feat: add simulator page and image quote analyzer

qingjian_cs/app/agents.py

@@ -134,7 +134,9 @@ class QuoteAgent(_AgentRuntime):
     def __init__(self) -> None:
         super().__init__(
             "QuoteAgent",
-            rules_prompt() + "\n你是报价Agent。负责收图、报价触发、报价回复和报价阶段状态更新。",
+            rules_prompt()
+            + "\n你是报价Agent。负责收图、报价触发、报价回复和报价阶段状态更新。"
+            + "\n若上下文里有 image_quote_analysis，优先参考其 诉求类型/可做性/复杂度/建议报价 来决定回复语气与报价动作。",
         )
 
     async def decide(self, context: dict[str, Any]) -> Decision:

qingjian_cs/app/client.py

@@ -4,6 +4,7 @@ import re
 import time
 from collections import defaultdict
 from datetime import datetime
+from contextlib import suppress
 
 import websockets
 
@@ -12,6 +13,8 @@ from .auto_draw import auto_draw_preview
 from .config import (
     AUTO_DRAW_ENABLED,
     AUTO_QUOTE_WAIT_SECONDS,
+    DECISION_TIMEOUT_SECONDS,
+    MAX_CONCURRENT_TURNS,
     MESSAGE_DEBOUNCE_SECONDS,
     QINGJIAN_WS_URI,
 )
@@ -20,6 +23,7 @@ from .observability import activity_event, build_trace_id
 from .orchestrator import Orchestrator
 from .rules import extract_image_urls, prefilter_message
 from .runtime_switch import is_listen_only
+from .store import ConversationStore
 
 
 class QingjianClient:
@@ -30,9 +34,13 @@ class QingjianClient:
         self.websocket = None
         self.running = True
         self.orchestrator = Orchestrator()
+        self.store = ConversationStore()
 
         self.pending_msgs: dict[str, list[dict]] = defaultdict(list)
         self.debounce_tasks: dict[str, asyncio.Task] = {}
+        self.processing_tasks: dict[str, asyncio.Task] = {}
+        self.customer_locks: dict[str, asyncio.Lock] = defaultdict(asyncio.Lock)
+        self.turn_semaphore = asyncio.Semaphore(max(1, int(MAX_CONCURRENT_TURNS)))
         self.pending_images: dict[str, list[str]] = defaultdict(list)
         self.auto_quote_tasks: dict[str, asyncio.Task] = {}
         self.last_reply_key: dict[str, str] = {}
@@ -153,6 +161,20 @@ class QingjianClient:
         activity_event(self.logger, "send_reply_attempt", trace_id=trace_id, customer_id=data.get("from_id", "-"), msg=text)
         await self.send_message(msg)
         self._append_dialogue(key, "assistant", text)
+        try:
+            self.store.append_event(
+                key,
+                "assistant_message",
+                {
+                    "acc_id": data.get("acc_id", ""),
+                    "customer_id": data.get("from_id", ""),
+                    "msg_type": 0,
+                    "msg": text,
+                    "trace_id": trace_id,
+                },
+            )
+        except Exception as e:
+            self.logger.error("[入库] 客服消息写入失败: %s", e)
         self.recent_outbound.append((str(data.get("acc_id", "")), str(data.get("from_id", "")), text, time.monotonic()))
         if len(self.recent_outbound) > 200:
             self.recent_outbound = self.recent_outbound[-200:]
@@ -177,6 +199,20 @@ class QingjianClient:
         activity_event(self.logger, "send_image_attempt", trace_id=trace_id, customer_id=data.get("from_id", "-"), msg=image_url)
         await self.send_message(msg)
         self._append_dialogue(key, "assistant", f"[image]{image_url}")
+        try:
+            self.store.append_event(
+                key,
+                "assistant_message",
+                {
+                    "acc_id": data.get("acc_id", ""),
+                    "customer_id": data.get("from_id", ""),
+                    "msg_type": 1,
+                    "msg": image_url,
+                    "trace_id": trace_id,
+                },
+            )
+        except Exception as e:
+            self.logger.error("[入库] 客服图片消息写入失败: %s", e)
         self.recent_outbound.append((str(data.get("acc_id", "")), str(data.get("from_id", "")), image_url, time.monotonic()))
         if len(self.recent_outbound) > 200:
             self.recent_outbound = self.recent_outbound[-200:]
@@ -269,6 +305,13 @@ class QingjianClient:
                 if u not in self.pending_images[key]:
                     self.pending_images[key].append(u)
 
+        # 上下文优先从数据库回读，保证重启后也能恢复最近对话
+        try:
+            recent_dialogue = self.store.get_recent_dialogue(key, limit=24)
+        except Exception as e:
+            self.logger.error("[入库] 读取最近对话失败: %s", e)
+            recent_dialogue = self.recent_dialogue.get(key, [])
+
         context = {
             "customer_key": key,
             "acc_id": data.get("acc_id", ""),
@@ -278,9 +321,12 @@ class QingjianClient:
             "msg": merged_msg,
             "intent": "unknown",
             "pending_images": len(self.pending_images[key]),
+            "pending_image_urls": self.pending_images[key][-5:],
+            "current_image_urls": urls[-3:],
+            "latest_image_url": (urls[-1] if urls else (self.pending_images[key][-1] if self.pending_images[key] else "")),
             "auto_quote_trigger": auto_quote,
             "last_reply": self.last_reply_key.get(key, ""),
-            "recent_dialogue": self.recent_dialogue.get(key, [])[-12:],
+            "recent_dialogue": recent_dialogue[-12:],
         }
 
         activity_event(self.logger, "agent_process_start", trace_id=trace_id, customer_id=context["customer_id"], acc_id=context["acc_id"], intent=context["intent"])        
@@ -390,7 +436,13 @@ class QingjianClient:
         try:
             await asyncio.sleep(AUTO_QUOTE_WAIT_SECONDS)
             if self.pending_images.get(key):
-                await self._handle_decision(data, "", auto_quote=True)
+                # 自动报价也走并发控制与客户互斥
+                async with self.turn_semaphore:
+                    async with self.customer_locks[key]:
+                        await asyncio.wait_for(
+                            self._handle_decision(data, "", auto_quote=True),
+                            timeout=max(10, int(DECISION_TIMEOUT_SECONDS)),
+                        )
         finally:
             self.auto_quote_tasks.pop(key, None)
 
@@ -398,13 +450,56 @@ class QingjianClient:
         queue = self.pending_msgs.get(key, [])
         if not queue:
             return
-        indexed = list(enumerate(queue))
+        # 只处理当前快照，避免把处理中新增消息一并清掉
+        snapshot = list(queue)
+        if not snapshot:
+            return
+        indexed = list(enumerate(snapshot))
         indexed.sort(key=lambda it: (self._parse_msg_ts(it[1]), it[0]))
         ordered = [x for _, x in indexed]
         merged = "、".join([self._msg_text(x) for x in ordered if self._msg_text(x)])
         data = ordered[-1]
-        self.pending_msgs[key].clear()
         await self._handle_decision(data, merged)
+        # 仅弹出已处理快照，保留处理中到达的新消息
+        cur = self.pending_msgs.get(key, [])
+        n = min(len(snapshot), len(cur))
+        if n > 0:
+            del cur[:n]
+        if not cur:
+            self.pending_msgs.pop(key, None)
+
+    async def _run_customer_turn(self, key: str) -> None:
+        async with self.turn_semaphore:
+            async with self.customer_locks[key]:
+                await asyncio.wait_for(
+                    self._flush_customer(key),
+                    timeout=max(10, int(DECISION_TIMEOUT_SECONDS)),
+                )
+
+    def _schedule_customer_turn(self, key: str) -> None:
+        # 新消息来了就取消同客户旧任务，重新按最新消息计算
+        old = self.processing_tasks.get(key)
+        if old and not old.done():
+            old.cancel()
+
+        async def runner() -> None:
+            try:
+                await self._run_customer_turn(key)
+            except asyncio.CancelledError:
+                activity_event(self.logger, "customer_turn_cancelled", customer_id=key.split(":")[-1], reason="newer_message")
+                return
+            except asyncio.TimeoutError:
+                activity_event(self.logger, "customer_turn_timeout", customer_id=key.split(":")[-1], reason="decision_timeout")
+                return
+            except Exception as e:
+                activity_event(self.logger, "customer_turn_error", customer_id=key.split(":")[-1], reason=str(e))
+                return
+            finally:
+                cur = self.processing_tasks.get(key)
+                if cur is asyncio.current_task():
+                    self.processing_tasks.pop(key, None)
+
+        self.processing_tasks[key] = asyncio.create_task(runner())
 
     async def _debounce_enqueue(self, data: dict) -> None:
         key = self._customer_key(data)
@@ -420,7 +515,7 @@ class QingjianClient:
         async def later() -> None:
             try:
                 await asyncio.sleep(wait_s)
-                await self._flush_customer(key)
+                self._schedule_customer_turn(key)
             except asyncio.CancelledError:
                 return
             finally:
@@ -441,6 +536,23 @@ class QingjianClient:
 
         self.logger.info("[收消息] acc=%s from=%s type=%s msg=%s", data.get("acc_id", ""), data.get("from_id", ""), msg_type, msg)
         await post_tianwang_callback("message_received", data, extra={"msg_type": msg_type})
+        # 客户消息全量入库（监听模式也落库）
+        try:
+            customer_key = self._customer_key(data)
+            self.store.append_event(
+                customer_key,
+                "customer_message",
+                {
+                    "acc_id": data.get("acc_id", ""),
+                    "customer_id": data.get("from_id", ""),
+                    "msg_type": msg_type,
+                    "msg": msg,
+                    "raw_msg": data.get("msg", ""),
+                    "timestamp": data.get("timestamp", ""),
+                },
+            )
+        except Exception as e:
+            self.logger.error("[入库] 客户消息写入失败: %s", e)
 
         if self._is_outbound_echo(data, msg):
             activity_event(

qingjian_cs/app/http_api.py

@@ -1,8 +1,14 @@
 from __future__ import annotations
 
+import asyncio
+from collections import defaultdict
+
 from flask import Flask, jsonify, request
 
+from .auto_draw import auto_draw_preview
 from .logger import setup_logger
+from .orchestrator import Orchestrator
+from .rules import extract_image_urls
 from .runtime_switch import is_listen_only, set_listen_only
 from .task_manager import TaskManager
 
@@ -11,6 +17,17 @@ def create_http_app(task_manager: TaskManager | None = None) -> Flask:
     app = Flask(__name__)
     logger = setup_logger()
     tm = task_manager or TaskManager()
+    sim_orch = Orchestrator()
+    sim_pending_images: dict[str, list[str]] = defaultdict(list)
+    sim_recent_dialogue: dict[str, list[dict]] = defaultdict(list)
+
+    def _sim_append_dialogue(key: str, role: str, text: str) -> None:
+        t = str(text or "").strip()
+        if not t:
+            return
+        sim_recent_dialogue[key].append({"role": role, "text": t})
+        if len(sim_recent_dialogue[key]) > 24:
+            sim_recent_dialogue[key] = sim_recent_dialogue[key][-24:]
 
     @app.get('/api/health')
     def health():
@@ -58,6 +75,170 @@ def create_http_app(task_manager: TaskManager | None = None) -> Flask:
         limit = int(request.args.get('limit', 100))
         return jsonify({'ok': True, 'tasks': tm.list_tasks(limit=limit)})
 
+    @app.get('/debug/simulator')
+    def debug_simulator():
+        return """
+<!doctype html>
+<html lang="zh-CN">
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1" />
+  <title>链路测试页</title>
+  <style>
+    :root { --bg:#f6f8fb; --card:#fff; --ink:#1f2937; --line:#dbe3ef; --brand:#0f766e; }
+    body { margin:0; background:var(--bg); color:var(--ink); font-family:"Microsoft YaHei UI", "PingFang SC", sans-serif; }
+    .wrap { max-width:900px; margin:24px auto; padding:0 16px; }
+    .card { background:var(--card); border:1px solid var(--line); border-radius:14px; padding:16px; }
+    h2 { margin:0 0 14px; font-size:20px; }
+    .row { display:grid; grid-template-columns:140px 1fr; gap:8px; margin:8px 0; align-items:center; }
+    input, textarea { width:100%; box-sizing:border-box; border:1px solid #c7d3e3; border-radius:8px; padding:8px 10px; font-size:14px; }
+    textarea { min-height:100px; resize:vertical; }
+    .btns { display:flex; gap:10px; margin-top:12px; }
+    button { border:0; border-radius:8px; padding:9px 14px; cursor:pointer; color:#fff; background:var(--brand); }
+    button.secondary { background:#475569; }
+    pre { margin-top:12px; background:#0f172a; color:#e2e8f0; padding:12px; border-radius:8px; overflow:auto; }
+    label.chk { display:flex; gap:8px; align-items:center; font-size:14px; }
+  </style>
+</head>
+<body>
+  <div class="wrap">
+    <div class="card">
+      <h2>AI链路模拟测试</h2>
+      <div class="row"><div>店铺acc_id</div><input id="acc_id" value="demo_shop:cs" /></div>
+      <div class="row"><div>客户customer_id</div><input id="customer_id" value="demo_user_001" /></div>
+      <div class="row"><div>商品标题</div><input id="goods_name" value="模糊图清晰处理专业代找原图素材淘宝图片找图修复服务" /></div>
+      <div class="row"><div>订单信息</div><input id="goods_order" value="" placeholder="可空" /></div>
+      <div class="row"><div>客户消息</div><textarea id="msg" placeholder="可直接粘贴图片URL，支持 #*# 拼接"></textarea></div>
+      <div class="row"><div>选项</div><label class="chk"><input type="checkbox" id="simulate_draw" />quote时顺带模拟作图</label></div>
+      <div class="btns">
+        <button onclick="sendMsg()">发送模拟消息</button>
+        <button class="secondary" onclick="resetCtx()">清空该客户上下文</button>
+      </div>
+      <pre id="out">等待发送...</pre>
+    </div>
+  </div>
+  <script>
+    async function sendMsg() {
+      const body = {
+        acc_id: document.getElementById('acc_id').value.trim(),
+        customer_id: document.getElementById('customer_id').value.trim(),
+        goods_name: document.getElementById('goods_name').value.trim(),
+        goods_order: document.getElementById('goods_order').value.trim(),
+        msg: document.getElementById('msg').value.trim(),
+        simulate_draw: document.getElementById('simulate_draw').checked
+      };
+      const res = await fetch('/api/simulate/message', {
+        method: 'POST',
+        headers: {'Content-Type': 'application/json'},
+        body: JSON.stringify(body),
+      });
+      const data = await res.json();
+      document.getElementById('out').textContent = JSON.stringify(data, null, 2);
+    }
+    async function resetCtx() {
+      const body = {
+        acc_id: document.getElementById('acc_id').value.trim(),
+        customer_id: document.getElementById('customer_id').value.trim(),
+      };
+      const res = await fetch('/api/simulate/reset', {
+        method: 'POST',
+        headers: {'Content-Type': 'application/json'},
+        body: JSON.stringify(body),
+      });
+      const data = await res.json();
+      document.getElementById('out').textContent = JSON.stringify(data, null, 2);
+    }
+  </script>
+</body>
+</html>
+"""
+
+    @app.post('/api/simulate/reset')
+    def sim_reset():
+        body = request.get_json(silent=True) or {}
+        acc_id = str(body.get("acc_id", "")).strip()
+        customer_id = str(body.get("customer_id", "")).strip()
+        if not acc_id or not customer_id:
+            return jsonify({'ok': False, 'error': 'acc_id and customer_id required'}), 400
+        key = f"{acc_id}:{customer_id}"
+        sim_pending_images.pop(key, None)
+        sim_recent_dialogue.pop(key, None)
+        try:
+            sim_orch.store.upsert_session(key, acc_id, customer_id, "pre_sales", {"after_sales_stage": "new"})
+        except Exception:
+            pass
+        return jsonify({'ok': True, 'customer_key': key, 'message': '上下文已清空'})
+
+    @app.post('/api/simulate/message')
+    def sim_message():
+        body = request.get_json(silent=True) or {}
+        acc_id = str(body.get("acc_id", "")).strip()
+        customer_id = str(body.get("customer_id", "")).strip()
+        goods_name = str(body.get("goods_name", "")).strip()
+        goods_order = str(body.get("goods_order", "")).strip()
+        msg = str(body.get("msg", "")).strip()
+        simulate_draw = bool(body.get("simulate_draw"))
+        if not acc_id or not customer_id or not msg:
+            return jsonify({'ok': False, 'error': 'acc_id, customer_id, msg required'}), 400
+
+        key = f"{acc_id}:{customer_id}"
+        urls = extract_image_urls(msg)
+        for u in urls:
+            if u not in sim_pending_images[key]:
+                sim_pending_images[key].append(u)
+        _sim_append_dialogue(key, "user", msg)
+        latest_image_url = urls[-1] if urls else (sim_pending_images[key][-1] if sim_pending_images[key] else "")
+
+        context = {
+            "customer_key": key,
+            "acc_id": acc_id,
+            "customer_id": customer_id,
+            "goods_name": goods_name,
+            "goods_order": goods_order,
+            "msg": msg,
+            "intent": "unknown",
+            "pending_images": len(sim_pending_images[key]),
+            "pending_image_urls": sim_pending_images[key][-5:],
+            "current_image_urls": urls[-3:],
+            "latest_image_url": latest_image_url,
+            "auto_quote_trigger": False,
+            "last_reply": "",
+            "recent_dialogue": sim_recent_dialogue[key][-12:],
+        }
+
+        route, decision, state = asyncio.run(sim_orch.decide(context))
+        if decision.reply:
+            _sim_append_dialogue(key, "assistant", decision.reply)
+
+        draw_result: dict = {}
+        if simulate_draw and decision.action == "quote" and latest_image_url:
+            draw_result = asyncio.run(
+                auto_draw_preview(
+                    image_url=latest_image_url,
+                    customer_id=customer_id,
+                    requirement=msg,
+                )
+            )
+
+        logger.info("[模拟] key=%s route=%s action=%s", key, route, decision.action)
+        return jsonify(
+            {
+                "ok": True,
+                "customer_key": key,
+                "route": route,
+                "decision": {
+                    "action": decision.action,
+                    "reply": decision.reply,
+                    "transfer_msg": decision.transfer_msg,
+                    "quote_mode": decision.quote_mode,
+                    "reason": decision.reason,
+                },
+                "state": state,
+                "pending_images": sim_pending_images[key],
+                "draw_result": draw_result,
+            }
+        )
+
     return app
 
 

qingjian_cs/app/image_quote_analyzer.py

@@ -0,0 +1,265 @@
+from __future__ import annotations
+
+import asyncio
+import logging
+import os
+import re
+from typing import Any
+
+from openai import AsyncOpenAI
+
+from .config import OPENAI_API_KEY, OPENAI_BASE_URL, OPENAI_MODEL_NAME
+
+logger = logging.getLogger(__name__)
+
+VISION_MODEL = OPENAI_MODEL_NAME
+
+ANALYZE_PROMPT = """
+你是一个电商图片处理评估专家，同时也是 Gemini 图像生成提示词专家。
+请仔细分析这张图片，输出以下字段，每行一个，不要多余内容：
+
+敏感内容: <yes|no>
+平整度: <flat|mild|rough>
+含文字: <yes|no>
+含人脸: <yes|no>
+阴影: <yes|no>
+复杂度: <simple|normal|complex|hard>
+原因: <15字以内，说明复杂度判断依据>
+主体: <图片核心内容，如：印花图案/logo/人物/产品/老照片/风景/文字/其他>
+类型: <处理类型，如：印花提取/高清修复/去背景/老照片修复/logo提取/人像修复/其他>
+质量: <原图质量，如：清晰/轻微模糊/严重模糊/低分辨率/截图/扫描件>
+可做: <yes|partial|no>
+风险: <none|low|high>
+业务相关: <yes|no>
+透视: <no|mild|strong>
+比例: <从以下选一个最合适的：1:1 / 9:16 / 16:9 / 3:4 / 4:3 / 3:2 / 2:3 / 5:4 / 4:5>
+提示词: <为 Gemini 写处理指令，中文，60字以内，说明要做什么、保留什么、去掉什么>
+备注: <给客服AI的特别提示，没有则填无>
+
+判断规则：
+
+【报价核心：越平整越便宜】
+- 平整度 flat：画面平整、无褶皱、无透视 → 便宜
+- 平整度 mild：轻微褶皱/透视 → 中等
+- 平整度 rough：有褶皱/透视/曲面 → 贵
+- 含文字：大字没关系不加价；小字需精细保留/清晰化 → 加价（含文字填 yes 仅指有小字的情况）
+- 含人脸 yes：有人脸 → 加价
+- 阴影 yes：有明显阴影需处理 → 加价
+综合以上因素，越平整、无小字、无人脸、无阴影 → 越便宜（simple）
+
+【含文字】
+- yes：含小字需精细保留/清晰化（小字难处理 → 加价）
+- no：无文字，或仅有大字（大字没关系 → 不加价）
+
+【文字数量加价规则】
+- none：无文字，不加价
+- 少量 (1-10 字)：+5 元
+- 中量 (11-50 字)：+10-15 元
+- 大量 (51-200 字)：+20-30 元
+- 极多 (200 字以上)：+30-50 元
+
+【文字分层需求】
+- yes：客户要求可编辑分层文件（PSD 等） → 基础价格 x2 或 +50 元起
+- no：普通图片处理 → 正常价格
+
+【文字分层 + 大量文字】
+- 如果 文字数量=大量/极多 且 文字分层需求=yes → 总价可达 60-80 元
+
+【含人脸】
+- yes：图中有真实人物面孔（人像照/集体照/证件照/老照片等）
+- no：无人脸或人脸极小不影响主体
+
+【风险评估 - 重要！】
+- none：印花/图案/logo/风景/产品，AI处理效果稳定，可直接报价接单
+- low：有人脸但清晰度尚可，AI修复后人脸相似度70-90%，可以接单但要说明风险
+- high：以下任一情况 → 严重模糊的人脸照片/老照片人像/需要打印/客户问能否找回原图
+  high情况下，可做改为partial，备注写明风险话术，谨慎接单
+
+【敏感内容检测 - 必须严格判断！】
+- yes：含以下任一内容 → 色情/黄色/擦边/裸露/性暗示/大尺度/涉政/暴力/血腥/违禁品/地图类
+  敏感内容=yes 时，可做必须填 no，直接拒绝不接单
+- no：无上述敏感内容，可以正常接单处理
+
+【可做判断 - 决定是否接单】
+- yes：效果有把握，可以接单处理
+- partial：能处理但有明显限制（人脸变形风险/分辨率极低/严重损坏）→ 可以接单但要说明风险
+- no：无法接单（纯黑/纯白/完全损坏/找原始 RAW 文件/敏感内容/违法内容）
+
+【敏感内容】优先判断，若为 yes 则 可做 必填 no
+- yes：图片含色情/黄色/擦边/裸露/性暗示/大尺度等违规内容
+- no：无上述敏感内容
+
+【可做判断】
+- yes：效果有把握，可直接处理
+- partial：能处理但有明显限制（人脸变形风险/分辨率极低/严重损坏）
+- no：无法处理（纯黑/纯白/完全损坏/找原始RAW文件/敏感内容）
+
+【业务相关判断 - 必须由你判断】
+- yes：需求与印花/印刷素材处理，或高清修复业务相关
+- no：与以上业务无关（例如闲聊、非图像处理诉求）
+- 若业务相关=no，则可做必须填 no
+
+【风险话术模板（备注字段）】
+- 含人脸+需打印：AI修复后人脸可能有轻微变化，建议先看效果确认再打印
+- 严重模糊人脸：这张模糊程度较高，修复后清晰了但人脸可能跟原来有差异
+- 找原图：找不到原始文件，只能对现有图片做高清修复处理
+- 完全损坏：这张无法处理
+
+【透视判断】
+- no：正面拍摄，无明显变形
+- mild：轻微透视（衣服悬挂/桌面小角度斜拍）
+- strong：严重透视（俯拍/贴墙/大角度倾斜）
+
+【比例选择】
+- 印花/图案/logo/正方形 -> 1:1
+- 竖屏壁纸/短视频封面 -> 9:16
+- 宽屏/横版视频 -> 16:9
+- 移动广告/Instagram竖图 -> 4:5
+- 竖向人像/海报/证件照 -> 3:4
+- 竖向相机照片 -> 2:3
+- 接近正方形产品图 -> 5:4
+- 横向标准图/风景 -> 4:3
+- 横向相机照片/产品实拍 -> 3:2
+
+示例1（印花，无风险）：
+敏感内容: no
+平整度: mild
+含文字: no
+含人脸: no
+阴影: no
+复杂度: complex
+原因: 印花细节密集颜色层次多
+主体: 印花图案
+类型: 印花提取
+质量: 轻微模糊
+可做: yes
+风险: none
+透视: mild
+比例: 1:1
+提示词: 提取衣物印花图案，去除褶皱和背景杂色，补全缺失部分，保持颜色细节100%还原，输出干净平面印花图
+备注: 无
+
+示例2（人像老照片，要打印）：
+敏感内容: no
+平整度: flat
+含文字: no
+含人脸: yes
+阴影: no
+复杂度: hard
+原因: 严重模糊人脸细节丢失
+主体: 人物照片
+类型: 人像修复
+质量: 严重模糊
+可做: partial
+风险: high
+透视: no
+比例: 3:4
+提示词: 对模糊人像进行高清修复，增强细节，保持人物特征不变
+备注: AI修复后人脸可能有轻微变化，建议先看效果确认满意再用于打印
+
+示例3（平整印花，最便宜）：
+敏感内容: no
+平整度: flat
+含文字: no
+含人脸: no
+阴影: no
+复杂度: simple
+原因: 画面平整无褶皱无文字无人脸
+主体: 印花图案
+类型: 印花提取
+质量: 清晰
+可做: yes
+风险: none
+透视: no
+比例: 1:1
+提示词: 提取印花图案，去除背景，输出干净平面图
+备注: 无
+"""
+
+
+def _clip(text: str, n: int = 180) -> str:
+    t = re.sub(r"\s+", " ", str(text or "")).strip()
+    if len(t) <= n:
+        return t
+    return f"{t[:n]}..."
+
+
+def _extract_line(text: str, key: str) -> str:
+    m = re.search(rf"{re.escape(key)}\s*[:：]\s*(.+)", text or "")
+    return m.group(1).strip() if m else ""
+
+
+def _parse_result(text: str) -> dict[str, Any]:
+    intent = _extract_line(text, "诉求类型")
+    can_do = _extract_line(text, "可做").lower()
+    complexity = _extract_line(text, "复杂度").lower()
+    price_text = _extract_line(text, "建议报价")
+    note = _extract_line(text, "说明")
+
+    if intent not in {"找图", "高清修复", "其他"}:
+        intent = "其他"
+    if can_do not in {"yes", "partial", "no"}:
+        can_do = "partial"
+    if complexity not in {"simple", "normal", "complex", "hard"}:
+        complexity = "normal"
+
+    price = 0
+    m = re.search(r"\d+", price_text or "")
+    if m:
+        price = int(m.group(0))
+
+    return {
+        "intent_type": intent,
+        "can_do": can_do,
+        "complexity": complexity,
+        "price_suggest": price,
+        "note": note or "已看图",
+    }
+
+
+async def analyze_image_for_quote(image_url: str, customer_text: str = "", goods_name: str = "") -> dict[str, Any]:
+    """
+    识图报价分析（用于 QuoteAgent 上下文增强）
+    """
+    image_url = str(image_url or "").strip()
+    if not image_url:
+        return {"ok": False, "error": "empty_image_url"}
+    if not OPENAI_API_KEY:
+        return {"ok": False, "error": "OPENAI_API_KEY 未配置"}
+
+    model_name = VISION_MODEL
+    client = AsyncOpenAI(base_url=OPENAI_BASE_URL, api_key=OPENAI_API_KEY)
+    user_text = (
+        f"客户文本: {customer_text or '无'}\n"
+        f"商品标题: {goods_name or '无'}\n"
+        "请按固定字段输出。"
+    )
+    logger.info("[识图报价] 豆包模型=%s 图片=%s 提示词=%s", model_name, image_url, _clip(ANALYZE_PROMPT, 90))
+    try:
+        resp = await asyncio.wait_for(
+            client.chat.completions.create(
+                model=model_name,
+                temperature=0.1,
+                messages=[
+                    {"role": "system", "content": "你是电商图片识别报价助手。"},
+                    {
+                        "role": "user",
+                        "content": [
+                            {"type": "text", "text": f"{ANALYZE_PROMPT}\n{user_text}"},
+                            {"type": "image_url", "image_url": {"url": image_url}},
+                        ],
+                    },
+                ],
+            ),
+            timeout=30,
+        )
+        text = str((resp.choices[0].message.content if resp and resp.choices else "") or "").strip()
+        logger.info("[识图报价] 模型输出=%s", _clip(text, 260))
+        parsed = _parse_result(text)
+        parsed["ok"] = True
+        parsed["raw"] = text
+        return parsed
+    except Exception as e:
+        logger.error("[识图报价] 调用失败: %s", e)
+        return {"ok": False, "error": str(e)}
+

qingjian_cs/app/orchestrator.py

@@ -3,6 +3,7 @@ from __future__ import annotations
 from typing import Any
 
 from .agents import AfterSalesAgent, PreSalesAgent, QuoteAgent, RiskAgent, RouterAgent
+from .image_quote_analyzer import analyze_image_for_quote
 from .models import Decision
 from .state_machine import evolve_after_sales_state, migrate_state_schema
 from .store import ConversationStore
@@ -38,6 +39,14 @@ class Orchestrator:
         route, route_reason = await self.router.route(merged_ctx)
 
         if route == "quote":
+            latest_image_url = str(context.get("latest_image_url", "") or "").strip()
+            if latest_image_url:
+                analysis = await analyze_image_for_quote(
+                    image_url=latest_image_url,
+                    customer_text=str(context.get("msg", "") or ""),
+                    goods_name=str(context.get("goods_name", "") or ""),
+                )
+                merged_ctx["image_quote_analysis"] = analysis
             decision = await self.quote.decide(merged_ctx)
         elif route == "after_sales":
             decision = await self.after_sales.decide(merged_ctx)

qingjian_cs/services/service_gemini.py

@@ -67,6 +67,13 @@ class GeminiExtractV2Service:
   #  DEFAULT_PROMPT = "生成图片，把衣服的图案展开起来做成数码印花印刷平面图。去掉皱褶，生成图案增强细节。排除衣服图案以外内容"
     def __init__(self):
         self.session = None
+
+    @staticmethod
+    def _clip_text(text: str, limit: int = 120) -> str:
+        if not text:
+            return ""
+        compact = re.sub(r"\s+", " ", str(text)).strip()
+        return compact if len(compact) <= limit else f"{compact[:limit]}..."
     
     def image_to_base64(self, image_path: str) -> str:
         """将图片文件转换为base64编码字符串"""
@@ -108,6 +115,12 @@ class GeminiExtractV2Service:
             
         # 使用自定义提示词或默认提示词
         prompt = custom_prompt or self.DEFAULT_PROMPT
+        logger.info(
+            "图片识别任务开始: 输入=%s 输出=%s 提示词=%s",
+            input_path,
+            output_path,
+            self._clip_text(prompt, 100),
+        )
         
         # 按优先级逐个尝试API配置
         for config_index, config in enumerate(self.API_CONFIGS):
@@ -117,6 +130,12 @@ class GeminiExtractV2Service:
             for attempt in range(config['max_retries']):
                 try:
                     logger.info(f"开始Gemini V2印花提取 - {config['name']} (第{attempt + 1}/{config['max_retries']}次尝试): {input_path}")
+                    logger.info(
+                        "%s 模型参数: model=%s aspect_ratio=%s",
+                        config["name"],
+                        config["api_model"],
+                        aspect_ratio,
+                    )
                     
                     # 准备请求数据和URL
                     if config.get('use_gemini_format', False):
@@ -271,7 +290,15 @@ class GeminiExtractV2Service:
             # 根据API格式提取内容
             if config.get('use_gemini_format', False):
                 # Gemini原生API格式: candidates[0].content.parts[0]
-                content_parts = result['candidates'][0]['content']['parts']
+                candidates = result.get("candidates", [])
+                logger.info("%s 返回候选数量: %s", api_name, len(candidates))
+                if not candidates:
+                    logger.error("%s 响应缺少candidates", api_name)
+                    return False, "响应缺少candidates", {}
+                finish_reason = candidates[0].get("finishReason", "")
+                if finish_reason:
+                    logger.info("%s 返回结束原因: %s", api_name, finish_reason)
+                content_parts = candidates[0]['content']['parts']
                 
                 # 查找包含图片数据的part
                 image_data = None
@@ -287,6 +314,8 @@ class GeminiExtractV2Service:
                         except Exception as e:
                             logger.error(f"{api_name} Base64解码失败: {e}")
                             return False, f"Base64解码失败: {e}", {}
+                    elif "text" in part:
+                        logger.info("%s 模型文本输出: %s", api_name, self._clip_text(part.get("text", ""), 160))
                 
                 if not image_data:
                     logger.error(f"{api_name} 在Gemini响应中未找到图片数据")