # Don't suppress exceptions
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