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
2026-03-30 20:23:52 +08:00
committed by GitHub
parent 921d12f596
commit 6e37aae636
18 changed files with 4721 additions and 1110 deletions
+45
View File
@@ -9,6 +9,7 @@ from ..core import app, entities as core_entities, taskmgr
from ..discover import engine
from ..entity.persistence import bot as persistence_bot
from ..entity.persistence import pipeline as persistence_pipeline
from ..entity.errors import platform as platform_errors
@@ -141,6 +142,50 @@ class RuntimeBot:
self.adapter.register_listener(platform_events.FriendMessage, on_friend_message)
self.adapter.register_listener(platform_events.GroupMessage, on_group_message)
# Register feedback listener (only effective on adapters that support it)
async def on_feedback(
event: platform_events.FeedbackEvent,
adapter: abstract_platform_adapter.AbstractMessagePlatformAdapter,
):
try:
# Resolve pipeline name
pipeline_name = ''
if self.bot_entity.use_pipeline_uuid:
try:
pipeline_result = await self.ap.persistence_mgr.execute_async(
sqlalchemy.select(persistence_pipeline.LegacyPipeline.name).where(
persistence_pipeline.LegacyPipeline.uuid == self.bot_entity.use_pipeline_uuid
)
)
pipeline_row = pipeline_result.first()
if pipeline_row:
pipeline_name = pipeline_row[0]
except Exception:
pass
await self.ap.monitoring_service.record_feedback(
feedback_id=event.feedback_id,
feedback_type=event.feedback_type,
feedback_content=event.feedback_content,
inaccurate_reasons=event.inaccurate_reasons,
bot_id=self.bot_entity.uuid,
bot_name=self.bot_entity.name,
pipeline_id=self.bot_entity.use_pipeline_uuid or '',
pipeline_name=pipeline_name,
session_id=event.session_id,
message_id=event.message_id,
stream_id=event.stream_id,
user_id=event.user_id,
platform=adapter.__class__.__name__,
)
await self.logger.info(
f'Recorded feedback: feedback_id={event.feedback_id}, type={event.feedback_type}'
)
except Exception:
await self.logger.error(f'Failed to record feedback: {traceback.format_exc()}')
self.adapter.register_listener(platform_events.FeedbackEvent, on_feedback)
async def run(self):
async def exception_wrapper():
try: