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