Files
LangBot/src/langbot/pkg/pipeline/process/handlers/chat.py
Copilot 90a22d894d fix: prevent memory overflow from excessive logging in streaming and query processing (#1879)
* Initial plan

* fix: reduce excessive logging to prevent memory overflow

- Add log file rotation (10MB max per file, 5 backups)
- Reduce streaming response logging (every 10th chunk instead of every chunk)
- Remove debug logging from controller tight loop
- Add summary logging after streaming completes

Co-authored-by: RockChinQ <45992437+RockChinQ@users.noreply.github.com>

* refactor: address code review feedback

- Extract log rotation config to module-level constants
- Keep first streaming chunk at INFO level for connection debugging
- Use DEBUG level for subsequent chunks

Co-authored-by: RockChinQ <45992437+RockChinQ@users.noreply.github.com>

* style: fix code formatting whitespace

Co-authored-by: RockChinQ <45992437+RockChinQ@users.noreply.github.com>

---------

Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: RockChinQ <45992437+RockChinQ@users.noreply.github.com>
2025-12-22 18:25:24 +08:00

151 lines
6.4 KiB
Python

from __future__ import annotations
import uuid
import typing
import traceback
from .. import handler
from ... import entities
from ....provider import runner as runner_module
import langbot_plugin.api.entities.events as events
from ....utils import importutil
from ....provider import runners
import langbot_plugin.api.entities.builtin.provider.session as provider_session
import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
importutil.import_modules_in_pkg(runners)
class ChatMessageHandler(handler.MessageHandler):
async def handle(
self,
query: pipeline_query.Query,
) -> typing.AsyncGenerator[entities.StageProcessResult, None]:
"""处理"""
# 调API
# 生成器
# 触发插件事件
event_class = (
events.PersonNormalMessageReceived
if query.launcher_type == provider_session.LauncherTypes.PERSON
else events.GroupNormalMessageReceived
)
event = event_class(
launcher_type=query.launcher_type.value,
launcher_id=query.launcher_id,
sender_id=query.sender_id,
text_message=str(query.message_chain),
message_event=query.message_event,
message_chain=query.message_chain,
query=query,
)
# Get bound plugins for filtering
bound_plugins = query.variables.get('_pipeline_bound_plugins', None)
event_ctx = await self.ap.plugin_connector.emit_event(event, bound_plugins)
is_create_card = False # 判断下是否需要创建流式卡片
if event_ctx.is_prevented_default():
if event_ctx.event.reply_message_chain is not None:
mc = event_ctx.event.reply_message_chain
query.resp_messages.append(mc)
yield entities.StageProcessResult(result_type=entities.ResultType.CONTINUE, new_query=query)
else:
yield entities.StageProcessResult(result_type=entities.ResultType.INTERRUPT, new_query=query)
else:
if event_ctx.event.user_message_alter is not None:
# if isinstance(event_ctx.event, str): # 现在暂时不考虑多模态alter
query.user_message.content = event_ctx.event.user_message_alter
text_length = 0
try:
is_stream = await query.adapter.is_stream_output_supported()
except AttributeError:
is_stream = False
try:
for r in runner_module.preregistered_runners:
if r.name == query.pipeline_config['ai']['runner']['runner']:
runner = r(self.ap, query.pipeline_config)
break
else:
raise ValueError(f'Request Runner not found: {query.pipeline_config["ai"]["runner"]["runner"]}')
if is_stream:
resp_message_id = uuid.uuid4()
chunk_count = 0 # Track streaming chunks to reduce excessive logging
async for result in runner.run(query):
result.resp_message_id = str(resp_message_id)
if query.resp_messages:
query.resp_messages.pop()
if query.resp_message_chain:
query.resp_message_chain.pop()
# 此时连接外部 AI 服务正常,创建卡片
if not is_create_card: # 只有不是第一次才创建卡片
await query.adapter.create_message_card(str(resp_message_id), query.message_event)
is_create_card = True
query.resp_messages.append(result)
chunk_count += 1
# Only log every 10th chunk to reduce excessive logging during streaming
# This prevents memory overflow from thousands of log entries per conversation
# First chunk uses INFO level to confirm connection establishment
if chunk_count == 1:
self.ap.logger.info(
f'Conversation({query.query_id}) Streaming started: {self.cut_str(result.readable_str())}'
)
elif chunk_count % 10 == 0:
self.ap.logger.debug(
f'Conversation({query.query_id}) Streaming chunk {chunk_count}: {self.cut_str(result.readable_str())}'
)
if result.content is not None:
text_length += len(result.content)
yield entities.StageProcessResult(result_type=entities.ResultType.CONTINUE, new_query=query)
# Log final summary after streaming completes
self.ap.logger.info(
f'Conversation({query.query_id}) Streaming completed: {chunk_count} chunks, {text_length} chars'
)
else:
async for result in runner.run(query):
query.resp_messages.append(result)
self.ap.logger.info(
f'Conversation({query.query_id}) Response: {self.cut_str(result.readable_str())}'
)
if result.content is not None:
text_length += len(result.content)
yield entities.StageProcessResult(result_type=entities.ResultType.CONTINUE, new_query=query)
query.session.using_conversation.messages.append(query.user_message)
query.session.using_conversation.messages.extend(query.resp_messages)
except Exception as e:
self.ap.logger.error(f'Conversation({query.query_id}) Request Failed: {type(e).__name__} {str(e)}')
traceback.print_exc()
hide_exception_info = query.pipeline_config['output']['misc']['hide-exception']
yield entities.StageProcessResult(
result_type=entities.ResultType.INTERRUPT,
new_query=query,
user_notice='请求失败' if hide_exception_info else f'{e}',
error_notice=f'{e}',
debug_notice=traceback.format_exc(),
)
finally:
# TODO statistics
pass