diff --git a/.env b/.env index f31bf6c..19a074f 100755 --- a/.env +++ b/.env @@ -1,8 +1,8 @@ # 火山引擎 Ark API OPENAI_API_KEY=cb2360ff-1a52-4289-abd6-ec29118376d0 OPENAI_BASE_URL=https://ark.cn-beijing.volces.com/api/v3 -OPENAI_MODEL=doubao-seed-2-0-mini-260215 -VISION_MODEL=doubao-seed-2-0-mini-260215 +OPENAI_MODEL=doubao-seed-2-0-lite-260215 +VISION_MODEL=doubao-seed-2-0-lite-260215 GEMINI_IMAGE_MODEL=gemini-3.1-flash-image-preview GEMINI_IMAGE_FALLBACK_MODEL=gemini-2.5-flash-image GEMINI_IMAGE_SIZE=1K @@ -22,8 +22,17 @@ SMTP_PASSWORD=bnnppvaweytkcadc SENDER_NAME=修图客服 EMAIL_POLL_INTERVAL=30 -# 企业微信群机器人 Webhook（日报推送） -WECHAT_WEBHOOK=https://qyapi.weixin.qq.com/cgi-bin/webhook/send?key=cc88bdef-a13f-4d7e-bdb6-ee51b68b8205 +# 企业微信群机器人 Webhook（聊天记录推送专用） +WECHAT_WEBHOOK=https://qyapi.weixin.qq.com/cgi-bin/webhook/send?key=b16b26a9-c8a7-402e-9bc9-8b5c0a83ebb4 + +# 收图阶段话术：true=优先AI生成（模板仅兜底） +AI_DYNAMIC_COLLECTION_REPLIES=true +# 所有最终回复都再经过AI润色（转人工指令除外） +AI_REWRITE_ALL_REPLIES=true +# 图片收齐后延后几轮消息再报价（1=先承接一轮，下一句再报价） +BATCH_QUOTE_DELAY_TURNS=1 +# AI 客服人设（可随时改） +AI_REPLY_PERSONA=淘宝老店主，说话直接、有耐心、像真人微信聊天，不端着 # 每日日报接收邮箱（留空则不发邮件） SUMMARY_EMAIL= @@ -57,6 +66,6 @@ TUHUI_DEFAULT_PRICE=20 DB_TYPE=mysql MYSQL_HOST=1.12.50.92 MYSQL_PORT=3306 -MYSQL_USER=root +MYSQL_USER=ai_cs_user MYSQL_PASSWORD=Zuowei1216 MYSQL_DATABASE=ai_cs diff --git a/core/pydantic_ai_agent.py b/core/pydantic_ai_agent.py index d42a772..7f93421 100755 --- a/core/pydantic_ai_agent.py +++ b/core/pydantic_ai_agent.py @@ -29,7 +29,7 @@ from core.workflow_router import get_workflow_router from core.workflow_router import get_workflow_router # ========== 企业微信通知 ========== -_WECHAT_WEBHOOK = "https://qyapi.weixin.qq.com/cgi-bin/webhook/send?key=cc88bdef-a13f-4d7e-bdb6-ee51b68b8205" +_WECHAT_WEBHOOK = os.getenv("WECHAT_WEBHOOK", "") async def _notify_wechat(content: str, tag: str = "通知"): @@ -96,6 +96,8 @@ class ConversationState(BaseModel): image_count: int = 0 # 图片数量 pending_image_urls: List[str] = Field(default_factory=list) # 待统一报价图片 pending_requirements: List[str] = Field(default_factory=list) # 待统一报价需求 + quote_phase: str = "idle" # idle/collecting/ready_to_quote/waiting_result + quote_ready_turns: int = 0 # ready_to_quote 阶段还需等待的消息轮次 last_update: str = "" last_reply_at: Optional[datetime] = None # 最后一次回复客户的时间 @@ -142,19 +144,23 @@ def _get_shop_type(acc_id: str = "", goods_name: str = "") -> str: return "find_image" -def load_skill_md(skills_dir: str = "skills") -> str: - """加载 skills 目录下的所有 SKILL.md 文件内容""" - skill_contents = [] +def load_skill_map(skills_dir: str = "skills") -> Dict[str, str]: + """按技能目录名加载 SKILL.md，返回 {skill_name: content}。""" + skill_map: Dict[str, str] = {} skill_files = glob.glob(os.path.join(skills_dir, "**/SKILL.md"), recursive=True) for skill_file in skill_files: try: - with open(skill_file, 'r', encoding='utf-8') as f: - content = f.read() - skill_contents.append(content) + content = Path(skill_file).read_text(encoding="utf-8") + skill_name = Path(skill_file).parent.name.strip().lower() + if not skill_name: + continue + if skill_name in skill_map: + skill_map[skill_name] += "\n\n" + content + else: + skill_map[skill_name] = content except Exception as e: print(f"警告: 读取 {skill_file} 失败: {e}") - - return "\n\n".join(skill_contents) + return skill_map class CustomerServiceAgent: @@ -171,6 +177,13 @@ class CustomerServiceAgent: self.api_key = os.getenv("OPENAI_API_KEY") self.base_url = os.getenv("OPENAI_BASE_URL", "https://api.openai.com/v1") self.model_name = os.getenv("OPENAI_MODEL", "gpt-4o-mini") + self.reply_persona = os.getenv("AI_REPLY_PERSONA", "淘宝老店主，直爽利落，口语自然") + self.dynamic_collection_replies = os.getenv("AI_DYNAMIC_COLLECTION_REPLIES", "true").strip().lower() in {"1", "true", "yes", "on"} + self.rewrite_all_replies = os.getenv("AI_REWRITE_ALL_REPLIES", "true").strip().lower() in {"1", "true", "yes", "on"} + try: + self.batch_quote_delay_turns = max(0, int(os.getenv("BATCH_QUOTE_DELAY_TURNS", "1"))) + except Exception: + self.batch_quote_delay_turns = 1 if not self.api_key: raise ValueError("请设置 OPENAI_API_KEY 环境变量") @@ -181,8 +194,13 @@ class CustomerServiceAgent: self.message_histories: Dict[str, list] = {} self.evolution_candidate = self._load_evolution_candidate() - # 加载 skills 内容 - self.skills_content = load_skill_md(skills_dir) + # 加载技能并按角色拆分，避免所有 Agent 吃同一份大杂烩提示词 + self.skill_map = load_skill_map(skills_dir) + self.skill_style = self._compose_skill_content(["style-skill", "owner-style"]) + self.skill_pre_sales = self._compose_skill_content(["pre-sales-skill"]) + self.skill_pricing = self._compose_skill_content(["pricing-skill"]) + self.skill_after_sale = self._compose_skill_content(["after-sales-skill"]) + self.skill_risk = self._compose_skill_content(["risk-skill"]) # 创建 OpenAI 模型 model = OpenAIChatModel( @@ -218,6 +236,11 @@ class CustomerServiceAgent: deps_type=AgentDeps, system_prompt=self._get_similar_prompt() ) + self.agent_natural_reply = Agent( + model=model, + deps_type=AgentDeps, + system_prompt=self._get_natural_reply_prompt() + ) # 工作流程路由器 self.workflow_router = get_workflow_router() @@ -235,6 +258,22 @@ class CustomerServiceAgent: # 注册工具 self._register_tools() + def _compose_skill_content(self, names: List[str]) -> str: + """按技能名拼接技能文本，找不到则跳过。""" + parts: List[str] = [] + for name in names: + key = (name or "").strip().lower() + if key and key in self.skill_map: + parts.append(self.skill_map[key]) + return "\n\n".join(parts) + + @staticmethod + def _attach_skill_docs(prompt: str, *skill_docs: str) -> str: + docs = [d for d in skill_docs if d] + if not docs: + return prompt + return prompt + "\n\n=== 角色技能 ===\n" + "\n\n".join(docs) + def _load_evolution_candidate(self) -> Dict[str, Any]: """读取自我进化候选配置（灰度策略），读取失败时返回空。""" try: @@ -331,6 +370,96 @@ class CustomerServiceAgent: t = t.replace(k, v) return t + async def _render_collection_reply_with_ai( + self, + *, + message: CustomerMessage, + state: ConversationState, + scene: str, + intent_hint: str, + fallback: str, + ) -> str: + """ + 收图阶段回复默认走 AI 改写，失败时回退到固定模板。 + """ + if not self.dynamic_collection_replies: + return fallback + + async def _rewrite_reply_with_ai( + self, + *, + message: CustomerMessage, + state: ConversationState, + reply: str, + scene: str = "final_reply", + ) -> str: + """ + 对最终回复做 AI 润色，统一口吻。失败时返回原文。 + """ + text = (reply or "").strip() + if not text or not self.rewrite_all_replies: + return text + transfer_keywords = ("TRANSFER_REQUESTED", "[转移会话]", "转移会话") + if any(k in text for k in transfer_keywords): + return text + try: + deps = AgentDeps( + msg_id=message.msg_id, + acc_id=message.acc_id, + from_id=message.from_id, + platform=message.acc_type, + ) + history = self.message_histories.get(message.from_id, []) + pending_req = "；".join((state.pending_requirements or [])[-4:]) or "无" + prompt = ( + "请把下面这句客服回复润色成更自然的微信聊天口吻，语义必须保持一致。\n" + f"场景: {scene}\n" + f"客户原话: {message.msg}\n" + f"当前已收图: {len(state.pending_image_urls)}张\n" + f"当前需求摘要: {pending_req}\n" + f"原回复: {text}\n" + "要求: 不要新增承诺/价格/流程；不超过2句话；只输出润色后的最终回复。" + ) + result = await self.agent_natural_reply.run(prompt, deps=deps, message_history=history) + self.message_histories[message.from_id] = result.all_messages()[-30:] + polished = self._colloquialize_reply(self._normalize_reply_text(result.output)) + if not polished: + return text + if any(k in polished for k in transfer_keywords): + return text + return polished + except Exception: + return text + try: + deps = AgentDeps( + msg_id=message.msg_id, + acc_id=message.acc_id, + from_id=message.from_id, + platform=message.acc_type, + ) + history = self.message_histories.get(message.from_id, []) + pending_req = "；".join((state.pending_requirements or [])[-4:]) or "无" + user_prompt = ( + "请按下面意图生成给客户的自然回复。\n" + f"场景: {scene}\n" + f"回复意图: {intent_hint}\n" + f"客户原话: {message.msg}\n" + f"当前已收图片数: {len(state.pending_image_urls)}\n" + f"当前需求摘要: {pending_req}\n" + "输出要求: 不超过2句话，像真人店主聊天。" + ) + result = await self.agent_natural_reply.run(user_prompt, deps=deps, message_history=history) + self.message_histories[message.from_id] = result.all_messages()[-30:] + text = self._colloquialize_reply(self._normalize_reply_text(result.output)) + if not text: + return fallback + transfer_keywords = ("TRANSFER_REQUESTED", "[转移会话]", "转移会话") + if any(k in text for k in transfer_keywords): + return fallback + return text + except Exception: + return fallback + def _register_tools(self): """注册所有 Tool，让 Agent 可以主动调用""" @@ -951,6 +1080,7 @@ class CustomerServiceAgent: def _sync_pending_quote_state(self, customer_id: str, state: ConversationState): """把待报价队列同步到客户库，避免重启丢失。""" try: + self._refresh_quote_phase(state) from db.customer_db import db db.update_pending_quote_state( customer_id, @@ -968,9 +1098,49 @@ class CustomerServiceAgent: state.pending_image_urls = list(getattr(profile, "pending_quote_images", []) or []) state.pending_requirements = list(getattr(profile, "pending_quote_requirements", []) or []) state.image_count = len(state.pending_image_urls) + self._refresh_quote_phase(state) except Exception: pass + @staticmethod + def _refresh_quote_phase(state: ConversationState, phase_hint: str = ""): + """统一维护收图报价状态机。""" + if phase_hint in {"idle", "collecting", "ready_to_quote", "waiting_result"}: + state.quote_phase = phase_hint + if phase_hint == "idle": + state.quote_ready_turns = 0 + return + if not state.pending_image_urls: + state.quote_phase = "idle" + state.quote_ready_turns = 0 + return + if state.quote_phase in {"ready_to_quote", "waiting_result"}: + return + if state.pending_image_urls and state.pending_requirements: + state.quote_phase = "collecting" + return + state.quote_phase = "collecting" + + def _should_defer_batch_quote(self, state: ConversationState, mark_ready: bool = False) -> bool: + """ + 批量报价延后控制： + - 首次进入 ready_to_quote 时按配置等待 N 轮 + - 等待轮次归零后，本轮即可报价 + """ + if mark_ready and state.quote_phase != "ready_to_quote": + state.quote_phase = "ready_to_quote" + state.quote_ready_turns = max(0, int(self.batch_quote_delay_turns)) + if state.quote_phase == "ready_to_quote" and state.quote_ready_turns > 0: + state.quote_ready_turns -= 1 + return True + return False + + def _mark_quote_ready(self, state: ConversationState): + """仅标记 ready 状态，不消费等待轮次。""" + if state.quote_phase != "ready_to_quote": + state.quote_phase = "ready_to_quote" + state.quote_ready_turns = max(0, int(self.batch_quote_delay_turns)) + def _build_reject_message(self, reason: str = "") -> str: templates = [ "这类图文字内容太密了，我们这边不接这单哈，建议精简后再发我看看。", @@ -1088,14 +1258,22 @@ class CustomerServiceAgent: - 回复不超过2句话 - 绝对禁止输出任何内部独白或状态说明，包括但不限于："无需回复""已完成""已经完成""不需要回复""流程结束""操作完成""任务完成""记录完成""报价已记录"等 - 每次必须输出真实的、发给客户看的回复文字，哪怕只有一句话""" + base_prompt += f"\n\n【人设语气】\n- 人设：{self.reply_persona}\n- 语气像真人店主，不官腔，不机械，不背模板。" - if self.skills_content: - base_prompt += f"\n\n=== 技能文档 ===\n{self.skills_content}" + return self._attach_skill_docs(base_prompt, self.skill_pre_sales, self.skill_style) - return base_prompt + def _get_natural_reply_prompt(self) -> str: + base = f"""你是淘宝店主客服，专门把系统给你的“回复意图”改写成自然的一句话或两句话。 +人设：{self.reply_persona} +规则： +- 只输出发给客户的话，不要解释你的思考。 +- 口语化、简短、有温度，避免“这个需求我收到了”这类机械表达。 +- 不要编造价格、订单、进度；只按输入意图表达。 +- 默认不超过2句话。""" + return self._attach_skill_docs(base, self.skill_style) def _get_after_sale_prompt(self) -> str: - return """你是淘宝客服的售后助手，负责售后阶段的自然沟通与处理进度反馈。 + base = """你是淘宝客服的售后助手，负责售后阶段的自然沟通与处理进度反馈。 核心：简洁、自然、不解释技术细节、尽量不调用报价相关工具。 规则： - 已付款客户优先：确认安排、说明进度、承诺时间点 @@ -1103,6 +1281,7 @@ class CustomerServiceAgent: - 催进度：自然回复在做了/快了/马上好，给预计时间 - 投诉/情绪激动/退款：转人工 - 输出不超过2句话，不说内部状态""" + return self._attach_skill_docs(base, self.skill_after_sale, self.skill_style) def _get_pricing_prompt(self) -> str: try: @@ -1110,7 +1289,7 @@ class CustomerServiceAgent: floor = MIN_PRICE_FLOOR except Exception: floor = 15 - return f"""你是淘宝客服的报价助手，负责在客户明确提到价格/询价时快速给出自然报价并推动成交。 + base = f"""你是淘宝客服的报价助手，负责在客户明确提到价格/询价时快速给出自然报价并推动成交。 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 @@ -1122,33 +1301,38 @@ class CustomerServiceAgent: 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""" + return self._attach_skill_docs(base, self.skill_pricing, self.skill_style) def _get_processing_prompt(self) -> str: - return """你是淘宝客服的处理助手，负责在客户说安排/处理/开始做或已付款的场景下进行处理安排与进度反馈。 + base = """你是淘宝客服的处理助手，负责在客户说安排/处理/开始做或已付款的场景下进行处理安排与进度反馈。 规则： - 已付款或明确要求开始时，确认安排并给预计时间点 - 可调用处理流程工具 - 投诉/退款时转人工 - 输出不超过2句话""" + return self._attach_skill_docs(base, self.skill_after_sale, self.skill_style) def _get_similar_prompt(self) -> str: - return """你是淘宝客服的相似图助手，客户问“有一样的吗/类似的吗/同款吗”时，给出自然回复与参考建议。 + base = """你是淘宝客服的相似图助手，客户问“有一样的吗/类似的吗/同款吗”时，给出自然回复与参考建议。 规则： - 先确认可以找类似款，建议拍后我发参考图 - 如已知图案/类型，简要说明“同类型都有”，推动成交 - 输出不超过2句话""" + return self._attach_skill_docs(base, self.skill_pre_sales, self.skill_style) def _get_order_prompt(self) -> str: - return """你是淘宝客服的订单助手，负责系统订单通知的处理。 + base = """你是淘宝客服的订单助手，负责系统订单通知的处理。 规则： - 已付款时自然确认安排；其他状态静默（输出空字符串） - 输出不超过1句话""" + return self._attach_skill_docs(base, self.skill_after_sale, self.skill_style) def _get_risk_prompt(self) -> str: - return """你是淘宝客服的风控助手，负责敏感/违规内容的前置拦截与替代话术。 + base = """你是淘宝客服的风控助手，负责敏感/违规内容的前置拦截与替代话术。 规则： - 黄色/擦边/涉政/政治人物/政治事件/政治图片等不接单，礼貌拒绝 - 输出不超过1句话""" + return self._attach_skill_docs(base, self.skill_risk, self.skill_style) @staticmethod def _is_political_inquiry(text: str) -> bool: @@ -1435,7 +1619,12 @@ class CustomerServiceAgent: state.pending_image_urls.clear() state.pending_requirements.clear() self._sync_pending_quote_state(message.from_id, state) - reply = "这类不做哈，政治相关图片和人物都不接。" + reply = await self._rewrite_reply_with_ai( + message=message, + state=state, + reply="这类不做哈，政治相关图片和人物都不接。", + scene="risk_reject", + ) state.last_reply_at = datetime.now() print(f"{self.C_REPLY}[REPLY->CUSTOMER]{self.C_RESET} {reply}") return AgentResponse(reply=reply, should_reply=True, need_transfer=False) @@ -1444,7 +1633,12 @@ class CustomerServiceAgent: state.pending_image_urls.clear() state.pending_requirements.clear() self._sync_pending_quote_state(message.from_id, state) - reply = "这类不做哈，政治相关图片和人物都不接。" + reply = await self._rewrite_reply_with_ai( + message=message, + state=state, + reply="这类不做哈，政治相关图片和人物都不接。", + scene="risk_reject", + ) state.last_reply_at = datetime.now() print(f"{self.C_REPLY}[REPLY->CUSTOMER]{self.C_RESET} {reply}") return AgentResponse(reply=reply, should_reply=True, need_transfer=False) @@ -1498,14 +1692,19 @@ class CustomerServiceAgent: if shop_type == "find_image" and self._is_batch_quote_enabled(message.from_id, message.acc_id): incoming_urls = self._extract_image_urls(customer_text) text_without_urls = self._strip_urls_from_text(customer_text) + short_intent = self._classify_short_customer_text(text_without_urls) if incoming_urls: + is_related_followup = bool(text_without_urls and self._is_related_image_followup_intent(text_without_urls)) for u in incoming_urls: if u not in state.pending_image_urls: state.pending_image_urls.append(u) if text_without_urls: self._append_requirement(state, text_without_urls) + if is_related_followup: + self._append_requirement(state, "与上一张相关（截图/局部细节）") state.image_count = len(state.pending_image_urls) + self._refresh_quote_phase(state, "collecting") self._sync_pending_quote_state(message.from_id, state) if self._is_batch_finish_intent( @@ -1513,8 +1712,28 @@ class CustomerServiceAgent: state=state, has_incoming_urls=bool(incoming_urls), ): + should_defer = self._should_defer_batch_quote(state, mark_ready=True) + self._sync_pending_quote_state(message.from_id, state) + if should_defer: + defer_fallback = "图片和需求我都收齐了，我先整理下，马上给你报总价。" + defer_reply = await self._render_collection_reply_with_ai( + message=message, + state=state, + scene="quote_defer_notice", + intent_hint="确认已收齐图片与需求，先承接，告知稍后马上报价。", + fallback=defer_fallback, + ) + state.last_reply_at = datetime.now() + print(f"{self.C_REPLY}[REPLY->CUSTOMER]{self.C_RESET} {defer_reply}") + return AgentResponse(reply=defer_reply, should_reply=True, need_transfer=False) quote_res = await self._quote_pending_images(state, message) reply_text = self._colloquialize_reply(quote_res.get("reply", "")) + reply_text = await self._rewrite_reply_with_ai( + message=message, + state=state, + reply=reply_text, + scene="batch_quote_reply", + ) need_transfer = bool(quote_res.get("need_transfer")) state.last_reply_at = datetime.now() print(f"{self.C_REPLY}[REPLY->CUSTOMER]{self.C_RESET} {reply_text}") @@ -1525,25 +1744,96 @@ class CustomerServiceAgent: transfer_msg=TRANSFER_MESSAGE if need_transfer else "", ) - ack = self._colloquialize_reply(self._build_collect_ack(len(state.pending_image_urls))) + ack_fallback = "图片收到了，你有补充就继续发，我这边一起看。" + ack_intent = ( + "告知图片已收到；如果客户继续发图就继续收，发完可统一报价。" + if not is_related_followup + else "告知这是和上一张相关的截图/局部图，已按同一需求一起处理。" + ) + ack = await self._render_collection_reply_with_ai( + message=message, + state=state, + scene="collect_ack", + intent_hint=ack_intent, + fallback=ack_fallback, + ) state.last_reply_at = datetime.now() print(f"{self.C_REPLY}[REPLY->CUSTOMER]{self.C_RESET} {ack}") return AgentResponse(reply=ack, should_reply=True, need_transfer=False) if state.pending_image_urls: if text_without_urls: - self._append_requirement(state, text_without_urls) + # 短句先分类再路由，避免误追加为需求导致上下文漂移 + if short_intent == "finish_signal": + self._mark_quote_ready(state) + elif short_intent == "progress_query": + if state.quote_phase != "ready_to_quote": + self._refresh_quote_phase(state, "waiting_result") + elif short_intent == "ack": + if state.quote_phase != "ready_to_quote": + self._refresh_quote_phase(state, "collecting") + else: + self._append_requirement(state, text_without_urls) + self._refresh_quote_phase(state, "collecting") self._sync_pending_quote_state(message.from_id, state) # 客户明确“找图，不是做图”时，先澄清意图，不继续报价链路 if self._is_find_image_not_edit_conflict(text_without_urls): - clarify = self._build_find_image_clarify_reply(state) + clarify_fallback = "明白你是要找图，不是做图。你说下要找原图、同款还是高清版，我按这个给你找。" + clarify = await self._render_collection_reply_with_ai( + message=message, + state=state, + scene="find_not_edit_clarify", + intent_hint="确认客户要找图不是做图，并追问是找原图/同款/高清版。", + fallback=clarify_fallback, + ) state.last_reply_at = datetime.now() print(f"{self.C_REPLY}[REPLY->CUSTOMER]{self.C_RESET} {clarify}") return AgentResponse(reply=clarify, should_reply=True, need_transfer=False) + # 已到报价就绪阶段且等待轮次结束：对“有吗/进度”等追问直接报价 + if state.quote_phase == "ready_to_quote" and state.quote_ready_turns <= 0 and short_intent in {"progress_query", "ack", "finish_signal"}: + quote_res = await self._quote_pending_images(state, message) + reply_text = self._colloquialize_reply(quote_res.get("reply", "")) + reply_text = await self._rewrite_reply_with_ai( + message=message, + state=state, + reply=reply_text, + scene="batch_quote_reply", + ) + need_transfer = bool(quote_res.get("need_transfer")) + state.last_reply_at = datetime.now() + print(f"{self.C_REPLY}[REPLY->CUSTOMER]{self.C_RESET} {reply_text}") + return AgentResponse( + reply=reply_text, + should_reply=not need_transfer, + need_transfer=need_transfer, + transfer_msg=TRANSFER_MESSAGE if need_transfer else "", + ) + + # 客户在追问“找到了吗/没找到吗/多久好”时，优先给进度承接，不走“没听懂” + if short_intent == "progress_query" or self._is_result_followup_query(text_without_urls): + progress_fallback = "我这边在跟进了，一有结果马上发你。" + progress = await self._render_collection_reply_with_ai( + message=message, + state=state, + scene="collect_progress", + intent_hint="承接客户的进度/结果追问，简短说明正在跟进，有结果会第一时间回复。", + fallback=progress_fallback, + ) + state.last_reply_at = datetime.now() + print(f"{self.C_REPLY}[REPLY->CUSTOMER]{self.C_RESET} {progress}") + return AgentResponse(reply=progress, should_reply=True, need_transfer=False) + # 信息不足时先追问，避免误判为“直接报价” if self._needs_clarification_in_collecting(text_without_urls): - ask = self._build_not_understood_reply() + ask_fallback = "你再补一句具体要什么效果，我马上按你的要求来。" + ask = await self._render_collection_reply_with_ai( + message=message, + state=state, + scene="collect_clarify", + intent_hint="客户表达不清，礼貌请对方补充一句关键需求，不要机械，不要生硬。", + fallback=ask_fallback, + ) state.last_reply_at = datetime.now() print(f"{self.C_REPLY}[REPLY->CUSTOMER]{self.C_RESET} {ask}") return AgentResponse(reply=ask, should_reply=True, need_transfer=False) @@ -1552,8 +1842,28 @@ class CustomerServiceAgent: state=state, has_incoming_urls=False, ): + should_defer = self._should_defer_batch_quote(state, mark_ready=True) + self._sync_pending_quote_state(message.from_id, state) + if should_defer: + defer_fallback = "收到，我先把这批图过一遍，马上给你总价。" + defer_reply = await self._render_collection_reply_with_ai( + message=message, + state=state, + scene="quote_defer_notice", + intent_hint="确认已收齐，先承接并告知稍后马上报价。", + fallback=defer_fallback, + ) + state.last_reply_at = datetime.now() + print(f"{self.C_REPLY}[REPLY->CUSTOMER]{self.C_RESET} {defer_reply}") + return AgentResponse(reply=defer_reply, should_reply=True, need_transfer=False) quote_res = await self._quote_pending_images(state, message) reply_text = self._colloquialize_reply(quote_res.get("reply", "")) + reply_text = await self._rewrite_reply_with_ai( + message=message, + state=state, + reply=reply_text, + scene="batch_quote_reply", + ) need_transfer = bool(quote_res.get("need_transfer")) state.last_reply_at = datetime.now() print(f"{self.C_REPLY}[REPLY->CUSTOMER]{self.C_RESET} {reply_text}") @@ -1564,7 +1874,14 @@ class CustomerServiceAgent: transfer_msg=TRANSFER_MESSAGE if need_transfer else "", ) - remind = self._colloquialize_reply(self._build_collect_remind(len(state.pending_image_urls))) + remind_fallback = "需求我记上了，你继续发图，或者让我直接给你报价都行。" + remind = await self._render_collection_reply_with_ai( + message=message, + state=state, + scene="collect_remind", + intent_hint="确认需求已记录，引导客户继续补图或直接让你报价。", + fallback=remind_fallback, + ) state.last_reply_at = datetime.now() print(f"{self.C_REPLY}[REPLY->CUSTOMER]{self.C_RESET} {remind}") return AgentResponse(reply=remind, should_reply=True, need_transfer=False) @@ -1711,8 +2028,14 @@ class CustomerServiceAgent: # AI 失败兜底：给一个不出错的万能回复 if not reply_text: - return AgentResponse( + 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 ) @@ -1778,6 +2101,14 @@ class CustomerServiceAgent: if evo_hit and need_transfer and self._evolution_has_proposal("tone-empathy-pack"): should_reply = True + if should_reply: + reply_text = await self._rewrite_reply_with_ai( + message=message, + state=state, + reply=reply_text, + scene="final_reply", + ) + # 记录本次回复时间，供冷却期判断 if should_reply: state.last_reply_at = datetime.now() @@ -2108,9 +2439,13 @@ class CustomerServiceAgent: """客户是否表达“图发完了，可以统一报价”。""" if not text: return False + if self._classify_short_customer_text(text) == "finish_signal": + return True finish_keywords = [ "发完了", "都发完了", "发齐了", "齐了", "先这些", "就这些", "全部", "一起报", "统一报价", "总共多少钱", "一共多少钱", "打包价", "总价", "报价吧", "报个总价", "给个总价", + "没了", "没有了", "没图了", "就这", "就这张", "就这一张", "就这一个", "就一个", + "先报吧", "报下价", "报个价", "可以报价了", "能报吗", ] return any(k in text for k in finish_keywords) @@ -2185,7 +2520,84 @@ class CustomerServiceAgent: ) return any(k in s.lower() for k in pair_marks) and any(k in s for k in op_kw) - def _build_collect_ack(self, count: int) -> str: + @staticmethod + def _is_related_image_followup_intent(text: str) -> bool: + """ + 识别“新发的是上一张的截图/局部细节”的关联意图。 + 这类输入应与前图关联处理，避免当成完全独立需求。 + """ + s = (text or "").strip().lower() + if not s: + return False + relation_kw = ( + "截图", "截屏", "局部", "细节", "放大", "裁剪", "同一张", "同一幅", + "上一张", "上张", "前一张", "前面那张", "刚才那张", "这个是上面", + "这个是那张", "补一张细节", "补个截图", + ) + return any(k in s for k in relation_kw) + + @staticmethod + def _is_result_followup_query(text: str) -> bool: + """识别客户在找图流程中的结果/进度追问。""" + short_type = CustomerServiceAgent._classify_short_customer_text(text) + if short_type == "progress_query": + return True + s = (text or "").strip() + if not s: + return False + followup_kw = ( + "找到了吗", "没找到吗", "找到没", "找到没有", "找到了没", "有吗", "有没", "有没有", + "有结果吗", "结果呢", + "进度", "多久好", "什么时候好", "好了没", "弄好了吗", "做了没", + "你重新发", "重新发给我", "高清", "发我", + ) + if any(k in s for k in followup_kw): + return True + return s in {"?", "？", "在吗", "人呢"} + + @staticmethod + def _classify_short_customer_text(text: str) -> str: + """ + 短句分类器（状态机前置）： + - finish_signal: 发图完成，可报价 + - progress_query: 追问进度/结果 + - ack: 简短确认 + - unknown: 未识别 + """ + s = (text or "").strip() + if not s: + return "unknown" + if len(s) > 8: + return "unknown" + + finish_kw = ( + "没了", "没有了", "就这", "就这张", "就这一张", "就这一个", "就一个", + "先这些", "就这些", "发完了", "都发完了", + ) + if any(k in s for k in finish_kw): + return "finish_signal" + + progress_kw = ( + "有吗", "有没", "有没有", "找到了吗", "找到了没", "没找到吗", "找到没", "找到没有", + "进度", "结果", "多久好", "什么时候好", "好了没", "弄好了吗", "做了没", + "高清", "发我", "重新发", "你重新发给我", + ) + if any(k in s for k in progress_kw) or s in {"?", "？", "在吗", "人呢"}: + return "progress_query" + + ack_kw = ("嗯", "嗯嗯", "好", "好的", "行", "可以", "ok", "OK", "收到", "明白") + if s in ack_kw: + return "ack" + return "unknown" + + def _build_collect_ack(self, count: int, related_followup: bool = False) -> str: + if related_followup and count >= 2: + related_templates = [ + "这张我收到了，看起来是上一张的截图/细节图，我按同一单一起处理。还有补充就继续发。", + "收到，这张是关联补图我记上了（按同一需求处理）。你还有图就继续发。", + "明白，这张是前图的局部截图，我会和前面那张一起算，不会分开漏掉。", + ] + return random.choice(related_templates) if count <= 1: one_templates = [ "这张收到啦，还有图就继续发，我一起给你看。", @@ -2210,13 +2622,28 @@ class CustomerServiceAgent: ] return random.choice(templates).format(n=count) + def _build_collect_progress_reply(self, count: int) -> str: + if count <= 1: + templates = [ + "我这边在处理了，这张有结果我第一时间回你。", + "在跟进中，这张一有进展我马上发你。", + "这张我正在看，稍等我一会儿，结果出来就回你。", + ] + return random.choice(templates) + templates = [ + "我这边在按你这{n}张一起处理，有结果我立刻同步你。", + "正在跟进这{n}张，出结果我第一时间发你，不会漏。", + "进度在跑了（共{n}张），你稍等一下，我这边有结果马上回。", + ] + return random.choice(templates).format(n=count) + def _build_collect_remind(self, count: int) -> str: if count <= 1: one_templates = [ "这个要求我记住了。你还有图就继续发，不补图我就按这张给你报价。", "明白，这个需求我加上了。你继续发图也行，想直接报价也可以。", "我先记下这张。你如果是要我找图，不是做图，直接说一声，我按找图思路给你走。", - "这个需求我收到了。你要是就做这张，我现在就给你报。", + "收到，这张我先按你的要求记好了。就做这一张的话，我现在直接给你报实价。", "你这要求我记下了，后面还有图就发，没有的话我现在直接算价。", "行，我按你这个要求来。继续补图也行，不补我就先报这张。", "这个点我懂了，你还要补图就接着发，不补我立刻给你报价。", @@ -2254,7 +2681,14 @@ class CustomerServiceAgent: s = (text or "").strip() if not s: return False + short_non_vague_kw = ( + "？", "?", "没了", "没有了", "就这", "行", "好的", "ok", "报价", + "找到了吗", "没找到吗", "找到没", "找到了没", "有吗", "有没", "有没有", + "多久好", "什么时候好", "高清", + ) if len(s) <= 4: + if any(k in s for k in short_non_vague_kw): + return False return True vague_kw = ( "这个也是", "一共几个图", "几个图", "啥意思", "没明白", "什么意思", @@ -2585,6 +3019,7 @@ class CustomerServiceAgent: pass state.pending_image_urls.clear() state.pending_requirements.clear() + self._refresh_quote_phase(state, "idle") self._sync_pending_quote_state(customer_id, state) async def _quote_pending_images(self, state: ConversationState, message: CustomerMessage) -> Dict[str, Any]: diff --git a/skills/after-sales-skill/SKILL.md b/skills/after-sales-skill/SKILL.md new file mode 100644 index 0000000..d4141ab --- /dev/null +++ b/skills/after-sales-skill/SKILL.md @@ -0,0 +1,20 @@ +--- +name: after-sales-skill +description: 售后沟通技能，覆盖催进度、修改反馈、交付确认与情绪安抚。 +--- + +# 售后技能 + +## 目标 + +- 售后回复简洁稳定，先安抚再推进处理。 + +## 执行规则 + +- 催进度：直接给状态和下一步，不空话。 +- 修改需求：先确认收到，再问关键修改点。 +- 已付款客户：优先确认已安排。 +- 客户不满：先接情绪，再给处理动作。 +- 退款/投诉场景：按流程转人工。 +- 输出 1-2 句，避免解释技术细节。 + diff --git a/skills/customer-service/SKILL.md b/skills/customer-service/SKILL.md index 0504aab..8f69340 100755 --- a/skills/customer-service/SKILL.md +++ b/skills/customer-service/SKILL.md @@ -9,6 +9,30 @@ description: 找原图店客服 - 售前咨询、报价成交、售后处理 > **像真人聊天，不要套模板。** 每次说法自然变化，跟着客户语气走，禁止背台词、固定句式。 +## 0) 上下文优先（最高优先级） + +回复前先做 3 步： + +1. 先看最近 6-10 条对话，不要只看当前一句。 +2. 判断客户当前阶段：收图中 / 等结果 / 询价 / 砍价 / 售后。 +3. 再回复 1-2 句自然口语。 + +短句必须结合上下文，不要机械回“没听懂”。 + +### 短句意图映射（收图或待结果阶段） + +- 「有吗 / 有没 / 有没有 / 找到了吗 / 找到了没 / 没找到吗」=> 结果追问（承接进度） +- 「就这一个 / 没有了 / 先这些 / 就这些」=> 发图完成（进入报价） +- 「高清 / 重新发 / 发我 / 在吗 / ？」=> 跟进催办（先承接，不要反问） + +### 多图关联识别 + +客户发第二张图时，若有以下表达，默认按“同一需求补充图”处理，不要当新单： + +- 「上一张」「前面那张」「这个是截图」「局部」「细节」「放大图」「补个截图」 + +回复里要明确“已按同一需求一起处理”，避免断上下文。 + --- ## 【重要】业务背景 @@ -73,6 +97,10 @@ description: 找原图店客服 - 售前咨询、报价成交、售后处理 不解释，自然推下单发图、不满意退。说正面的。 +如果当前已经在收图/待结果阶段，像「有吗」「没找到吗」这类短句，优先按“催进度”承接： +- 可用话术方向：在跟进中 / 有进展马上发你 / 这边正在看 +- 不要回“你说具体点”“没听懂” + **⚠️ 前后一致**：若上一句刚说了「这类不做」「不接」某张图，客户接着问「能找到吗」「可以吗」→ 必须明确区分：能做的是哪张（如第一张），不能做的是哪张。不可只说「放心拍」「可以」，否则会让客户以为刚才拒绝的那张也能做，前后矛盾。 --- diff --git a/skills/owner-style/SKILL.md b/skills/owner-style/SKILL.md index 5ee922e..78efb72 100755 --- a/skills/owner-style/SKILL.md +++ b/skills/owner-style/SKILL.md @@ -28,6 +28,12 @@ description: 店主个人说话风格 - 开头不用每次都加"好的"，直接说正事也行 - 报价可以有多种说法：30块 / 30元 / 30 / 这张30 / 价格30，轮换着用 +### 上下文承接（必须） + +- 客户短句如「有吗」「没找到吗」「高清」「？」，先按进度追问承接，不要直接回“没听懂”。 +- 客户说「就这一个」「没有了」「先这些」，默认进入可报价状态。 +- 客户说「这是上一张截图/局部」，按同一需求补充处理，不当成新单。 + --- ## 报价风格 diff --git a/skills/pre-sales-skill/SKILL.md b/skills/pre-sales-skill/SKILL.md new file mode 100644 index 0000000..f374e08 --- /dev/null +++ b/skills/pre-sales-skill/SKILL.md @@ -0,0 +1,22 @@ +--- +name: pre-sales-skill +description: 售前接待与收图阶段技能，强调上下文承接、短句意图识别和多图关联理解。 +--- + +# 售前技能 + +## 目标 + +- 收图阶段回复自然，快速推进到“发完图 -> 报价”。 +- 短句不误判，优先承接上下文。 + +## 执行规则 + +- 先看最近对话再回复，不只看当前一句。 +- 收图阶段客户短句： + - 「有吗/有没/有没有/找到了吗/没找到吗」=> 按进度追问承接。 + - 「就这一个/没有了/先这些/就这些」=> 视为发图完成，进入报价。 + - 「高清/重新发/发我/？」=> 按跟进催办承接。 +- 若客户说「上一张/截图/局部/细节/补图」，按同一需求补充处理，不当新单。 +- 输出 1-2 句，口语化，不官腔。 + diff --git a/skills/pricing-skill/SKILL.md b/skills/pricing-skill/SKILL.md new file mode 100644 index 0000000..7a5d7c0 --- /dev/null +++ b/skills/pricing-skill/SKILL.md @@ -0,0 +1,20 @@ +--- +name: pricing-skill +description: 报价与成交推进技能，约束价格表达、打包优惠和压价应对。 +--- + +# 报价技能 + +## 目标 + +- 快速给明确价格并推动成交，不拖沓。 + +## 执行规则 + +- 价格用 5 的整数倍表达。 +- 没图不报价，只引导发图。 +- 报价后紧跟一句推进成交。 +- 多图优先给总价或打包价，不逐张拉扯。 +- 客户压价时先让一次，仍不成交再明确到底线。 +- 不给价格区间，不输出模糊话术。 + diff --git a/skills/risk-skill/SKILL.md b/skills/risk-skill/SKILL.md new file mode 100644 index 0000000..0eade2a --- /dev/null +++ b/skills/risk-skill/SKILL.md @@ -0,0 +1,18 @@ +--- +name: risk-skill +description: 风控拒绝技能，覆盖敏感内容拦截、拒绝边界和安全回复约束。 +--- + +# 风控技能 + +## 目标 + +- 对敏感内容快速、稳定、礼貌拒绝，避免前后矛盾。 + +## 执行规则 + +- 政治/色情/暴力/明显违规内容：直接拒绝，不报价。 +- 拒绝后若客户追问「能做吗/有吗」，保持一致，不反复改口。 +- 不输出技术解释，不展开争论。 +- 句子短、边界清晰、语气克制。 + diff --git a/skills/style-skill/SKILL.md b/skills/style-skill/SKILL.md new file mode 100644 index 0000000..cbbcd2b --- /dev/null +++ b/skills/style-skill/SKILL.md @@ -0,0 +1,19 @@ +--- +name: style-skill +description: 全局语气技能，统一店主口吻，减少模板腔与机械回复。 +--- + +# 风格技能 + +## 语气 + +- 像真人店主聊天，简短直接，不官腔。 +- 同意图回复换说法，避免连续复读。 +- 默认 1-2 句，不堆表情和感叹号。 + +## 约束 + +- 不说内部流程、系统状态、模型行为。 +- 不输出“未理解你的意思”这类机械句，优先结合上下文承接。 +- 不编造未确认的事实或承诺。 + diff --git a/tests/test_regression_pipeline.py b/tests/test_regression_pipeline.py index 449a1e7..8bee9c8 100644 --- a/tests/test_regression_pipeline.py +++ b/tests/test_regression_pipeline.py @@ -13,6 +13,9 @@ class RegressionPipelineTest(unittest.IsolatedAsyncioTestCase): os.environ["FEATURE_BATCH_QUOTE_ENABLED"] = "true" os.environ["FEATURE_BATCH_QUOTE_PERCENT"] = "100" os.environ["FEATURE_BATCH_QUOTE_SHOPS"] = "" + os.environ["AI_DYNAMIC_COLLECTION_REPLIES"] = "false" + os.environ["AI_REWRITE_ALL_REPLIES"] = "false" + os.environ["BATCH_QUOTE_DELAY_TURNS"] = "0" async def test_collect_images_then_ack(self): agent = CustomerServiceAgent() @@ -31,7 +34,7 @@ class RegressionPipelineTest(unittest.IsolatedAsyncioTestCase): ) resp = await agent.process_message(msg) self.assertTrue(resp.should_reply) - self.assertIn("张", resp.reply) + self.assertTrue(resp.reply.strip()) st = agent._get_conversation_state(self.customer_id) self.assertEqual(len(st.pending_image_urls), 2) @@ -113,6 +116,78 @@ class RegressionPipelineTest(unittest.IsolatedAsyncioTestCase): self.assertIn("45", resp.reply) agent._quote_pending_images.assert_awaited() + async def test_finish_signal_with_meile_triggers_quote(self): + agent = CustomerServiceAgent() + st = agent._get_conversation_state(self.customer_id) + st.pending_image_urls = ["https://img.alicdn.com/a.jpg"] + st.pending_requirements = [] + agent._sync_pending_quote_state(self.customer_id, st) + agent._quote_pending_images = AsyncMock(return_value={"reply": "这张15元，确认就开始", "need_transfer": False}) + + msg = CustomerMessage( + msg_id="m4b", + acc_id="test_shop", + msg="没有了，报下价", + from_id=self.customer_id, + from_name="t", + cy_id=self.customer_id, + acc_type="AliWorkbench", + msg_type=0, + cy_name="t", + goods_name="专业找图", + goods_order="", + ) + resp = await agent.process_message(msg) + self.assertTrue(resp.should_reply) + self.assertIn("15", resp.reply) + agent._quote_pending_images.assert_awaited() + + async def test_finish_signal_defers_quote_when_delay_enabled(self): + os.environ["BATCH_QUOTE_DELAY_TURNS"] = "1" + agent = CustomerServiceAgent() + st = agent._get_conversation_state(self.customer_id) + st.pending_image_urls = ["https://img.alicdn.com/a.jpg"] + st.pending_requirements = [] + agent._sync_pending_quote_state(self.customer_id, st) + agent._quote_pending_images = AsyncMock(return_value={"reply": "这张20元，确认就开做", "need_transfer": False}) + + msg1 = CustomerMessage( + msg_id="m4c-1", + acc_id="test_shop", + msg="没有了，报价吧", + from_id=self.customer_id, + from_name="t", + cy_id=self.customer_id, + acc_type="AliWorkbench", + msg_type=0, + cy_name="t", + goods_name="专业找图", + goods_order="", + ) + resp1 = await agent.process_message(msg1) + self.assertTrue(resp1.should_reply) + self.assertNotIn("20", resp1.reply) + agent._quote_pending_images.assert_not_awaited() + + msg2 = CustomerMessage( + msg_id="m4c-2", + acc_id="test_shop", + msg="有吗", + from_id=self.customer_id, + from_name="t", + cy_id=self.customer_id, + acc_type="AliWorkbench", + msg_type=0, + cy_name="t", + goods_name="专业找图", + goods_order="", + ) + resp2 = await agent.process_message(msg2) + self.assertTrue(resp2.should_reply) + self.assertIn("20", resp2.reply) + agent._quote_pending_images.assert_awaited() + os.environ["BATCH_QUOTE_DELAY_TURNS"] = "0" + async def test_cross_image_composite_intent_triggers_quote(self): agent = CustomerServiceAgent() st = agent._get_conversation_state(self.customer_id) @@ -139,6 +214,95 @@ class RegressionPipelineTest(unittest.IsolatedAsyncioTestCase): self.assertIn("50", resp.reply) agent._quote_pending_images.assert_awaited() + async def test_result_followup_query_uses_progress_reply(self): + agent = CustomerServiceAgent() + st = agent._get_conversation_state(self.customer_id) + st.pending_image_urls = ["https://img.alicdn.com/a.jpg"] + st.pending_requirements = [] + agent._sync_pending_quote_state(self.customer_id, st) + agent._quote_pending_images = AsyncMock(return_value={"reply": "不应触发报价", "need_transfer": False}) + + msg = CustomerMessage( + msg_id="m5b", + acc_id="test_shop", + msg="没找到吗", + from_id=self.customer_id, + from_name="t", + cy_id=self.customer_id, + acc_type="AliWorkbench", + msg_type=0, + cy_name="t", + goods_name="专业找图", + goods_order="", + ) + resp = await agent.process_message(msg) + self.assertTrue(resp.should_reply) + self.assertNotIn("不太懂你的意思", resp.reply) + self.assertNotIn("没完全理解", resp.reply) + self.assertNotIn("没听明白", resp.reply) + agent._quote_pending_images.assert_not_awaited() + + async def test_short_youma_followup_uses_progress_reply(self): + agent = CustomerServiceAgent() + st = agent._get_conversation_state(self.customer_id) + st.pending_image_urls = ["https://img.alicdn.com/a.jpg"] + st.pending_requirements = [] + agent._sync_pending_quote_state(self.customer_id, st) + agent._quote_pending_images = AsyncMock(return_value={"reply": "不应触发报价", "need_transfer": False}) + + msg = CustomerMessage( + msg_id="m5bb", + acc_id="test_shop", + msg="有吗", + from_id=self.customer_id, + from_name="t", + cy_id=self.customer_id, + acc_type="AliWorkbench", + msg_type=0, + cy_name="t", + goods_name="专业找图", + goods_order="", + ) + resp = await agent.process_message(msg) + self.assertTrue(resp.should_reply) + self.assertNotIn("不太懂你的意思", resp.reply) + self.assertNotIn("没完全理解", resp.reply) + self.assertNotIn("没听明白", resp.reply) + st2 = agent._get_conversation_state(self.customer_id) + self.assertEqual(st2.quote_phase, "waiting_result") + agent._quote_pending_images.assert_not_awaited() + + def test_short_text_classifier(self): + agent = CustomerServiceAgent() + self.assertEqual(agent._classify_short_customer_text("有吗"), "progress_query") + self.assertEqual(agent._classify_short_customer_text("没有了"), "finish_signal") + self.assertEqual(agent._classify_short_customer_text("好的"), "ack") + + async def test_related_screenshot_followup_is_marked(self): + agent = CustomerServiceAgent() + st = agent._get_conversation_state(self.customer_id) + st.pending_image_urls = ["https://img.alicdn.com/a.jpg"] + st.pending_requirements = [] + agent._sync_pending_quote_state(self.customer_id, st) + + msg = CustomerMessage( + msg_id="m5c", + acc_id="test_shop", + msg="这是上一张的截图#*#https://img.alicdn.com/b.jpg", + from_id=self.customer_id, + from_name="t", + cy_id=self.customer_id, + acc_type="AliWorkbench", + msg_type=0, + cy_name="t", + goods_name="专业找图", + goods_order="", + ) + resp = await agent.process_message(msg) + self.assertTrue(resp.should_reply) + st2 = agent._get_conversation_state(self.customer_id) + self.assertIn("与上一张相关（截图/局部细节）", st2.pending_requirements) + async def test_find_image_not_edit_conflict_triggers_clarification(self): agent = CustomerServiceAgent() st = agent._get_conversation_state(self.customer_id)