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https://github.com/langbot-app/LangBot.git
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6e37aae636
* feat(wecom): add user feedback support for WeChat Work AI Bot This commit implements user feedback functionality (like/dislike) for WeChat Work AI Bot conversations, including: Backend changes: - Add feedback_id and stream_id fields to WecomBotEvent - Implement feedback event handling in WecomBotClient (api.py) - Add StreamSessionManager._feedback_index for feedback_id lookup - Add on_feedback decorator for custom feedback handlers - Create MonitoringFeedback entity for database persistence - Add dbm025 migration for monitoring_feedback table - Implement FeedbackMonitor helper class - Update all platform adapters with ap parameter support - Update botmgr to pass bot_info for monitoring context Frontend changes: - Add FeedbackCard and FeedbackList components - Add useFeedbackData hook for feedback data fetching - Add feedback tab to monitoring page - Add feedback types and interfaces - Add i18n translations (zh-Hans, en-US) Other changes: - Update Dockerfile with Chinese mirror for faster builds - Update docker-compose.yaml with network configuration - Update .gitignore for docker data and backup files Note: Known issues that need future improvement: - feedback_type=3 (cancel) is recorded but not properly handled - Duplicate feedback records are not deduplicated * chore: remove unnecessary migration for new table will be created automatically * chore: ruff format * chore: prettier * feat: add feedback handling support across multiple platform adapters * fix(web): remove unused imports and variables in monitoring module --------- Co-authored-by: 6mvp6 <13727783693@163.com> Co-authored-by: Junyan Qin <rockchinq@gmail.com>
132 lines
7.6 KiB
Python
132 lines
7.6 KiB
Python
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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user_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=True) # User display name
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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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role = sqlalchemy.Column(sqlalchemy.String(50), nullable=True, default='user') # user, assistant
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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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user_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=True) # User display name
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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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class MonitoringFeedback(Base):
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"""User feedback records (like/dislike) from AI Bot conversations"""
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__tablename__ = 'monitoring_feedback'
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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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feedback_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, unique=True, index=True)
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feedback_type = sqlalchemy.Column(sqlalchemy.Integer, nullable=False) # 1=like, 2=dislike
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feedback_content = sqlalchemy.Column(sqlalchemy.Text, nullable=True) # User feedback text
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inaccurate_reasons = sqlalchemy.Column(sqlalchemy.Text, nullable=True) # JSON list of inaccurate reasons
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# Context fields
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bot_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True)
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bot_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
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pipeline_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True)
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pipeline_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
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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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stream_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True)
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user_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
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platform = sqlalchemy.Column(sqlalchemy.String(255), nullable=True) # e.g., wecom
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