refactor: extract prompt bundle builder from agent

core/prompt_flow.py

@@ -0,0 +1,50 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, List
+
+if TYPE_CHECKING:
+    from core.pydantic_ai_agent import AgentDeps, ConversationState, CustomerMessage, CustomerServiceAgent
+
+
+@dataclass
+class PromptBundle:
+    user_prompt: str
+    deps: "AgentDeps"
+    history: List
+
+
+def build_prompt_bundle(
+    agent: "CustomerServiceAgent",
+    *,
+    message: "CustomerMessage",
+    state: "ConversationState",
+) -> PromptBundle:
+    from core.pydantic_ai_agent import AgentDeps
+
+    user_prompt = agent._build_prompt(message, state)
+
+    profile_context = agent._get_customer_profile_context(message.from_id)
+    if profile_context:
+        user_prompt = profile_context + "\n\n" + user_prompt
+
+    refusal_hint = agent._get_refusal_context_hint(message.from_id, message.msg, profile_context or "")
+    if refusal_hint:
+        user_prompt = refusal_hint + "\n\n" + user_prompt
+
+    conv_context = agent._get_conversation_context(message.from_id, acc_id=message.acc_id or "")
+    if conv_context:
+        user_prompt = conv_context + user_prompt
+
+    intent_hint = agent._get_intent_emotion_hint(message.msg)
+    if intent_hint:
+        user_prompt = intent_hint + "\n\n" + user_prompt
+
+    deps = AgentDeps(
+        msg_id=message.msg_id,
+        acc_id=message.acc_id,
+        from_id=message.from_id,
+        platform=message.acc_type,
+    )
+    history = agent.message_histories.get(message.from_id, [])
+    return PromptBundle(user_prompt=user_prompt, deps=deps, history=history)

core/pydantic_ai_agent.py

@@ -30,6 +30,7 @@ from core.find_image_flow import handle_find_image_batch_flow
 from core.order_flow import handle_order_notification
 from core.ai_reply_flow import execute_ai_turn
 from core.reply_finalize_flow import finalize_ai_reply
+from core.prompt_flow import build_prompt_bundle
 
 load_dotenv()
 
@@ -1809,38 +1810,10 @@ class CustomerServiceAgent:
         if isinstance(flow_response, AgentResponse):
             return flow_response
 
-        # 构建提示词（包含对话状态 + 客户画像）
-        user_prompt = self._build_prompt(message, state)
-
-        # 注入客户历史画像（个性化语气、报价策略、主动预测）
-        profile_context = self._get_customer_profile_context(message.from_id)
-        if profile_context:
-            user_prompt = profile_context + "\n\n" + user_prompt
-
-        # 前后一致提示：刚拒绝某张图后，客户问「能找到吗」等时，必须区分能做/不能做
-        refusal_hint = self._get_refusal_context_hint(message.from_id, message.msg, profile_context or "")
-        if refusal_hint:
-            user_prompt = refusal_hint + "\n\n" + user_prompt
-
-        # 每一次对话都注入近期上下文，确保模型看到完整对话
-        conv_context = self._get_conversation_context(message.from_id, acc_id=message.acc_id or "")
-        if conv_context:
-            user_prompt = conv_context + user_prompt
-
-        # 语义匹配：意图/情绪识别（配置 EMBEDDING_MODEL 时用 embedding，否则关键词）
-        intent_hint = self._get_intent_emotion_hint(message.msg)
-        if intent_hint:
-            user_prompt = intent_hint + "\n\n" + user_prompt
-
-        deps = AgentDeps(
-            msg_id=message.msg_id,
-            acc_id=message.acc_id,
-            from_id=message.from_id,
-            platform=message.acc_type
-        )
-
-        # 取出该客户的历史对话，传给 AI 保持上下文
-        history = self.message_histories.get(message.from_id, [])
+        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)