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feat(monitoring): 关联反馈记录与消息ID，新增反馈导出 (#2120)

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* feat(monitoring): link feedback to LangBot message ID and add feedback export

- Add pipeline→adapter notification hook so monitoring message ID is
  passed back to WecomBotAdapter after creation
- Store stream_id→monitoring_message_id mapping with 10-min TTL cleanup
- Replace feedback record stream_id with LangBot monitoring message ID
  so feedback can be linked to actual message records
- Rename streamId label to "Related Query ID" in all 7 i18n locales
- Remove non-functional message ID jump button from FeedbackList
- Add feedback export option to ExportDropdown (backend already implemented)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* feat(monitoring): add combined refresh handler for monitoring and feedback data

* fix(wecombot): improve stream ID mapping and error logging in WecomBotAdapter

* feat(lark): add monitoring message ID mapping for feedback correlation

* feat(lark): rename monitoring message ID mappings for clarity and consistency
feat(feedback): add button to view conversation for feedback items

* feat(bot-session-monitor): add feedback handling for bot messages with visual indicators

* feat(bot-session-monitor): enhance feedback display with hover content for like/dislike indicators

* fix(dingtalk): use voice recognition text instead of raw audio binary

When DingTalk sends a voice message to the bot, the callback JSON contains
a 'recognition' field with the speech-to-text result (powered by Qwen).

Previously, LangBot only extracted the 'downloadCode' to download the raw
audio binary and passed it as 'file_base64' to LLM APIs, which caused
400 errors since most models don't support this content type.

This patch:
- Extracts the 'recognition' field from DingTalk audio message content
- Uses it as plain text input to the LLM instead of raw audio
- Falls back to audio binary only when no recognition text is available
- Fixes duplicate text issue for audio messages with recognition

Fixes voice messages returning 'Request failed' on all LLM models.

* fix: add filereader for dingtalk,lark (#2122)

* fix: add filereader for dingtalk

* feat: add lark

* feat: update uv.lock

* chore: update version to 4.9.6 in pyproject.toml, __init__.py, and uv.lock

* fix: update langbot-plugin version to 0.3.8

* fix: update langbot-plugin version to 0.3.8

* fix(wecombot): extend StreamSession TTL for feedback sessions to prevent context data loss

StreamSessionManager.cleanup() removes sessions after 60s TTL, but feedback
events (like → cancel → dislike) can arrive later. When the session expires
before the dislike event, all context fields (session_id, user_id, message_id,
stream_id) are lost because get_session_by_feedback_id() returns None.

Fix: Sessions with registered feedback_ids now use a 10-minute TTL, aligned
with the adapter's _stream_to_monitoring_msg TTL in wecombot.py.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

---------

Co-authored-by: 6mvp6 <13727783693@163.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: fdc310 <2213070223@qq.com>
Co-authored-by: haiyangbg <zhouhaiyangaa@gmail.com>
Co-authored-by: Guanchao Wang <wangcham233@gmail.com>
Co-authored-by: Rock Chin <1010553892@qq.com>
This commit is contained in:
6mvp6
2026-04-18 12:56:41 +08:00
committed by GitHub
parent 917edb3413
commit f8010a20eb
15 changed files with 241 additions and 47 deletions
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49
src/langbot/pkg/platform/sources/lark.py
View File

@@ -787,6 +787,13 @@ class LarkAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
card_id_dict: dict[str, str] # 消息id到卡片id的映射，便于创建卡片后的发送消息到指定卡片
# Monitoring message ID mapping for feedback correlation
# Temp: user Lark message ID → monitoring_message_id (populated by on_monitoring_message_created, consumed by create_message_card)
pending_monitoring_msg: dict[str, str]
# Final: reply Lark message ID → (monitoring_message_id, timestamp) (used by feedback callbacks)
reply_to_monitoring_msg: dict[str, tuple[str, float]]
_MONITORING_MAPPING_TTL = 600 # 10 minutes
seq: int # 用于在发送卡片消息中识别消息顺序，直接以seq作为标识
bot_uuid: str = None # 机器人UUID
app_ticket: str = None # 商店应用用到
@@ -833,6 +840,11 @@ class LarkAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
else:
session_id = None
# Resolve monitoring message ID from reply message mapping
monitoring_msg_id = None
if open_message_id and open_message_id in self.reply_to_monitoring_msg:
monitoring_msg_id = self.reply_to_monitoring_msg[open_message_id][0]
feedback_event = platform_events.FeedbackEvent(
feedback_id=getattr(event.header, 'event_id', str(uuid.uuid4())),
feedback_type=feedback_type,
@@ -840,6 +852,7 @@ class LarkAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
user_id=user_id,
session_id=session_id,
message_id=open_message_id,
stream_id=monitoring_msg_id,
source_platform_object=event,
)
@@ -878,6 +891,8 @@ class LarkAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
logger=logger,
lark_tenant_key=config.get('lark_tenant_key', ''),
card_id_dict={},
pending_monitoring_msg={},
reply_to_monitoring_msg={},
seq=1,
listeners={},
quart_app=quart_app,
@@ -1018,6 +1033,22 @@ class LarkAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
is_stream = True
return is_stream
async def on_monitoring_message_created(self, query, monitoring_message_id: str):
"""Called by pipeline after monitoring message is created, to map user message ID to monitoring message ID."""
try:
user_msg_id = query.message_event.message_chain.message_id
if user_msg_id:
self.pending_monitoring_msg[user_msg_id] = monitoring_message_id
except Exception as e:
await self.logger.debug(f'Failed to map message to monitoring message: {e}')
def _cleanup_monitoring_mapping(self):
"""Remove entries older than TTL from the reply-to-monitoring mapping."""
now = time.time()
expired = [k for k, (_, ts) in self.reply_to_monitoring_msg.items() if now - ts > self._MONITORING_MAPPING_TTL]
for k in expired:
del self.reply_to_monitoring_msg[k]
async def create_card_id(self, message_id):
try:
# self.logger.debug('飞书支持stream输出,创建卡片......')
@@ -1257,6 +1288,18 @@ class LarkAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
raise Exception(
f'client.im.v1.message.reply failed, code: {response.code}, msg: {response.msg}, log_id: {response.get_log_id()}, resp: \n{json.dumps(json.loads(response.raw.content), indent=4, ensure_ascii=False)}'
)
# Transfer monitoring message mapping: user msg ID → reply msg ID
try:
user_msg_id = event.message_chain.message_id
reply_msg_id = getattr(response.data, 'message_id', None)
monitoring_msg_id = self.pending_monitoring_msg.pop(user_msg_id, None)
if reply_msg_id and monitoring_msg_id:
self.reply_to_monitoring_msg[reply_msg_id] = (monitoring_msg_id, time.time())
self._cleanup_monitoring_mapping()
except Exception as e:
asyncio.create_task(self.logger.debug(f'Failed to transfer monitoring mapping in create_message_card: {e}'))
return True
async def reply_message(
@@ -1567,6 +1610,11 @@ class LarkAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
else:
session_id = None
# Resolve monitoring message ID from reply message mapping
monitoring_msg_id = None
if open_message_id and open_message_id in self.reply_to_monitoring_msg:
monitoring_msg_id = self.reply_to_monitoring_msg[open_message_id][0]
feedback_event = platform_events.FeedbackEvent(
feedback_id=data.get('header', {}).get('event_id', str(uuid.uuid4())),
feedback_type=feedback_type,
@@ -1574,6 +1622,7 @@ class LarkAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
user_id=user_id,
session_id=session_id,
message_id=open_message_id,
stream_id=monitoring_msg_id,
source_platform_object=data,
)