mirror of
https://github.com/langbot-app/LangBot.git
synced 2026-06-11 16:26:02 +00:00
feat(wecom): add user feedback support for WeChat Work AI Bot (#2078)
* 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>
This commit is contained in:
6mvp6
@@ -456,6 +456,31 @@ class MonitoringRouterGroup(group.RouterGroup):
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'platform',
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'user_id',
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]
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elif export_type == 'feedback':
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data = await self.ap.monitoring_service.export_feedback(
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bot_ids=bot_ids if bot_ids else None,
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pipeline_ids=pipeline_ids if pipeline_ids else None,
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start_time=start_time,
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end_time=end_time,
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limit=limit,
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)
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headers = [
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'id',
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'timestamp',
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'feedback_id',
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'feedback_type',
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'feedback_content',
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'inaccurate_reasons',
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'bot_id',
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'bot_name',
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'pipeline_id',
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'pipeline_name',
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'session_id',
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'message_id',
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'stream_id',
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'user_id',
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'platform',
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]
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else:
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return self.error(message=f'Invalid export type: {export_type}', code=400)
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@@ -486,3 +511,63 @@ class MonitoringRouterGroup(group.RouterGroup):
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)
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return response, 200
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@self.route('/feedback/stats', methods=['GET'], auth_type=group.AuthType.USER_TOKEN)
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async def get_feedback_stats() -> str:
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"""Get feedback statistics"""
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# Parse query parameters
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bot_ids = quart.request.args.getlist('botId')
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pipeline_ids = quart.request.args.getlist('pipelineId')
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start_time_str = quart.request.args.get('startTime')
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end_time_str = quart.request.args.get('endTime')
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# Parse datetime
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start_time = parse_iso_datetime(start_time_str)
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end_time = parse_iso_datetime(end_time_str)
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stats = await self.ap.monitoring_service.get_feedback_stats(
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bot_ids=bot_ids if bot_ids else None,
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pipeline_ids=pipeline_ids if pipeline_ids else None,
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start_time=start_time,
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end_time=end_time,
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)
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return self.success(data=stats)
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@self.route('/feedback', methods=['GET'], auth_type=group.AuthType.USER_TOKEN)
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async def get_feedback() -> str:
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"""Get feedback list"""
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# Parse query parameters
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bot_ids = quart.request.args.getlist('botId')
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pipeline_ids = quart.request.args.getlist('pipelineId')
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feedback_type_str = quart.request.args.get('feedbackType')
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start_time_str = quart.request.args.get('startTime')
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end_time_str = quart.request.args.get('endTime')
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limit = int(quart.request.args.get('limit', 100))
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offset = int(quart.request.args.get('offset', 0))
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# Parse datetime
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start_time = parse_iso_datetime(start_time_str)
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end_time = parse_iso_datetime(end_time_str)
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# Parse feedback type
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feedback_type = int(feedback_type_str) if feedback_type_str else None
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feedback_list, total = await self.ap.monitoring_service.get_feedback_list(
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bot_ids=bot_ids if bot_ids else None,
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pipeline_ids=pipeline_ids if pipeline_ids else None,
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feedback_type=feedback_type,
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start_time=start_time,
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end_time=end_time,
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limit=limit,
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offset=offset,
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)
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return self.success(
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data={
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'feedback': feedback_list,
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'total': total,
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'limit': limit,
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'offset': offset,
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}
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)
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@@ -1183,3 +1183,261 @@ class MonitoringService:
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}
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for row in rows
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]
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# ========== Feedback Methods ==========
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async def record_feedback(
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self,
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feedback_id: str,
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feedback_type: int,
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feedback_content: str | None = None,
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inaccurate_reasons: list[str] | None = None,
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bot_id: str | None = None,
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bot_name: str | None = None,
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pipeline_id: str | None = None,
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pipeline_name: str | None = None,
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session_id: str | None = None,
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message_id: str | None = None,
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stream_id: str | None = None,
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user_id: str | None = None,
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platform: str | None = None,
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) -> str:
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"""Record user feedback (like/dislike) from AI Bot conversation.
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Args:
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feedback_id: Unique feedback identifier from platform (e.g., WeChat Work)
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feedback_type: 1 = like (thumbs up), 2 = dislike (thumbs down)
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feedback_content: Optional user feedback text
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inaccurate_reasons: List of reasons for inaccurate response (for dislike)
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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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message_id: Message ID
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stream_id: Stream ID (for WeChat Work streaming messages)
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user_id: User ID
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platform: Platform name (e.g., 'wecom')
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Returns:
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The record ID
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"""
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import json
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record_id = str(uuid.uuid4())
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record_data = {
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'id': record_id,
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'timestamp': datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None),
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'feedback_id': feedback_id,
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'feedback_type': feedback_type,
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'feedback_content': feedback_content,
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'inaccurate_reasons': json.dumps(inaccurate_reasons, ensure_ascii=False) if inaccurate_reasons else None,
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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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'message_id': message_id,
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'stream_id': stream_id,
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'user_id': user_id,
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'platform': platform,
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}
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await self.ap.persistence_mgr.execute_async(
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sqlalchemy.insert(persistence_monitoring.MonitoringFeedback).values(record_data)
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)
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return record_id
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async def get_feedback_stats(
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self,
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bot_ids: list[str] | None = None,
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pipeline_ids: list[str] | None = None,
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start_time: datetime.datetime | None = None,
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end_time: datetime.datetime | None = None,
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) -> dict:
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"""Get feedback statistics.
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Returns:
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Dictionary with total likes, dislikes, and breakdown by bot/pipeline
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"""
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conditions = []
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if bot_ids:
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conditions.append(persistence_monitoring.MonitoringFeedback.bot_id.in_(bot_ids))
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if pipeline_ids:
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conditions.append(persistence_monitoring.MonitoringFeedback.pipeline_id.in_(pipeline_ids))
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if start_time:
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conditions.append(persistence_monitoring.MonitoringFeedback.timestamp >= start_time)
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if end_time:
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conditions.append(persistence_monitoring.MonitoringFeedback.timestamp <= end_time)
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# Get total likes (feedback_type = 1)
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likes_query = sqlalchemy.select(sqlalchemy.func.count(persistence_monitoring.MonitoringFeedback.id)).where(
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persistence_monitoring.MonitoringFeedback.feedback_type == 1
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)
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if conditions:
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likes_query = likes_query.where(sqlalchemy.and_(*conditions))
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likes_result = await self.ap.persistence_mgr.execute_async(likes_query)
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total_likes = likes_result.scalar() or 0
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# Get total dislikes (feedback_type = 2)
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dislikes_query = sqlalchemy.select(sqlalchemy.func.count(persistence_monitoring.MonitoringFeedback.id)).where(
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persistence_monitoring.MonitoringFeedback.feedback_type == 2
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)
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if conditions:
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dislikes_query = dislikes_query.where(sqlalchemy.and_(*conditions))
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dislikes_result = await self.ap.persistence_mgr.execute_async(dislikes_query)
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total_dislikes = dislikes_result.scalar() or 0
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# Get total feedback count
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total_query = sqlalchemy.select(sqlalchemy.func.count(persistence_monitoring.MonitoringFeedback.id))
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if conditions:
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total_query = total_query.where(sqlalchemy.and_(*conditions))
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total_result = await self.ap.persistence_mgr.execute_async(total_query)
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total_feedback = total_result.scalar() or 0
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# Calculate satisfaction rate
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satisfaction_rate = (total_likes / total_feedback * 100) if total_feedback > 0 else 0
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# Get feedback by bot
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bot_stats_query = sqlalchemy.select(
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persistence_monitoring.MonitoringFeedback.bot_id,
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persistence_monitoring.MonitoringFeedback.bot_name,
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sqlalchemy.func.count(persistence_monitoring.MonitoringFeedback.id).label('total'),
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sqlalchemy.func.sum(
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sqlalchemy.case((persistence_monitoring.MonitoringFeedback.feedback_type == 1, 1), else_=0)
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).label('likes'),
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sqlalchemy.func.sum(
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sqlalchemy.case((persistence_monitoring.MonitoringFeedback.feedback_type == 2, 1), else_=0)
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).label('dislikes'),
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).group_by(
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persistence_monitoring.MonitoringFeedback.bot_id,
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persistence_monitoring.MonitoringFeedback.bot_name,
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)
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if conditions:
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bot_stats_query = bot_stats_query.where(sqlalchemy.and_(*conditions))
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bot_stats_result = await self.ap.persistence_mgr.execute_async(bot_stats_query)
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bot_stats = [
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{
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'bot_id': row.bot_id,
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'bot_name': row.bot_name,
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'total': row.total,
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'likes': row.likes or 0,
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'dislikes': row.dislikes or 0,
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}
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for row in bot_stats_result.all()
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]
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return {
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'total_feedback': total_feedback,
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'total_likes': total_likes,
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'total_dislikes': total_dislikes,
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'satisfaction_rate': round(satisfaction_rate, 2),
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'by_bot': bot_stats,
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}
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async def get_feedback_list(
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self,
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bot_ids: list[str] | None = None,
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pipeline_ids: list[str] | None = None,
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feedback_type: int | None = None,
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start_time: datetime.datetime | None = None,
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end_time: datetime.datetime | None = None,
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limit: int = 100,
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offset: int = 0,
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) -> tuple[list[dict], int]:
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"""Get feedback list with filters."""
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conditions = []
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if bot_ids:
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conditions.append(persistence_monitoring.MonitoringFeedback.bot_id.in_(bot_ids))
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if pipeline_ids:
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conditions.append(persistence_monitoring.MonitoringFeedback.pipeline_id.in_(pipeline_ids))
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if feedback_type is not None:
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conditions.append(persistence_monitoring.MonitoringFeedback.feedback_type == feedback_type)
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if start_time:
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conditions.append(persistence_monitoring.MonitoringFeedback.timestamp >= start_time)
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if end_time:
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conditions.append(persistence_monitoring.MonitoringFeedback.timestamp <= end_time)
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# Get total count
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count_query = sqlalchemy.select(sqlalchemy.func.count(persistence_monitoring.MonitoringFeedback.id))
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if conditions:
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count_query = count_query.where(sqlalchemy.and_(*conditions))
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count_result = await self.ap.persistence_mgr.execute_async(count_query)
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total = count_result.scalar() or 0
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# Get feedback list
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query = sqlalchemy.select(persistence_monitoring.MonitoringFeedback).order_by(
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persistence_monitoring.MonitoringFeedback.timestamp.desc()
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)
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if conditions:
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query = query.where(sqlalchemy.and_(*conditions))
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query = query.limit(limit).offset(offset)
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result = await self.ap.persistence_mgr.execute_async(query)
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rows = result.all()
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return (
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[
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self.ap.persistence_mgr.serialize_model(
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persistence_monitoring.MonitoringFeedback, row[0] if isinstance(row, tuple) else row
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)
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for row in rows
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],
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total,
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)
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async def export_feedback(
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self,
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bot_ids: list[str] | None = None,
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pipeline_ids: list[str] | None = None,
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start_time: datetime.datetime | None = None,
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end_time: datetime.datetime | None = None,
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limit: int = 100000,
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) -> list[dict]:
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"""Export feedback as list of dictionaries for CSV conversion."""
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conditions = []
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if bot_ids:
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conditions.append(persistence_monitoring.MonitoringFeedback.bot_id.in_(bot_ids))
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if pipeline_ids:
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conditions.append(persistence_monitoring.MonitoringFeedback.pipeline_id.in_(pipeline_ids))
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if start_time:
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conditions.append(persistence_monitoring.MonitoringFeedback.timestamp >= start_time)
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if end_time:
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conditions.append(persistence_monitoring.MonitoringFeedback.timestamp <= end_time)
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query = sqlalchemy.select(persistence_monitoring.MonitoringFeedback).order_by(
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persistence_monitoring.MonitoringFeedback.timestamp.desc()
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)
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if conditions:
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query = query.where(sqlalchemy.and_(*conditions))
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query = query.limit(limit)
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result = await self.ap.persistence_mgr.execute_async(query)
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rows = result.all()
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return [
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{
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'id': row[0].id if isinstance(row, tuple) else row.id,
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'timestamp': self._format_timestamp(row[0].timestamp if isinstance(row, tuple) else row.timestamp),
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'feedback_id': row[0].feedback_id if isinstance(row, tuple) else row.feedback_id,
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'feedback_type': 'like'
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if (row[0].feedback_type if isinstance(row, tuple) else row.feedback_type) == 1
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else 'dislike',
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'feedback_content': row[0].feedback_content if isinstance(row, tuple) else row.feedback_content,
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'inaccurate_reasons': row[0].inaccurate_reasons if isinstance(row, tuple) else row.inaccurate_reasons,
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'bot_id': row[0].bot_id if isinstance(row, tuple) else row.bot_id,
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'bot_name': row[0].bot_name if isinstance(row, tuple) else row.bot_name,
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'pipeline_id': row[0].pipeline_id if isinstance(row, tuple) else row.pipeline_id,
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'pipeline_name': row[0].pipeline_name if isinstance(row, tuple) else row.pipeline_name,
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'session_id': row[0].session_id if isinstance(row, tuple) else row.session_id,
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'message_id': row[0].message_id if isinstance(row, tuple) else row.message_id,
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'stream_id': row[0].stream_id if isinstance(row, tuple) else row.stream_id,
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'user_id': row[0].user_id if isinstance(row, tuple) else row.user_id,
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'platform': row[0].platform if isinstance(row, tuple) else row.platform,
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}
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for row in rows
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]
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