diff --git a/core/agent_pre_rules.py b/core/agent_pre_rules.py new file mode 100644 index 0000000..508ddf6 --- /dev/null +++ b/core/agent_pre_rules.py @@ -0,0 +1,200 @@ +from __future__ import annotations + +import random +from datetime import datetime +from typing import TYPE_CHECKING, Optional + +from core.rules import Rule, RuleContext, RuleEngine, RuleResult +from services.risk_service import RiskService + +if TYPE_CHECKING: + from core.pydantic_ai_agent import ( + AgentResponse, + ConversationState, + CustomerMessage, + CustomerServiceAgent, + ) + + +class AgentPreRuleService: + """Pre-processing rule chain for short replies, cooldown, and text risk.""" + + def __init__(self, agent: "CustomerServiceAgent", risk_service: RiskService): + self.agent = agent + self.risk_service = risk_service + self.engine = self._build_engine() + + async def run( + self, + *, + message: "CustomerMessage", + state: "ConversationState", + trace_id: str, + ) -> Optional["AgentResponse"]: + ctx = RuleContext(data={"message": message, "state": state, "trace_id": trace_id}) + result = await self.engine.run(ctx) + if not result.stop: + return None + response = result.payload.get("response") + return response + + def _build_engine(self) -> RuleEngine: + return RuleEngine( + rules=[ + Rule( + name="meaningless_short_text", + priority=10, + predicate=self._rule_pred_meaningless_short_text, + action=self._rule_act_meaningless_short_text, + ), + Rule( + name="cooldown_silent", + priority=20, + predicate=self._rule_pred_cooldown_silent, + action=self._rule_act_cooldown_silent, + ), + Rule( + name="manual_risk_block", + priority=30, + predicate=self._rule_pred_manual_risk_block, + action=self._rule_act_manual_risk_block, + ), + Rule( + name="text_risk_block", + priority=40, + predicate=self._rule_pred_text_risk_block, + action=self._rule_act_text_risk_block, + ), + ] + ) + + async def _rule_pred_meaningless_short_text(self, ctx: RuleContext) -> bool: + from core.pydantic_ai_agent import _is_meaningless_short_text + + message = ctx.get("message") + return _is_meaningless_short_text(message.msg) + + async def _rule_act_meaningless_short_text(self, ctx: RuleContext) -> RuleResult: + from core.pydantic_ai_agent import AgentResponse + + message = ctx.get("message") + state = ctx.get("state") + trace_id = ctx.get("trace_id", "") + ping = random.choice(("嗯咯", "嗯啦", "嗯", "哦")) + state.last_reply_at = datetime.now() + self.agent._activity_log( + "agent_ping_reply", + trace_id=trace_id, + customer_id=message.from_id, + msg=message.msg, + reply=ping, + ) + return RuleResult( + matched=True, + stop=True, + action="agent_ping_reply", + payload={"response": AgentResponse(reply=ping, should_reply=True, need_transfer=False)}, + ) + + async def _rule_pred_cooldown_silent(self, ctx: RuleContext) -> bool: + message = ctx.get("message") + state = ctx.get("state") + return self.agent._in_cooldown(state, message.msg) + + async def _rule_act_cooldown_silent(self, ctx: RuleContext) -> RuleResult: + from core.pydantic_ai_agent import AgentResponse + + message = ctx.get("message") + state = ctx.get("state") + trace_id = ctx.get("trace_id", "") + elapsed = int((datetime.now() - state.last_reply_at).total_seconds()) if state.last_reply_at else 0 + print(f"[Agent] 冷却期静默（距上次回复 {elapsed}s）：{message.msg!r}") + self.agent._activity_log( + "agent_cooldown_silent", + trace_id=trace_id, + customer_id=message.from_id, + elapsed_s=elapsed, + ) + return RuleResult( + matched=True, + stop=True, + action="agent_cooldown_silent", + payload={"response": AgentResponse(reply="", should_reply=False, need_transfer=False)}, + ) + + async def _rule_pred_manual_risk_block(self, ctx: RuleContext) -> bool: + message = ctx.get("message") + decision = self.risk_service.check_manual_block(message.from_id) + ctx.set("manual_risk_decision", decision) + return decision.blocked + + async def _rule_act_manual_risk_block(self, ctx: RuleContext) -> RuleResult: + from core.pydantic_ai_agent import AgentResponse, TRANSFER_MESSAGE + + message = ctx.get("message") + trace_id = ctx.get("trace_id", "") + decision = ctx.get("manual_risk_decision") + self.agent._activity_log( + "agent_manual_risk_reject", + trace_id=trace_id, + customer_id=message.from_id, + risk=(decision.profile if decision else {}), + ) + return RuleResult( + matched=True, + stop=True, + action="agent_manual_risk_reject", + payload={ + "response": AgentResponse( + reply="这边无法继续为你处理该类需求，给你转人工专员对接。", + should_reply=True, + need_transfer=True, + transfer_msg=TRANSFER_MESSAGE, + ) + }, + ) + + async def _rule_pred_text_risk_block(self, ctx: RuleContext) -> bool: + message = ctx.get("message") + decision = await self.risk_service.check_text_block( + message.msg, + political_detector=self.agent._is_political_inquiry, + map_detector=self.agent._is_map_inquiry, + ) + ctx.set("text_risk_decision", decision) + return decision.blocked + + async def _rule_act_text_risk_block(self, ctx: RuleContext) -> RuleResult: + from core.pydantic_ai_agent import AgentResponse + + message = ctx.get("message") + state = ctx.get("state") + trace_id = ctx.get("trace_id", "") + decision = ctx.get("text_risk_decision") + state.pending_image_urls.clear() + state.pending_requirements.clear() + self.agent._sync_pending_quote_state(message.from_id, state) + + reject_text = self.risk_service.build_reject_text(decision.category if decision else "other") + reply = await self.agent._rewrite_reply_with_ai( + message=message, + state=state, + reply=reject_text, + scene="risk_reject", + ) + state.last_reply_at = datetime.now() + print(f"{self.agent.C_REPLY}[REPLY->CUSTOMER]{self.agent.C_RESET} {reply}") + self.agent._activity_log( + "agent_risk_reject", + trace_id=trace_id, + customer_id=message.from_id, + risk_category=(decision.category if decision else "other"), + risk_source=(decision.source if decision else "unknown"), + reply=reply, + ) + return RuleResult( + matched=True, + stop=True, + action="agent_risk_reject", + payload={"response": AgentResponse(reply=reply, should_reply=True, need_transfer=False)}, + ) diff --git a/core/find_image_flow.py b/core/find_image_flow.py new file mode 100644 index 0000000..227b5fb --- /dev/null +++ b/core/find_image_flow.py @@ -0,0 +1,215 @@ +from __future__ import annotations + +from datetime import datetime +from typing import TYPE_CHECKING, Optional + +if TYPE_CHECKING: + from core.pydantic_ai_agent import AgentResponse, ConversationState, CustomerMessage, CustomerServiceAgent + + +async def handle_find_image_batch_flow( + agent: "CustomerServiceAgent", + *, + message: "CustomerMessage", + state: "ConversationState", + customer_text: str, + shop_type: str, +) -> Optional["AgentResponse"]: + """Handle find-image collecting/quote flow. Return response when handled.""" + from core.pydantic_ai_agent import AgentResponse, TRANSFER_MESSAGE + + if not (shop_type == "find_image" and agent._is_batch_quote_enabled(message.from_id, message.acc_id)): + return None + + incoming_urls = agent._extract_image_urls(customer_text) + text_without_urls = agent._strip_urls_from_text(customer_text) + short_intent = agent._classify_short_customer_text(text_without_urls) + + if incoming_urls: + is_related_followup = bool(text_without_urls and agent._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: + agent._append_requirement(state, text_without_urls) + if is_related_followup: + agent._append_requirement(state, "与上一张相关（截图/局部细节）") + state.image_count = len(state.pending_image_urls) + agent._refresh_quote_phase(state, "collecting") + agent._sync_pending_quote_state(message.from_id, state) + + if agent._is_batch_finish_intent( + text=customer_text, + state=state, + has_incoming_urls=bool(incoming_urls), + ): + should_defer = agent._should_defer_batch_quote(state, mark_ready=True) + agent._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 agent._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"{agent.C_REPLY}[REPLY->CUSTOMER]{agent.C_RESET} {defer_reply}") + return AgentResponse(reply=defer_reply, should_reply=True, need_transfer=False) + quote_res = await agent._quote_pending_images(state, message) + reply_text = agent._colloquialize_reply(quote_res.get("reply", "")) + reply_text = await agent._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"{agent.C_REPLY}[REPLY->CUSTOMER]{agent.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 "", + ) + + 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 agent._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"{agent.C_REPLY}[REPLY->CUSTOMER]{agent.C_RESET} {ack}") + return AgentResponse(reply=ack, should_reply=True, need_transfer=False) + + if not state.pending_image_urls: + return None + + if text_without_urls: + if short_intent == "finish_signal": + agent._mark_quote_ready(state) + elif short_intent == "progress_query": + if state.quote_phase != "ready_to_quote": + agent._refresh_quote_phase(state, "waiting_result") + elif short_intent == "ack": + if state.quote_phase != "ready_to_quote": + agent._refresh_quote_phase(state, "collecting") + else: + agent._append_requirement(state, text_without_urls) + agent._refresh_quote_phase(state, "collecting") + agent._sync_pending_quote_state(message.from_id, state) + if agent._is_find_image_not_edit_conflict(text_without_urls): + 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 agent._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"{agent.C_REPLY}[REPLY->CUSTOMER]{agent.C_RESET} {clarify}") + return AgentResponse(reply=clarify, should_reply=True, need_transfer=False) + + 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 agent._quote_pending_images(state, message) + reply_text = agent._colloquialize_reply(quote_res.get("reply", "")) + reply_text = await agent._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"{agent.C_REPLY}[REPLY->CUSTOMER]{agent.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 "", + ) + + if short_intent == "progress_query" or agent._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 agent._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"{agent.C_REPLY}[REPLY->CUSTOMER]{agent.C_RESET} {progress}") + return AgentResponse(reply=progress, should_reply=True, need_transfer=False) + + if agent._needs_clarification_in_collecting(text_without_urls): + 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 agent._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"{agent.C_REPLY}[REPLY->CUSTOMER]{agent.C_RESET} {ask}") + return AgentResponse(reply=ask, should_reply=True, need_transfer=False) + if agent._is_batch_finish_intent( + text=customer_text, + state=state, + has_incoming_urls=False, + ): + should_defer = agent._should_defer_batch_quote(state, mark_ready=True) + agent._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 agent._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"{agent.C_REPLY}[REPLY->CUSTOMER]{agent.C_RESET} {defer_reply}") + return AgentResponse(reply=defer_reply, should_reply=True, need_transfer=False) + quote_res = await agent._quote_pending_images(state, message) + reply_text = agent._colloquialize_reply(quote_res.get("reply", "")) + reply_text = await agent._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"{agent.C_REPLY}[REPLY->CUSTOMER]{agent.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 "", + ) + + 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 agent._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"{agent.C_REPLY}[REPLY->CUSTOMER]{agent.C_RESET} {remind}") + return AgentResponse(reply=remind, should_reply=True, need_transfer=False) diff --git a/core/pydantic_ai_agent.py b/core/pydantic_ai_agent.py index 46d8348..98b6384 100755 --- a/core/pydantic_ai_agent.py +++ b/core/pydantic_ai_agent.py @@ -24,8 +24,9 @@ from dotenv import load_dotenv from utils.metrics_tracker import emit as metrics_emit from utils.observability import emit_activity, build_trace_id from core.quote_state_machine import QuoteStateMachine -from core.rules import Rule, RuleContext, RuleEngine, RuleResult from services.risk_service import RiskService +from core.agent_pre_rules import AgentPreRuleService +from core.find_image_flow import handle_find_image_batch_flow load_dotenv() @@ -235,7 +236,7 @@ class CustomerServiceAgent: self.batch_quote_delay_turns = 1 self.quote_state_machine = QuoteStateMachine(delay_turns=self.batch_quote_delay_turns) self.risk_service = RiskService() - self._pre_rule_engine = self._build_pre_rule_engine() + self.pre_rule_service = AgentPreRuleService(self, self.risk_service) if not self.api_key: raise ValueError("请设置 OPENAI_API_KEY 环境变量") @@ -1764,157 +1765,6 @@ class CustomerServiceAgent: clean = msg.strip().rstrip("！!？?。.～~") return clean in self._COOLDOWN_PATTERNS - def _build_pre_rule_engine(self) -> RuleEngine: - return RuleEngine( - rules=[ - Rule( - name="meaningless_short_text", - priority=10, - predicate=self._rule_pred_meaningless_short_text, - action=self._rule_act_meaningless_short_text, - ), - Rule( - name="cooldown_silent", - priority=20, - predicate=self._rule_pred_cooldown_silent, - action=self._rule_act_cooldown_silent, - ), - Rule( - name="manual_risk_block", - priority=30, - predicate=self._rule_pred_manual_risk_block, - action=self._rule_act_manual_risk_block, - ), - Rule( - name="text_risk_block", - priority=40, - predicate=self._rule_pred_text_risk_block, - action=self._rule_act_text_risk_block, - ), - ] - ) - - async def _rule_pred_meaningless_short_text(self, ctx: RuleContext) -> bool: - message: CustomerMessage = ctx.get("message") - return _is_meaningless_short_text(message.msg) - - async def _rule_act_meaningless_short_text(self, ctx: RuleContext) -> RuleResult: - message: CustomerMessage = ctx.get("message") - state: ConversationState = ctx.get("state") - trace_id = ctx.get("trace_id", "") - ping = random.choice(("嗯咯", "嗯啦", "嗯", "哦")) - state.last_reply_at = datetime.now() - self._activity_log( - "agent_ping_reply", - trace_id=trace_id, - customer_id=message.from_id, - msg=message.msg, - reply=ping, - ) - return RuleResult( - matched=True, - stop=True, - action="agent_ping_reply", - payload={"response": AgentResponse(reply=ping, should_reply=True, need_transfer=False)}, - ) - - async def _rule_pred_cooldown_silent(self, ctx: RuleContext) -> bool: - message: CustomerMessage = ctx.get("message") - state: ConversationState = ctx.get("state") - return self._in_cooldown(state, message.msg) - - async def _rule_act_cooldown_silent(self, ctx: RuleContext) -> RuleResult: - message: CustomerMessage = ctx.get("message") - state: ConversationState = ctx.get("state") - trace_id = ctx.get("trace_id", "") - elapsed = int((datetime.now() - state.last_reply_at).total_seconds()) if state.last_reply_at else 0 - print(f"[Agent] 冷却期静默（距上次回复 {elapsed}s）：{message.msg!r}") - self._activity_log( - "agent_cooldown_silent", - trace_id=trace_id, - customer_id=message.from_id, - elapsed_s=elapsed, - ) - return RuleResult( - matched=True, - stop=True, - action="agent_cooldown_silent", - payload={"response": AgentResponse(reply="", should_reply=False, need_transfer=False)}, - ) - - async def _rule_pred_manual_risk_block(self, ctx: RuleContext) -> bool: - message: CustomerMessage = ctx.get("message") - decision = self.risk_service.check_manual_block(message.from_id) - ctx.set("manual_risk_decision", decision) - return decision.blocked - - async def _rule_act_manual_risk_block(self, ctx: RuleContext) -> RuleResult: - message: CustomerMessage = ctx.get("message") - trace_id = ctx.get("trace_id", "") - decision = ctx.get("manual_risk_decision") - self._activity_log( - "agent_manual_risk_reject", - trace_id=trace_id, - customer_id=message.from_id, - risk=(decision.profile if decision else {}), - ) - return RuleResult( - matched=True, - stop=True, - action="agent_manual_risk_reject", - payload={ - "response": AgentResponse( - reply="这边无法继续为你处理该类需求，给你转人工专员对接。", - should_reply=True, - need_transfer=True, - transfer_msg=TRANSFER_MESSAGE, - ) - }, - ) - - async def _rule_pred_text_risk_block(self, ctx: RuleContext) -> bool: - message: CustomerMessage = ctx.get("message") - decision = await self.risk_service.check_text_block( - message.msg, - political_detector=self._is_political_inquiry, - map_detector=self._is_map_inquiry, - ) - ctx.set("text_risk_decision", decision) - return decision.blocked - - async def _rule_act_text_risk_block(self, ctx: RuleContext) -> RuleResult: - message: CustomerMessage = ctx.get("message") - state: ConversationState = ctx.get("state") - trace_id = ctx.get("trace_id", "") - decision = ctx.get("text_risk_decision") - state.pending_image_urls.clear() - state.pending_requirements.clear() - self._sync_pending_quote_state(message.from_id, state) - - reject_text = self.risk_service.build_reject_text(decision.category if decision else "other") - reply = await self._rewrite_reply_with_ai( - message=message, - state=state, - reply=reject_text, - scene="risk_reject", - ) - state.last_reply_at = datetime.now() - print(f"{self.C_REPLY}[REPLY->CUSTOMER]{self.C_RESET} {reply}") - self._activity_log( - "agent_risk_reject", - trace_id=trace_id, - customer_id=message.from_id, - risk_category=(decision.category if decision else "other"), - risk_source=(decision.source if decision else "unknown"), - reply=reply, - ) - return RuleResult( - matched=True, - stop=True, - action="agent_risk_reject", - payload={"response": AgentResponse(reply=reply, should_reply=True, need_transfer=False)}, - ) - 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]) @@ -1929,12 +1779,9 @@ class CustomerServiceAgent: 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_ctx = RuleContext(data={"message": message, "state": state, "trace_id": trace_id}) - pre_result = await self._pre_rule_engine.run(pre_ctx) - if pre_result.stop: - response = pre_result.payload.get("response") - if isinstance(response, AgentResponse): - return response + 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) @@ -1979,205 +1826,17 @@ class CustomerServiceAgent: print(f"[Agent] 订单通知静默（{pay_status or order_status}），跳过回复") return AgentResponse(reply="", should_reply=False, need_transfer=False) - # 找图店：先收集图片和需求，等客户确认“发完”后统一报价 customer_text, _ = self._split_customer_text(message.msg) shop_type = _get_shop_type(message.acc_id or "", message.goods_name or "") - 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( - text=customer_text, - 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}") - return AgentResponse( - reply=reply_text, - should_reply=not need_transfer, - need_transfer=need_transfer, - transfer_msg=TRANSFER_MESSAGE if need_transfer else "", - ) - - 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: - # 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_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_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) - if self._is_batch_finish_intent( - text=customer_text, - 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}") - return AgentResponse( - reply=reply_text, - should_reply=not need_transfer, - need_transfer=need_transfer, - transfer_msg=TRANSFER_MESSAGE if need_transfer else "", - ) - - 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) + 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 # 构建提示词（包含对话状态 + 客户画像） user_prompt = self._build_prompt(message, state)