refactor: split quote pipeline stages and add trust case-script guidance

core/pydantic_ai_agent.py

@@ -63,6 +63,7 @@ async def _notify_wechat_overdue():
 
 # ========== 转接常量 ==========
 TRANSFER_MESSAGE = "话术|[转移会话],分组20252916034,无原因"
+CASE_LIBRARY_LINK = "https://www.yuque.com/zuowei-dfvpq/kge0in/mynala0g35b8cec5"
 
 
 # ========== 数据模型 ==========
@@ -1092,7 +1093,12 @@ class CustomerServiceAgent:
 规则：
 - 收到图片或历史有图片依据时尽量结合复杂度给出单价，价格为5的整数倍
 - 没有图片时引导发图，不给价格区间
-- 报价后紧跟一句推动成交，话术自然不重复
+- 报价后紧跟一句推动成交，话术自然不重复，避免机械重复“最低了”
+- 客户说“有点贵/优惠点/两张优惠点”时，优先给打包价或数量优惠，不要只会拒绝
+- 客户说“不放心/先看效果”时，先建立信任：可发案例链接 {CASE_LIBRARY_LINK}，并说明不满意可退
+- 可直接复用这条信任话术（按需微调，不要每次完全一样）：
+  小妹整理了一些案例图，亲点这个链接就能看到啦（{CASE_LIBRARY_LINK}）。
+  有什么想要的效果随时告诉我哈，我这边都可以按您的要求来做哦~/:065 效果不好不满意，我们这边包退的哦。
 - 最低价不低于{floor}元，客户出价低于底线时礼貌拒绝（不好意思）
 - 输出不超过2句话"""
 
@@ -2125,18 +2131,11 @@ class CustomerServiceAgent:
         lines.append("你定一个方案，我这边马上安排。")
         return "\n".join(lines)
 
-    async def _quote_pending_images(self, state: ConversationState, message: CustomerMessage) -> Dict[str, Any]:
+    def _prepare_batch_intake(self, state: ConversationState) -> Dict[str, Any]:
-        """
+        """Stage 1: 收集阶段，标准化输入并做上限约束。"""
-        批量识别待处理图片并统一处理：
-        - find_image 意图且可自动处理：直接 Gemini 处理 + 上传图绘 + 回链接
-        - 高风险/不可做：转人工
-        - 其他：统一报价
-        """
-        from image.image_analyzer import image_analyzer
-
         urls = list(state.pending_image_urls)
         if not urls:
-            return {"reply": "你先把图片发我，我看完再给你统一报价。", "need_transfer": False}
+            return {"ok": False, "reply": "你先把图片发我，我看完再给你统一报价。", "need_transfer": False}
         try:
             from config.config import BATCH_MAX_IMAGES, BATCH_ANALYZE_CONCURRENCY
             max_images = max(1, int(BATCH_MAX_IMAGES))
@@ -2146,12 +2145,23 @@ class CustomerServiceAgent:
             analyze_concurrency = 3
         if len(urls) > max_images:
             return {
+                "ok": False,
                 "reply": f"这次图片有点多（{len(urls)}张），我先按前{max_images}张处理报价，剩下的下一批继续发我。",
                 "need_transfer": False,
             }
-        urls = urls[:max_images]
+        return {
+            "ok": True,
+            "urls": urls[:max_images],
+            "requirements": list(state.pending_requirements or []),
+            "analyze_concurrency": analyze_concurrency,
+        }
+
+    async def _run_batch_feasibility(self, urls: List[str], concurrency: int) -> List[Tuple[str, Dict[str, Any]]]:
+        """Stage 2: 可做性分析（逐图）。"""
+        from image.image_analyzer import image_analyzer
+
+        sem = asyncio.Semaphore(max(1, concurrency))
 
-        sem = asyncio.Semaphore(analyze_concurrency)
         async def _analyze_one(url: str):
             async with sem:
                 try:
@@ -2167,9 +2177,10 @@ class CustomerServiceAgent:
                     }
                 return url, r
 
-        results = list(await asyncio.gather(*[_analyze_one(u) for u in urls]))
+        return list(await asyncio.gather(*[_analyze_one(u) for u in urls]))
+
+    async def _sync_batch_analysis_to_workflow(self, results: List[Tuple[str, Dict[str, Any]]], message: CustomerMessage) -> None:
         for url, r in results:
-            # 与单图流程一致：识别后写入 workflow 任务
             try:
                 from core.workflow import workflow
                 await workflow.image_analysis_result(
@@ -2188,12 +2199,26 @@ class CustomerServiceAgent:
             except Exception as e:
                 print(f"[Agent] Workflow 批量任务创建失败: {e}")
 
-        total_min = sum(int(r.get("price_min", 15) or 15) for _, r in results)
+    def _assess_batch_risk(self, results: List[Tuple[str, Dict[str, Any]]]) -> Dict[str, List[str]]:
-        total_max = sum(int(r.get("price_max", 25) or 25) for _, r in results)
+        """Stage 2.5: 分离可做和风险图。"""
-        total_suggest = sum(int(r.get("price_suggest", 20) or 20) for _, r in results)
+        unsafe: List[str] = []
-        req_fee = self._calc_requirement_surcharge(state.pending_requirements)
+        dense_text_reject: List[str] = []
+        for i, (_, r) in enumerate(results, 1):
+            if r.get("feasibility") == "no" or r.get("risk") == "high":
+                unsafe.append(f"图{i}")
+                note = str(r.get("note", "") or "")
+                if "文字内容过于密集" in note or "密集文字" in note:
+                    dense_text_reject.append(f"图{i}")
+        return {"unsafe": unsafe, "dense_text_reject": dense_text_reject}
 
-        # 打包优惠：2 张减 5，3 张及以上按 9 折（四舍五入到 5 元）
+    def _build_batch_pricing_plan(
+        self,
+        results: List[Tuple[str, Dict[str, Any]]],
+        requirements: List[str],
+    ) -> Dict[str, Any]:
+        """Stage 3: 报价计算（图片成本 + 需求加价 + 打包价）。"""
+        total_suggest = sum(int(r.get("price_suggest", 20) or 20) for _, r in results)
+        req_fee = self._calc_requirement_surcharge(requirements)
         if len(results) == 2:
             bundle_price = max(10, total_suggest - 5)
         elif len(results) >= 3:
@@ -2202,97 +2227,130 @@ class CustomerServiceAgent:
             bundle_price = total_suggest
         bundle_price += int(req_fee.get("extra", 0) or 0)
         bundle_price = round(bundle_price / 5) * 5
+        return {
+            "total_suggest": total_suggest,
+            "req_fee": req_fee,
+            "bundle_price": bundle_price,
+        }
 
-        # 先分流：高风险/不可做 -> 转人工
+    async def _try_batch_auto_process(
-        unsafe = []
+        self,
-        dense_text_reject = []
+        results: List[Tuple[str, Dict[str, Any]]],
-        for i, (_, r) in enumerate(results, 1):
+        message: CustomerMessage,
-            if r.get("feasibility") == "no" or r.get("risk") == "high":
+        req_fee: Dict[str, Any],
-                unsafe.append(f"图{i}")
+    ) -> Dict[str, Any]:
-                note = str(r.get("note", "") or "")
+        """Stage 4-A: 自动处理+图绘链接。失败时回退到需求澄清。"""
-                if "文字内容过于密集" in note or "密集文字" in note:
+        links = []
-                    dense_text_reject.append(f"图{i}")
+        try:
+            from image.image_processor import image_processor
+            from utils.image_queue import run_with_queue
+            for idx, (url, r) in enumerate(results, 1):
+                req_parts = [f"complexity:{r.get('complexity', 'normal')}"]
+                if r.get("gemini_prompt"):
+                    req_parts.append(f"prompt:{r.get('gemini_prompt')}")
+                if r.get("aspect_ratio"):
+                    req_parts.append(f"ratio:{r.get('aspect_ratio')}")
+                if r.get("perspective") and r.get("perspective") != "no":
+                    req_parts.append(f"perspective:{r.get('perspective')}")
+                if r.get("proc_type"):
+                    req_parts.append(f"proc_type:{r.get('proc_type')}")
+                if r.get("subject"):
+                    req_parts.append(f"subject:{r.get('subject')}")
+                if r.get("quality"):
+                    req_parts.append(f"quality:{r.get('quality')}")
+
+                process_res = await run_with_queue(image_processor.process_image(
+                    url,
+                    "enhance",
+                    requirements="|".join(req_parts),
+                    gemini_prompt=r.get("gemini_prompt", ""),
+                    aspect_ratio=r.get("aspect_ratio", "1:1"),
+                    perspective=r.get("perspective", "no"),
+                    proc_type=r.get("proc_type", ""),
+                    subject=r.get("subject", ""),
+                    quality=r.get("quality", ""),
+                ))
+                if not process_res.get("success"):
+                    raise RuntimeError(process_res.get("message", "图片处理失败"))
+
+                ok, link, _ = await upload_to_tuhui(
+                    process_res["result_path"],
+                    title=f"客户{message.from_id[-4:]}-图片{idx}",
+                    description="AI自动处理结果",
+                    price=max(10, int(r.get("price_suggest", 20) or 20) + int(req_fee.get("extra", 0) or 0) // max(1, len(results))),
+                )
+                if not ok:
+                    raise RuntimeError(str(link))
+                links.append(link)
+        except Exception as e:
+            print(f"[Agent] 找图自动处理失败，回退需求澄清: {e}")
+            return {
+                "reply": "这种可以做类似款。你先说下具体需求：要几张、是否改字、尺寸比例、交付格式（单图/打包链接），我按需求给你直接做。",
+                "need_transfer": False,
+            }
+        lines = ["找到了，链接如下："]
+        for i, link in enumerate(links, 1):
+            lines.append(f"链接{i}：{link}")
+        return {"reply": "\n".join(lines), "need_transfer": False}
+
+    def _finalize_batch_state(self, state: ConversationState, customer_id: str, final_price: int = 0):
+        if final_price > 0:
+            state.last_price = final_price
+            try:
+                from db.customer_db import db
+                db.update_last_price(customer_id, final_price)
+            except Exception:
+                pass
+        state.pending_image_urls.clear()
+        state.pending_requirements.clear()
+        self._sync_pending_quote_state(customer_id, state)
+
+    async def _quote_pending_images(self, state: ConversationState, message: CustomerMessage) -> Dict[str, Any]:
+        """
+        统一报价主流程（分层）：
+        1) Intake 收集
+        2) Feasibility 可做性
+        3) Pricing 报价
+        4) Router 自动处理/报价/转人工
+        """
+        intake = self._prepare_batch_intake(state)
+        if not intake.get("ok", False):
+            return {"reply": intake.get("reply", ""), "need_transfer": bool(intake.get("need_transfer", False))}
+
+        urls = intake["urls"]
+        requirements = intake["requirements"]
+        analyze_concurrency = int(intake["analyze_concurrency"])
+
+        results = await self._run_batch_feasibility(urls=urls, concurrency=analyze_concurrency)
+        await self._sync_batch_analysis_to_workflow(results=results, message=message)
+
+        risk = self._assess_batch_risk(results)
+        unsafe = risk["unsafe"]
+        dense_text_reject = risk["dense_text_reject"]
         if unsafe:
-            state.pending_image_urls.clear()
+            self._finalize_batch_state(state, message.from_id, final_price=0)
-            state.pending_requirements.clear()
-            self._sync_pending_quote_state(message.from_id, state)
             if dense_text_reject and len(dense_text_reject) == len(unsafe):
-                return {
+                return {"reply": self._build_reject_message("文字密集类图片暂不接单"), "need_transfer": False}
-                    "reply": self._build_reject_message("文字密集类图片暂不接单"),
-                    "need_transfer": False,
-                }
             return {
                 "reply": f"这批里{'、'.join(unsafe)}处理风险较高，我先帮你转人工设计师跟进会更稳妥。",
                 "need_transfer": True,
             }
 
-        # 查找图片意图：优先直接自动处理并回图绘链接
+        pricing = self._build_batch_pricing_plan(results=results, requirements=requirements)
-        intent_text = (message.msg or "") + " " + " ".join(state.pending_requirements[-5:])
+        total_suggest = int(pricing["total_suggest"])
+        bundle_price = int(pricing["bundle_price"])
+        req_fee = pricing["req_fee"]
+
+        intent_text = (message.msg or "") + " " + " ".join(requirements[-5:])
         workflow_type, _ = self.workflow_router.detect_workflow(intent_text)
         if workflow_type == "find_image":
-            links = []
+            route_res = await self._try_batch_auto_process(
-            try:
+                results=results,
-                from image.image_processor import image_processor
+                message=message,
-                from utils.image_queue import run_with_queue
+                req_fee=req_fee,
-                for idx, (url, r) in enumerate(results, 1):
+            )
-                    req_parts = [f"complexity:{r.get('complexity', 'normal')}"]
+            self._finalize_batch_state(state, message.from_id, final_price=bundle_price)
-                    if r.get("gemini_prompt"):
+            return route_res
-                        req_parts.append(f"prompt:{r.get('gemini_prompt')}")
-                    if r.get("aspect_ratio"):
-                        req_parts.append(f"ratio:{r.get('aspect_ratio')}")
-                    if r.get("perspective") and r.get("perspective") != "no":
-                        req_parts.append(f"perspective:{r.get('perspective')}")
-                    if r.get("proc_type"):
-                        req_parts.append(f"proc_type:{r.get('proc_type')}")
-                    if r.get("subject"):
-                        req_parts.append(f"subject:{r.get('subject')}")
-                    if r.get("quality"):
-                        req_parts.append(f"quality:{r.get('quality')}")
-
-                    process_res = await run_with_queue(image_processor.process_image(
-                        url,
-                        "enhance",
-                        requirements="|".join(req_parts),
-                        gemini_prompt=r.get("gemini_prompt", ""),
-                        aspect_ratio=r.get("aspect_ratio", "1:1"),
-                        perspective=r.get("perspective", "no"),
-                        proc_type=r.get("proc_type", ""),
-                        subject=r.get("subject", ""),
-                        quality=r.get("quality", ""),
-                    ))
-                    if not process_res.get("success"):
-                        raise RuntimeError(process_res.get("message", "图片处理失败"))
-
-                    ok, link, _ = await upload_to_tuhui(
-                        process_res["result_path"],
-                        title=f"客户{message.from_id[-4:]}-图片{idx}",
-                        description="AI自动处理结果",
-                        price=max(10, int(r.get("price_suggest", 20) or 20) + int(req_fee.get("extra", 0) or 0) // max(1, len(results))),
-                    )
-                    if not ok:
-                        raise RuntimeError(str(link))
-                    links.append(link)
-            except Exception as e:
-                # 找图分支失败时，不直接报价，先回到“收集需求再做”
-                print(f"[Agent] 找图自动处理失败，回退需求澄清: {e}")
-                return {
-                    "reply": "这种可以做类似款。你先说下具体需求：要几张、是否改字、尺寸比例、交付格式（单图/打包链接），我按需求给你直接做。",
-                    "need_transfer": False,
-                }
-            else:
-                lines = [f"找到了，链接如下："]
-                for i, link in enumerate(links, 1):
-                    lines.append(f"链接{i}：{link}")
-                state.last_price = bundle_price
-                try:
-                    from db.customer_db import db
-                    db.update_last_price(message.from_id, bundle_price)
-                except Exception:
-                    pass
-                state.pending_image_urls.clear()
-                state.pending_requirements.clear()
-                self._sync_pending_quote_state(message.from_id, state)
-                return {"reply": "\n".join(lines), "need_transfer": False}
 
         reply_text = self._build_batch_quote_reply(
             results=results,
@@ -2300,18 +2358,7 @@ class CustomerServiceAgent:
             bundle_price=bundle_price,
             req_fee=req_fee,
         )
-
+        self._finalize_batch_state(state, message.from_id, final_price=bundle_price)
-        state.last_price = bundle_price
-        try:
-            from db.customer_db import db
-            db.update_last_price(message.from_id, bundle_price)
-        except Exception:
-            pass
-
-        # 清空待报价队列（本轮已统一报价）
-        state.pending_image_urls.clear()
-        state.pending_requirements.clear()
-        self._sync_pending_quote_state(message.from_id, state)
         return {"reply": reply_text, "need_transfer": False}
 
     def _split_customer_text(self, msg: str) -> tuple: