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refactor: extract process_message orchestration from agent

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This commit is contained in:
jimi
2026-03-01 16:21:22 +08:00
parent 4b2d3347da
commit a6b7bf1982
2 changed files with 117 additions and 96 deletions
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110
core/message_orchestrator.py Normal file
View File

@@ -0,0 +1,110 @@
from __future__ import annotations
import asyncio
from typing import Any
from core.ai_reply_flow import execute_ai_turn
from core.find_image_flow import handle_find_image_batch_flow
from core.order_flow import handle_order_notification
from core.prompt_flow import build_prompt_bundle
from core.reply_finalize_flow import finalize_ai_reply
from utils.metrics_tracker import emit as metrics_emit
from utils.observability import build_trace_id
async def process_incoming_message(agent: Any, message: Any) -> Any:
"""主消息处理编排：预处理 -> 业务流 -> AI -> 收尾。"""
trace_id = build_trace_id(message.acc_id, message.from_id, message.msg_id, message.msg[:64])
agent._activity_log(
"agent_inbound",
trace_id=trace_id,
acc_id=message.acc_id,
customer_id=message.from_id,
msg=message.msg,
msg_type=message.msg_type,
)
metrics_emit("inbound_msg", customer_id=message.from_id, acc_id=message.acc_id)
state = agent._get_conversation_state(message.from_id)
pre_response = await agent.pre_rule_service.run(message=message, state=state, trace_id=trace_id)
if pre_response is not None:
return pre_response
new_stage = agent._detect_stage(message.msg)
if new_stage != state.stage:
state.stage = new_stage
from datetime import datetime
state.last_update = datetime.now().isoformat()
order_response = await handle_order_notification(agent, message=message, state=state)
if order_response is not None:
return order_response
customer_text, _ = agent._split_customer_text(message.msg)
shop_type = agent._get_shop_type(message.acc_id or "", message.goods_name or "")
flow_response = await handle_find_image_batch_flow(
agent,
message=message,
state=state,
customer_text=customer_text,
shop_type=shop_type,
)
if flow_response is not None:
return flow_response
prompt_bundle = build_prompt_bundle(agent, message=message, state=state)
user_prompt = prompt_bundle.user_prompt
deps = prompt_bundle.deps
history = prompt_bundle.history
agent._log_block("PROMPT->AI 前置提示词", user_prompt)
try:
reply_text = await execute_ai_turn(
agent,
message=message,
state=state,
user_prompt=user_prompt,
deps=deps,
history=history,
)
except Exception as e:
err_str = str(e)
print(f"[Agent] AI 调用失败: {e}，使用兜底回复")
agent._activity_log("agent_ai_error", customer_id=message.from_id, acc_id=message.acc_id, error=err_str)
metrics_emit("ai_call_failed", customer_id=message.from_id, acc_id=message.acc_id)
if "AccountOverdueError" in err_str or "overdue" in err_str.lower():
asyncio.create_task(agent._notify_wechat_overdue())
else:
asyncio.create_task(
agent._notify_wechat(
f"⚠️ **AI调用异常**\n"
f"客户：{message.from_id}\n"
f"店铺：{message.acc_id}\n"
f"错误：{err_str[:200]}",
tag="AI异常",
)
)
reply_text = None
else:
metrics_emit("ai_call_success", customer_id=message.from_id, acc_id=message.acc_id)
if not reply_text:
fallback_text = await agent._rewrite_reply_with_ai(
message=message,
state=state,
reply="好嘞，你稍等下，我这边看一下",
scene="fallback_reply",
)
from core.pydantic_ai_agent import AgentResponse
return AgentResponse(reply=fallback_text, should_reply=True, need_transfer=False)
return await finalize_ai_reply(
agent,
message=message,
state=state,
reply_text=reply_text,
)

103
core/pydantic_ai_agent.py
View File

@@ -75,6 +75,7 @@ from core.batch_quote_helpers import (
)
from core.prompt_builder import build_prompt as build_agent_prompt, split_customer_text
from core.image_workflow_router import handle_image_workflow as route_image_workflow
from core.message_orchestrator import process_incoming_message
load_dotenv()
@@ -729,6 +730,9 @@ class CustomerServiceAgent:
_is_political_inquiry = staticmethod(is_political_inquiry)
_is_map_inquiry = staticmethod(is_map_inquiry)
_get_shop_type = staticmethod(_get_shop_type)
_notify_wechat = staticmethod(_notify_wechat)
_notify_wechat_overdue = staticmethod(_notify_wechat_overdue)
_calc_avg_complexity = staticmethod(calc_avg_complexity)
_get_conversation_context = staticmethod(get_conversation_context)
@@ -759,101 +763,8 @@ class CustomerServiceAgent:
return clean in self._COOLDOWN_PATTERNS
async def process_message(self, message: CustomerMessage) -> AgentResponse:
"""处理客户消息并生成回复"""
trace_id = build_trace_id(message.acc_id, message.from_id, message.msg_id, message.msg[:64])
self._activity_log(
"agent_inbound",
trace_id=trace_id,
acc_id=message.acc_id,
customer_id=message.from_id,
msg=message.msg,
msg_type=message.msg_type,
)
metrics_emit("inbound_msg", customer_id=message.from_id, acc_id=message.acc_id)
# 获取或创建对话状态
state = self._get_conversation_state(message.from_id)
pre_response = await self.pre_rule_service.run(message=message, state=state, trace_id=trace_id)
if isinstance(pre_response, AgentResponse):
return pre_response
# 检测售前/售后
new_stage = self._detect_stage(message.msg)
if new_stage != state.stage:
state.stage = new_stage
state.last_update = datetime.now().isoformat()
order_response = await handle_order_notification(self, message=message, state=state)
if isinstance(order_response, AgentResponse):
return order_response
customer_text, _ = self._split_customer_text(message.msg)
shop_type = _get_shop_type(message.acc_id or "", message.goods_name or "")
flow_response = await handle_find_image_batch_flow(
self,
message=message,
state=state,
customer_text=customer_text,
shop_type=shop_type,
)
if isinstance(flow_response, AgentResponse):
return flow_response
prompt_bundle = build_prompt_bundle(self, message=message, state=state)
user_prompt = prompt_bundle.user_prompt
deps = prompt_bundle.deps
history = prompt_bundle.history
self._log_block("PROMPT->AI 前置提示词", user_prompt)
try:
reply_text = await execute_ai_turn(
self,
message=message,
state=state,
user_prompt=user_prompt,
deps=deps,
history=history,
)
except Exception as e:
err_str = str(e)
print(f"[Agent] AI 调用失败: {e}，使用兜底回复")
self._activity_log("agent_ai_error", customer_id=message.from_id, acc_id=message.acc_id, error=err_str)
metrics_emit("ai_call_failed", customer_id=message.from_id, acc_id=message.acc_id)
if "AccountOverdueError" in err_str or "overdue" in err_str.lower():
asyncio.create_task(_notify_wechat_overdue())
else:
asyncio.create_task(_notify_wechat(
f"⚠️ **AI调用异常**\n"
f"客户：{message.from_id}\n"
f"店铺：{message.acc_id}\n"
f"错误：{err_str[:200]}",
tag="AI异常"
))
reply_text = None
else:
metrics_emit("ai_call_success", customer_id=message.from_id, acc_id=message.acc_id)
# AI 失败兜底：给一个不出错的万能回复
if not reply_text:
fallback_text = await self._rewrite_reply_with_ai(
message=message,
state=state,
reply="好嘞，你稍等下，我这边看一下",
scene="fallback_reply",
)
return AgentResponse(
reply=fallback_text,
should_reply=True,
need_transfer=False
)
return await finalize_ai_reply(
self,
message=message,
state=state,
reply_text=reply_text,
)
"""处理客户消息并生成回复。"""
return await process_incoming_message(self, message)
async def _check_order_amount(self, customer_id: str, order: dict, acc_id: str):
"""核查订单实付金额是否与报价一致，异常时企业微信预警"""
@@ -885,7 +796,7 @@ class CustomerServiceAgent:
f"差额：{quoted - paid:.1f}元 — 请人工核查"
)
print(f"[Agent] {msg}")
await _notify_wechat(msg)
await self._notify_wechat(msg)
except Exception as e:
print(f"[Agent] 订单金额核查失败: {e}")