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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
105
src/langbot/pkg/entity/persistence/monitoring.py
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105
src/langbot/pkg/entity/persistence/monitoring.py
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import sqlalchemy
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from .base import Base
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class MonitoringMessage(Base):
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"""Monitoring message records"""
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__tablename__ = 'monitoring_messages'
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id = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True)
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timestamp = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, index=True)
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bot_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True)
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bot_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
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pipeline_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True)
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pipeline_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
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message_content = sqlalchemy.Column(sqlalchemy.Text, nullable=False)
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session_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True)
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status = sqlalchemy.Column(sqlalchemy.String(50), nullable=False) # success, error, pending
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level = sqlalchemy.Column(sqlalchemy.String(50), nullable=False) # info, warning, error, debug
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platform = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
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user_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
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runner_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=True) # Runner name for this query
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variables = sqlalchemy.Column(sqlalchemy.Text, nullable=True) # Query variables as JSON string
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class MonitoringLLMCall(Base):
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"""LLM call records"""
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__tablename__ = 'monitoring_llm_calls'
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id = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True)
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timestamp = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, index=True)
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model_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
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input_tokens = sqlalchemy.Column(sqlalchemy.Integer, nullable=False)
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output_tokens = sqlalchemy.Column(sqlalchemy.Integer, nullable=False)
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total_tokens = sqlalchemy.Column(sqlalchemy.Integer, nullable=False)
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duration = sqlalchemy.Column(sqlalchemy.Integer, nullable=False) # milliseconds
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cost = sqlalchemy.Column(sqlalchemy.Float, nullable=True)
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status = sqlalchemy.Column(sqlalchemy.String(50), nullable=False) # success, error
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bot_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True)
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bot_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
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pipeline_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True)
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pipeline_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
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session_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
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error_message = sqlalchemy.Column(sqlalchemy.Text, nullable=True)
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message_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True) # Associated message ID
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class MonitoringSession(Base):
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"""Session tracking records"""
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__tablename__ = 'monitoring_sessions'
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session_id = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True)
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bot_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True)
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bot_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
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pipeline_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True)
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pipeline_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
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message_count = sqlalchemy.Column(sqlalchemy.Integer, nullable=False, default=0)
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start_time = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, index=True)
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last_activity = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, index=True)
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is_active = sqlalchemy.Column(sqlalchemy.Boolean, nullable=False, default=True, index=True)
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platform = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
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user_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
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class MonitoringError(Base):
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"""Error log records"""
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__tablename__ = 'monitoring_errors'
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id = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True)
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timestamp = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, index=True)
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error_type = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
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error_message = sqlalchemy.Column(sqlalchemy.Text, nullable=False)
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bot_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True)
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bot_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
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pipeline_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True)
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pipeline_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
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session_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
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stack_trace = sqlalchemy.Column(sqlalchemy.Text, nullable=True)
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message_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True) # Associated message ID
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class MonitoringEmbeddingCall(Base):
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"""Embedding call records"""
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__tablename__ = 'monitoring_embedding_calls'
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id = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True)
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timestamp = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, index=True)
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model_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
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prompt_tokens = sqlalchemy.Column(sqlalchemy.Integer, nullable=False)
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total_tokens = sqlalchemy.Column(sqlalchemy.Integer, nullable=False)
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duration = sqlalchemy.Column(sqlalchemy.Integer, nullable=False) # milliseconds
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input_count = sqlalchemy.Column(sqlalchemy.Integer, nullable=False) # Number of input texts
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status = sqlalchemy.Column(sqlalchemy.String(50), nullable=False) # success, error
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error_message = sqlalchemy.Column(sqlalchemy.Text, nullable=True)
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# Optional context fields
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knowledge_base_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True)
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query_text = sqlalchemy.Column(sqlalchemy.Text, nullable=True) # For retrieval calls
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session_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True)
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message_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True)
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call_type = sqlalchemy.Column(sqlalchemy.String(50), nullable=True) # embedding, retrieve
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