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feat(monitoring): 关联反馈记录与消息ID，新增反馈导出 (#2120)
* 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
@@ -71,6 +71,11 @@ class StreamSession:
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class StreamSessionManager:
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"""管理 stream 会话的生命周期，并负责队列的生产消费。"""
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# Sessions with registered feedback_ids use a longer TTL to survive the
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# full like → cancel → dislike feedback flow. Must align with the adapter's
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# _stream_to_monitoring_msg TTL (wecombot.py).
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_FEEDBACK_SESSION_TTL = 600 # 10 minutes
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def __init__(self, logger: EventLogger, ttl: int = 60) -> None:
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self.logger = logger
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@@ -214,11 +219,17 @@ class StreamSessionManager:
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session.last_access = time.time()
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def cleanup(self) -> None:
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"""定期清理过期会话，防止队列与映射无上限累积。"""
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"""定期清理过期会话，防止队列与映射无上限累积。
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已注册 feedback_id 的会话使用更长的 TTL，确保用户在点赞/取消/点踩流程中
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不会因为 session 被提前清除而丢失上下文信息。
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"""
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now = time.time()
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expired: list[str] = []
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for stream_id, session in self._sessions.items():
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if now - session.last_access > self.ttl:
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# Sessions with registered feedback_ids use a longer TTL
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effective_ttl = self._FEEDBACK_SESSION_TTL if session.feedback_id else self.ttl
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if now - session.last_access > effective_ttl:
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expired.append(stream_id)
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for stream_id in expired:
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@@ -297,6 +297,9 @@ class RuntimePipeline:
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)
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# Store message_id in query variables for LLM call monitoring
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query.variables['_monitoring_message_id'] = message_id
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# Notify adapter so it can map platform-specific IDs to monitoring message ID
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if hasattr(query.adapter, 'on_monitoring_message_created'):
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await query.adapter.on_monitoring_message_created(query, message_id)
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except Exception as e:
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self.ap.logger.error(f'Failed to record query start: {e}')
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@@ -787,6 +787,13 @@ class LarkAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
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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
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# Monitoring message ID mapping for feedback correlation
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# Temp: user Lark message ID → monitoring_message_id (populated by on_monitoring_message_created, consumed by create_message_card)
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pending_monitoring_msg: dict[str, str]
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# Final: reply Lark message ID → (monitoring_message_id, timestamp) (used by feedback callbacks)
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reply_to_monitoring_msg: dict[str, tuple[str, float]]
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_MONITORING_MAPPING_TTL = 600 # 10 minutes
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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
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bot_uuid: str = None # 机器人UUID
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app_ticket: str = None # 商店应用用到
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@@ -833,6 +840,11 @@ class LarkAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
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else:
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session_id = None
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# Resolve monitoring message ID from reply message mapping
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monitoring_msg_id = None
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if open_message_id and open_message_id in self.reply_to_monitoring_msg:
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monitoring_msg_id = self.reply_to_monitoring_msg[open_message_id][0]
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feedback_event = platform_events.FeedbackEvent(
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feedback_id=getattr(event.header, 'event_id', str(uuid.uuid4())),
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feedback_type=feedback_type,
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@@ -840,6 +852,7 @@ class LarkAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
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user_id=user_id,
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session_id=session_id,
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message_id=open_message_id,
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stream_id=monitoring_msg_id,
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source_platform_object=event,
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)
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@@ -878,6 +891,8 @@ class LarkAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
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logger=logger,
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lark_tenant_key=config.get('lark_tenant_key', ''),
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card_id_dict={},
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pending_monitoring_msg={},
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reply_to_monitoring_msg={},
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seq=1,
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listeners={},
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quart_app=quart_app,
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@@ -1018,6 +1033,22 @@ class LarkAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
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is_stream = True
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return is_stream
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async def on_monitoring_message_created(self, query, monitoring_message_id: str):
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"""Called by pipeline after monitoring message is created, to map user message ID to monitoring message ID."""
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try:
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user_msg_id = query.message_event.message_chain.message_id
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if user_msg_id:
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self.pending_monitoring_msg[user_msg_id] = monitoring_message_id
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except Exception as e:
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await self.logger.debug(f'Failed to map message to monitoring message: {e}')
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def _cleanup_monitoring_mapping(self):
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"""Remove entries older than TTL from the reply-to-monitoring mapping."""
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now = time.time()
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expired = [k for k, (_, ts) in self.reply_to_monitoring_msg.items() if now - ts > self._MONITORING_MAPPING_TTL]
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for k in expired:
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del self.reply_to_monitoring_msg[k]
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async def create_card_id(self, message_id):
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try:
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# self.logger.debug('飞书支持stream输出,创建卡片......')
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@@ -1257,6 +1288,18 @@ class LarkAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
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raise Exception(
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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)}'
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)
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# Transfer monitoring message mapping: user msg ID → reply msg ID
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try:
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user_msg_id = event.message_chain.message_id
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reply_msg_id = getattr(response.data, 'message_id', None)
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monitoring_msg_id = self.pending_monitoring_msg.pop(user_msg_id, None)
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if reply_msg_id and monitoring_msg_id:
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self.reply_to_monitoring_msg[reply_msg_id] = (monitoring_msg_id, time.time())
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self._cleanup_monitoring_mapping()
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except Exception as e:
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asyncio.create_task(self.logger.debug(f'Failed to transfer monitoring mapping in create_message_card: {e}'))
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return True
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async def reply_message(
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@@ -1567,6 +1610,11 @@ class LarkAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
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else:
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session_id = None
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# Resolve monitoring message ID from reply message mapping
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monitoring_msg_id = None
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if open_message_id and open_message_id in self.reply_to_monitoring_msg:
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monitoring_msg_id = self.reply_to_monitoring_msg[open_message_id][0]
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feedback_event = platform_events.FeedbackEvent(
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feedback_id=data.get('header', {}).get('event_id', str(uuid.uuid4())),
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feedback_type=feedback_type,
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@@ -1574,6 +1622,7 @@ class LarkAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
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user_id=user_id,
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session_id=session_id,
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message_id=open_message_id,
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stream_id=monitoring_msg_id,
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source_platform_object=data,
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)
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@@ -1,6 +1,7 @@
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from __future__ import annotations
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import typing
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import asyncio
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import time
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import traceback
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import datetime
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@@ -293,6 +294,8 @@ class WecomBotAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
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_ws_mode: bool = False
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bot_name: str = ''
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listeners: dict = {}
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_stream_to_monitoring_msg: dict = {} # Maps stream_id to (monitoring_message_id, timestamp)
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_STREAM_MAPPING_TTL = 600 # 10 minutes
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def __init__(self, config: dict, logger: EventLogger):
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enable_webhook = config.get('enable-webhook', False)
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@@ -329,8 +332,9 @@ class WecomBotAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
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bot_account_id=bot_account_id,
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bot_name=bot_name,
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event_converter=event_converter,
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listeners={},
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_stream_to_monitoring_msg={},
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)
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self.listeners = {}
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async def reply_message(
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self,
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@@ -422,6 +426,23 @@ class WecomBotAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
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"""设置 bot UUID（用于生成 webhook URL）"""
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self.bot_uuid = bot_uuid
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async def on_monitoring_message_created(self, query, monitoring_message_id: str):
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"""Called by pipeline after monitoring message is created, to map stream_id to monitoring message ID."""
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try:
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stream_id = query.message_event.source_platform_object.stream_id
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if stream_id:
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self._stream_to_monitoring_msg[stream_id] = (monitoring_message_id, time.time())
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self._cleanup_stream_mapping()
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except Exception as e:
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await self.logger.debug(f'Failed to map stream_id to monitoring message: {e}')
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def _cleanup_stream_mapping(self):
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"""Remove entries older than TTL from the stream_id to monitoring message mapping."""
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now = time.time()
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expired = [k for k, (_, ts) in self._stream_to_monitoring_msg.items() if now - ts > self._STREAM_MAPPING_TTL]
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for k in expired:
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del self._stream_to_monitoring_msg[k]
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async def _on_feedback(self, **kwargs):
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"""Handle feedback event from WeChat Work AI Bot SDK and dispatch as FeedbackEvent."""
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try:
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@@ -447,6 +468,11 @@ class WecomBotAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
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message_id = session.msg_id
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stream_id = session.stream_id
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# Resolve stream_id to LangBot monitoring message ID if available
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monitoring_msg_id = None
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if stream_id and stream_id in self._stream_to_monitoring_msg:
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monitoring_msg_id = self._stream_to_monitoring_msg[stream_id][0]
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await self.logger.info(
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f'Feedback event: feedback_id={feedback_id}, type={feedback_type}, '
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f'session_id={session_id}, user_id={user_id}, message_id={message_id}'
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@@ -460,7 +486,7 @@ class WecomBotAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
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user_id=user_id,
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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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stream_id=monitoring_msg_id or stream_id,
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source_platform_object=session,
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)
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