Merge remote-tracking branch 'origin/3.0AI添加' into 3.0AI添加

docker-compose.yml

@@ -2,7 +2,7 @@ version: '3.8'
 
 services:
   db:
-    image: postgres:15-alpine
+    image: pgvector/pgvector:pg15  # 换成这个
     container_name: inventory_db
     restart: always
     environment:
@@ -10,7 +10,7 @@ services:
       POSTGRES_PASSWORD: 1234
       POSTGRES_DB: inventory_system
     volumes:
-      - ./pgdata_docker:/var/lib/postgresql/data
+      - ./pgdata_docker:/var/lib/postgresql/data # 这里保持不变，Docker会自动创建这个新文件夹
     ports:
       - "5435:5432"
 
@@ -41,4 +41,4 @@ services:
     ports:
       - "5175:5173"
     depends_on:
-      - backend
+      - backend

inventory-backend/app/__init__.py

@@ -90,6 +90,17 @@ def create_app():
     except ImportError as e:
         print(f"❌ 错误: Upload 模块导入失败: {e}")
 
+    # -----------------------------------------------------
+    # 2.4 注册以图搜图模块 (Image Search)
+    # -----------------------------------------------------
+    try:
+        from app.api.v1.common.image_search import image_search_bp
+        app.register_blueprint(image_search_bp, url_prefix='/api/v1/common')
+        app.register_blueprint(image_search_bp, url_prefix='/api/common', name='image_search_legacy')
+        print("✅ Image Search 模块注册成功")
+    except ImportError as e:
+        print(f"❌ 错误: Image Search 模块导入失败: {e}")
+
     # -----------------------------------------------------
     # 2.4 注册业务操作模块 (Transactions - 借还/维修/报废)
     # -----------------------------------------------------

inventory-backend/app/api/v1/common/image_search.py

@@ -0,0 +1,153 @@
+# -*- coding: utf-8 -*-
+"""
+以图搜图 API - CLIP Vision Embedding + pgvector 余弦距离检索
+"""
+
+import os
+import uuid
+import json
+from flask import Blueprint, request, jsonify
+from sqlalchemy import text
+from app.extensions import db
+from app.utils.ai_vision import load_clip_model, get_image_embedding
+
+# 注册蓝图
+image_search_bp = Blueprint('image_search', __name__)
+
+
+# ============================================================================
+# POST /api/v1/common/image-search
+# 以图搜图：上传图片 → CLIP embedding → pgvector 余弦相似度检索
+# ============================================================================
+
+@image_search_bp.route('/image-search', methods=['POST'])
+def image_search():
+    # ---------------------------------------------------------
+    # 1. 检查文件
+    # ---------------------------------------------------------
+    if 'file' not in request.files:
+        return jsonify({"code": 400, "msg": "未找到图片文件"}), 400
+
+    file = request.files['file']
+    if file.filename == '':
+        return jsonify({"code": 400, "msg": "未选择文件"}), 400
+
+    # ---------------------------------------------------------
+    # 2. 安全保存临时文件
+    # ---------------------------------------------------------
+    ext = file.filename.rsplit('.', 1)[-1].lower()
+    if ext not in {'png', 'jpg', 'jpeg', 'gif', 'bmp', 'webp'}:
+        return jsonify({"code": 400, "msg": "不支持的图片格式"}), 400
+
+    tmp_filename = f"{uuid.uuid4().hex}.{ext}"
+    tmp_dir = os.path.join(os.path.dirname(__file__), '..', '..', '..', 'uploads')
+    os.makedirs(tmp_dir, exist_ok=True)
+    tmp_path = os.path.join(tmp_dir, tmp_filename)
+
+    try:
+        file.save(tmp_path)
+        print(f"💾 [ImageSearch] 临时文件已保存: {tmp_path}")
+
+        # ---------------------------------------------------------
+        # 3. 提取 CLIP embedding
+        # ---------------------------------------------------------
+        load_clip_model()
+        embedding = get_image_embedding(tmp_path)
+        print(f"✅ [ImageSearch] Embedding 提取成功，维度: {len(embedding)}")
+
+    except Exception as e:
+        print(f"❌ [ImageSearch] 图像处理失败: {e}")
+        return jsonify({"code": 500, "msg": f"图像处理失败: {str(e)}"}), 500
+
+    finally:
+        # ---------------------------------------------------------
+        # 4. 无论成功与否，都删除临时文件
+        # ---------------------------------------------------------
+        if os.path.exists(tmp_path):
+            try:
+                os.remove(tmp_path)
+                print(f"🗑️ [ImageSearch] 临时文件已清理: {tmp_path}")
+            except Exception as e:
+                print(f"⚠️ [ImageSearch] 临时文件删除失败: {e}")
+
+    # ---------------------------------------------------------
+    # 5. pgvector 余弦相似度检索（跨表联合检索）
+    # ---------------------------------------------------------
+    try:
+        query_vector_str = '[' + ','.join(str(v) for v in embedding) + ']'
+
+        sql = text("""
+            SELECT id, name, spec_model, image_url,
+                   (1 - (vec <=> :query_vector)) AS similarity
+            FROM (
+                SELECT id,
+                       COALESCE(name, '') AS name,
+                       COALESCE(spec, '') AS spec_model,
+                       COALESCE(product_image, '') AS image_url,
+                       img_embedding AS vec
+                FROM material_base
+                WHERE img_embedding IS NOT NULL
+
+                UNION ALL
+
+                SELECT id,
+                       '采购入库' AS name,
+                       '到货照片' AS spec_model,
+                       COALESCE(arrival_photo, '') AS image_url,
+                       arrival_image_embedding AS vec
+                FROM stock_buy
+                WHERE arrival_image_embedding IS NOT NULL
+
+                UNION ALL
+
+                SELECT id,
+                       '采购入库' AS name,
+                       '质检报告' AS spec_model,
+                       COALESCE(qc_report, '') AS image_url,
+                       qc_report_image_embedding AS vec
+                FROM stock_buy
+                WHERE qc_report_image_embedding IS NOT NULL
+            ) AS combined
+            ORDER BY vec <=> :query_vector
+            LIMIT 10
+        """)
+
+        result = db.session.execute(sql, {"query_vector": query_vector_str})
+        rows = result.fetchall()
+
+        results = []
+        for row in rows:
+            item_id = row[0]
+            item_name = row[1] or ""
+            spec_model = row[2] or ""
+            raw_image = row[3]
+
+            # 解析图片 URL 列表，取第一张
+            image_url = ""
+            if raw_image:
+                try:
+                    image_list = json.loads(raw_image)
+                    if image_list and len(image_list) > 0:
+                        image_url = image_list[0]
+                except Exception:
+                    # 纯字符串直接使用
+                    image_url = str(raw_image)
+
+            results.append({
+                "id": item_id,
+                "name": item_name,
+                "spec_model": spec_model,
+                "image_url": image_url,
+                "similarity": round(float(row[4]), 4)
+            })
+
+        print(f"✅ [ImageSearch] 跨表检索完成，命中 {len(results)} 条结果")
+        return jsonify({
+            "code": 200,
+            "msg": "检索成功",
+            "data": results
+        })
+
+    except Exception as e:
+        print(f"❌ [ImageSearch] 数据库检索失败: {e}")
+        return jsonify({"code": 500, "msg": f"检索失败: {str(e)}"}), 500

inventory-backend/app/services/dify_permission_service.py

@@ -11,6 +11,8 @@ Dify 智能客服权限服务层
    - 跨模块越权查询：直接阻断，返回角色专属的错误信息给大模型
 """
 
+from typing import Optional
+
 from flask import g, current_app
 from flask_jwt_extended import decode_token
 from app.models.system import SysRolePermission
@@ -185,7 +187,7 @@ class DifyPermissionService:
         返回:
             {
                 'blocked': bool,           # 是否被拦截
-                'message': str | None,     # AI 应返回给用户的错误信息（如果有）
+                'message': Optional[str],     # AI 应返回给用户的错误信息（如果有）
             }
         """
         if DifyPermissionService.is_super_admin(role):

inventory-backend/app/services/export_service/excel_task.py

@@ -20,6 +20,8 @@ import logging
 from threading import Thread
 from datetime import datetime
 
+from typing import Optional
+
 from openpyxl import Workbook
 from openpyxl.styles import Font, PatternFill, Alignment, Border, Side
 
@@ -346,7 +348,7 @@ def get_task_status(task_id: str) -> dict:
 # 获取导出文件路径（供下载接口调用）
 # =============================================================================
 
-def get_export_filepath(task_id: str) -> str | None:
+def get_export_filepath(task_id: str) -> Optional[str]:
     """
     根据 task_id 返回已生成文件的完整路径。
     未完成或不存在返回 None。

inventory-backend/app/utils/ai_vision.py

@@ -0,0 +1,132 @@
+# -*- coding: utf-8 -*-
+"""
+AI Vision 模块 - CLIP Vision Encoder ONNX 推理
+"""
+
+import os
+import numpy as np
+from PIL import Image
+import onnxruntime as ort
+
+# ============================================================================
+# 全局模型单例（项目启动时加载一次）
+# ============================================================================
+
+MODEL_PATH = os.path.join(os.path.dirname(__file__), '..', '..', 'models', 'clip_vision.onnx')
+
+# 加载选项：CPU 推理，禁用依赖库的启动开销
+_session_options = ort.SessionOptions()
+_session_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
+
+ort_session: ort.InferenceSession = None
+
+
+def load_clip_model():
+    """启动时调用：全局加载 CLIP Vision 模型"""
+    global ort_session
+    if ort_session is not None:
+        return ort_session
+
+    if not os.path.exists(MODEL_PATH):
+        raise FileNotFoundError(f"CLIP Vision 模型未找到: {MODEL_PATH}")
+
+    ort_session = ort.InferenceSession(MODEL_PATH, sess_options=_session_options, providers=['CPUExecutionProvider'])
+    print(f"✅ [AI Vision] CLIP 模型加载成功: {MODEL_PATH}")
+    return ort_session
+
+
+# ============================================================================
+# CLIP 预处理常量
+# ============================================================================
+
+# ImageNet 标准归一化（CLIP 官方）
+IMAGENET_MEAN = [0.485, 0.456, 0.406]
+IMAGENET_STD = [0.229, 0.224, 0.225]
+
+# 模型输入尺寸
+INPUT_SIZE = 224
+
+
+def _center_crop_and_resize(image: Image.Image) -> Image.Image:
+    """
+    CLIP 官方预处理：中心裁剪抗干扰
+    - 将图片最短边缩放到 224
+    - 从正中间切取 224x224 区域
+    """
+    w, h = image.size
+
+    # 计算缩放后的目标尺寸
+    if w < h:
+        new_w = INPUT_SIZE
+        new_h = int(h * INPUT_SIZE / w)
+    else:
+        new_h = INPUT_SIZE
+        new_w = int(w * INPUT_SIZE / h)
+
+    # 缩放
+    image = image.resize((new_w, new_h), Image.BILINEAR)
+
+    # 中心裁剪
+    left = (new_w - INPUT_SIZE) // 2
+    top = (new_h - INPUT_SIZE) // 2
+    right = left + INPUT_SIZE
+    bottom = top + INPUT_SIZE
+
+    return image.crop((left, top, right, bottom))
+
+
+def _normalize(image_np: np.ndarray) -> np.ndarray:
+    """
+    对 224x224x3 图像进行 CLIP 标准归一化
+    image_np: shape (H, W, C), dtype uint8, 值域 [0, 255]
+    返回: shape (C, H, W), dtype float32, 值域 [0, 1]
+    """
+    # HWC -> CHW
+    image_np = image_np.transpose(2, 0, 1).astype(np.float32) / 255.0
+
+    # 归一化
+    for i, (mean, std) in enumerate(zip(IMAGENET_MEAN, IMAGENET_STD)):
+        image_np[i] = (image_np[i] - mean) / std
+
+    return image_np
+
+
+# ============================================================================
+# 主函数：提取图像 embedding
+# ============================================================================
+
+def get_image_embedding(image_path: str) -> list:
+    """
+    提取图像的 512 维 CLIP embedding 向量
+
+    参数:
+        image_path: 图像文件路径
+
+    返回:
+        list: 512 维浮点向量
+    """
+    if ort_session is None:
+        load_clip_model()
+
+    # 1. 图片预处理
+    image = Image.open(image_path).convert('RGB')
+    image = _center_crop_and_resize(image)
+    input_data = _normalize(np.array(image))
+    input_data = np.expand_dims(input_data, axis=0)  # [1, 3, 224, 224]
+
+    # 2. 构造占位符输入 (关键修复)
+    dummy_ids = np.zeros((1, 77), dtype=np.int64)
+    dummy_mask = np.zeros((1, 77), dtype=np.int64)
+
+    # 3. 传入模型进行推理
+    # 注意: 模型输入名在你的模型里必须叫 'pixel_values', 'input_ids', 'attention_mask'
+    # 如果报错找不到输入名，请打印 ort_session.get_inputs()[0].name 确认
+    outputs = ort_session.run(
+        ['image_embeds'],
+        {
+            'input_ids': dummy_ids,
+            'pixel_values': input_data.astype(np.float32),
+            'attention_mask': dummy_mask
+        }
+    )
+    return outputs[0][0].tolist()

inventory-backend/requirements.txt

@@ -10,6 +10,10 @@ flask-cors==4.0.0
 redis==5.0.1
 # 图片处理核心库
 Pillow>=10.0.0
+# ONNX 模型本地 CPU 推理
+onnxruntime>=1.16.0
+# 数值计算（ONNX 推理依赖）
+numpy>=1.24.0
 # [旧] 条形码生成库 (建议保留，防止旧代码报错)
 python-barcode>=0.14.0
 # [新增] 二维码生成库 (标签打印必需，包含PIL支持)

inventory-backend/scripts/init_all_vectors.py

@@ -0,0 +1,220 @@
+# -*- coding: utf-8 -*-
+from __future__ import annotations
+
+"""
+全量历史图片向量初始化脚本
+
+功能：遍历配置表中所有历史图片字段，批量提取 CLIP 512 维向量并存回数据库。
+用法：python scripts/init_all_vectors.py
+"""
+
+import os
+import json
+import sys
+from datetime import datetime
+from typing import List, Optional
+
+# 将项目根目录加入 Python 路径
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..'))
+
+from tqdm import tqdm
+from sqlalchemy import text
+
+# Flask 应用环境
+from app import create_app
+from app.extensions import db
+from app.utils.ai_vision import get_image_embedding, load_clip_model
+
+# ============================================================================
+# 业务配置：表 → 图片字段 → 向量字段 映射
+# ============================================================================
+TARGET_TABLES = [
+    # 基础物料
+    {"table": "material_base", "img_col": "product_image", "vec_col": "img_embedding"},
+
+    # 采购入库
+    {"table": "stock_buy", "img_col": "arrival_photo", "vec_col": "arrival_image_embedding"},
+    {"table": "stock_buy", "img_col": "qc_report", "vec_col": "qc_report_image_embedding"},
+]
+
+# 物理图片根目录（相对于 app 目录的相对路径 ../uploads/）
+APP_DIR = os.path.join(os.path.dirname(__file__), '..', 'app')
+UPLOADS_ROOT = os.path.abspath(os.path.join(APP_DIR, '..', 'uploads'))
+
+
+# ============================================================================
+# 核心工具函数
+# ============================================================================
+
+def parse_img_field(raw_value: str) -> List[str]:
+    """
+    健壮解析图片字段，支持以下格式：
+      - JSON 数组字符串: ["a.jpg", "b.jpg"]
+      - 纯字符串单图片: "a.jpg"
+      - 带 /api/v1/files/ 前缀: ["/api/v1/files/a.jpg"]
+    返回: 提取出的文件名列表
+    """
+    if not raw_value or (isinstance(raw_value, str) and not raw_value.strip()):
+        return []
+
+    try:
+        # 先尝试按 JSON 解析（处理 JSON 数组字符串）
+        parsed = json.loads(raw_value)
+        if isinstance(parsed, list):
+            items = parsed
+        else:
+            items = [parsed]
+    except (json.JSONDecodeError, TypeError):
+        # JSON 解析失败，说明是纯字符串，直接按单图片处理
+        items = [raw_value.strip()]
+
+    filenames = []
+    for item in items:
+        if not item or not isinstance(item, str):
+            continue
+        item = item.strip()
+        if not item:
+            continue
+        # 去掉可能的 /api/v1/files/ 前缀
+        filename = os.path.basename(item)
+        filenames.append(filename)
+
+    return filenames
+
+
+def build_local_path(filename: str) -> str:
+    """
+    将文件名拼装成本地绝对路径
+    """
+    return os.path.join(UPLOADS_ROOT, filename)
+
+
+def extract_first_valid_vector(raw_img_field: str, table_name: str, img_col: str) -> Optional[str]:
+    """
+    读取图片字段，从第一条有效图片提取向量，返回写入 DB 的 JSON 字符串。
+    如果所有图片均失败，返回 None。
+    """
+    filenames = parse_img_field(raw_img_field)
+    if not filenames:
+        return None
+
+    for filename in filenames:
+        local_path = build_local_path(filename)
+
+        if not os.path.exists(local_path):
+            print(f"\033[91m[WARN] {table_name}.{img_col} | 文件不存在: {local_path}\033[0m")
+            continue
+
+        try:
+            vec = get_image_embedding(local_path)
+            if vec is not None:
+                return json.dumps(vec)
+        except Exception as e:
+            print(f"\033[91m[WARN] {table_name}.{img_col} | 推理异常 [{filename}]: {type(e).__name__}: {e}\033[0m")
+            continue
+
+    return None
+
+
+# ============================================================================
+# 主入口
+# ============================================================================
+
+def main():
+    start = datetime.now()
+    total_success = 0
+    total_skip = 0
+
+    print("=" * 60)
+    print("📦  全量历史图片向量初始化")
+    print("=" * 60)
+    print(f"图片目录: {UPLOADS_ROOT}")
+    print(f"待处理表数: {len(TARGET_TABLES)}")
+    print()
+
+    # 1. 初始化 Flask 应用上下文（加载 CLIP 模型）
+    app = create_app()
+    with app.app_context():
+        load_clip_model()
+        print("✅ CLIP 模型加载完成")
+        print()
+
+        # 2. 遍历目标表
+        for config in TARGET_TABLES:
+            table_name = config["table"]
+            img_col = config["img_col"]
+            vec_col = config["vec_col"]
+
+            print(f"正在处理表: {table_name}, 字段: {img_col}")
+
+            # 3. 查询待清洗记录（只选未处理过的）
+            sql = text(f"""
+                SELECT id, {img_col}
+                FROM {table_name}
+                WHERE {img_col} IS NOT NULL 
+                  AND {img_col} != '[]' 
+                  AND ({vec_col} IS NULL)
+            """)
+            rows = db.session.execute(sql).fetchall()
+
+            if not rows:
+                print(f"[{table_name}/{img_col}] ⏭  无待处理记录")
+                continue
+
+            print(f"\n[{table_name}/{img_col}] 📋  待处理: {len(rows)} 条")
+
+            # 4. 逐条处理
+            processed = 0
+            success_count = 0
+
+            for row in tqdm(rows, desc=f"{table_name}/{img_col}", unit="条"):
+                record_id = row[0]
+                raw_img = row[1]
+
+                try:
+                    vec_json = extract_first_valid_vector(raw_img, table_name, img_col)
+                    if vec_json is None:
+                        total_skip += 1
+                        continue
+
+                    # 更新向量字段
+                    update_sql = text(f"""
+                        UPDATE {table_name} SET {vec_col} = :vec_str WHERE id = :id
+                    """)
+                    db.session.execute(update_sql, {"vec_str": vec_json, "id": record_id})
+                    success_count += 1
+
+                    # 每 50 条提交一次
+                    if processed > 0 and processed % 50 == 0:
+                        db.session.commit()
+                        print(f"\n  ✅ 已提交 {processed} 条")
+
+                except Exception as e:
+                    print(f"\n\033[91m[WARN] {table_name}/{img_col} | ID={record_id} 处理异常: {type(e).__name__}: {e}\033[0m")
+                    # 关键：任何异常都不中断，只 continue 下一条
+                    db.session.rollback()
+                    continue
+                finally:
+                    processed += 1
+
+            # 循环结束后补一次 commit（处理未凑满50条的剩余数据）
+            try:
+                db.session.commit()
+            except Exception:
+                db.session.rollback()
+
+            total_success += success_count
+            print(f"[{table_name}/{img_col}] ✅  完成，成功 {success_count} 条 / 跳过 {len(rows) - success_count} 条")
+
+    # 5. 汇总报告
+    elapsed = (datetime.now() - start).total_seconds()
+    print()
+    print("=" * 60)
+    print(f"🏁  全部完成！总计耗时 {elapsed:.1f} 秒")
+    print(f"    ✅ 成功写入向量: {total_success} 条")
+    print(f"    ⏭  无有效图片（跳过）: {total_skip} 条")
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    main()

inventory-web/index.html

@@ -11,6 +11,15 @@
     <script type="module" src="/src/main.ts"></script>
     <script>
   window.initDifyChatbot = function() {
+    // 【关键】增加保护检查：确保 DOM 已经就绪
+    if (document.readyState === 'loading') {
+      document.addEventListener('DOMContentLoaded', performInit);
+    } else {
+      performInit();
+    }
+  };
+
+  function performInit() {
     var currentToken = localStorage.getItem('access_token') || localStorage.getItem('token') || '';
     var username = localStorage.getItem("username") || '';
 
@@ -19,17 +28,16 @@
       return;
     }
 
-    // 【新增 1】彻底清理浏览器内存中残留的 Dify 全局对象
+    // 彻底清理浏览器内存中残留的 Dify 全局对象
     window.difyChatbot = undefined;
     delete window.difyChatbot;
 
-    // 【新增 2】清理旧的 DOM 节点
+    // 清理旧的 DOM 节点
     var oldScript = document.getElementById('6T0eTgukUEqzK0iW');
     if (oldScript) oldScript.remove();
     document.querySelectorAll('[id^="dify-chatbot-"]').forEach(function(el) { el.remove(); });
 
-    // 【核心破解 3】动态化 user_id，打破 Dify 会话锁定机制
+    // 动态化 user_id，打破 Dify 会话锁定机制
-    // 取 token 的最后 8 位拼在用户名后。只要 Token 变了，Dify 就会开启新会话，强制读取新 Token。
     var dynamicUserId = username + '_' + currentToken.slice(-8);
 
     window.difyChatbotConfig = {
@@ -39,22 +47,20 @@
         "user_token": currentToken
       },
       systemVariables: {
-        "user_id": dynamicUserId  // <- 这里使用了动态 ID
+        "user_id": dynamicUserId
       },
       userVariables: {},
     };
 
-    // 【新增 4】在脚本 URL 后加上时间戳，破除浏览器强缓存
+    // 重新挂载
     var script = document.createElement('script');
     script.src = 'http://172.16.0.198:8080/embed.min.js?t=' + new Date().getTime();
     script.id = '6T0eTgukUEqzK0iW';
     script.defer = true;
     document.head.appendChild(script);
-    
-    console.log('✅ Dify chatbot 已挂载新会话，当前绑定 ID:', dynamicUserId);
-  };
 
-  setTimeout(window.initDifyChatbot, 100);
+    console.log('✅ Dify chatbot 已挂载新会话，当前绑定 ID:', dynamicUserId);
+  }
 </script>
 
 <!--<script-->
@@ -71,7 +77,7 @@
 
   /* 变成"独立悬浮窗口" */
   #dify-chatbot-bubble-window {
-    /* 👇 解除原本锁定在右下角的限制，将其定位在屏幕中间偏左上 */
+    /* 解除原本锁定在右下角的限制，将其定位在屏幕中间偏左上 */
     top: 15vh !important;
     left: 20vw !important;
     bottom: auto !important;
@@ -82,9 +88,9 @@
     height: 70vh !important;
 
     border-radius: 12px !important;
-    box-shadow: 0 12px 48px rgba(0, 0, 0, 0.2) !important; /* 增加超大弥散阴影，浮现感更强 */
+    box-shadow: 0 12px 48px rgba(0, 0, 0, 0.2) !important;
 
-    /* 👇 开启右下角拖拽，并强制留出 16px 的白边给拖拽手柄 */
+    /* 开启右下角拖拽，并强制留出 16px 的白边给拖拽手柄 */
     resize: both !important;
     overflow: hidden !important;
     padding-bottom: 16px !important;
@@ -124,4 +130,4 @@
   });
 </script>
   </body>
-</html>
+</html>

inventory-web/src/App.vue

@@ -20,6 +20,10 @@ onMounted(() => {
   if (userStore.token) {
     userStore.refreshUserPermissions()
   }
+  // 当 Vue 根组件挂载完毕，确保 Dify 图标一定会被加载
+  if (typeof (window as any).initDifyChatbot === 'function') {
+    (window as any).initDifyChatbot()
+  }
 })
 
 // ================================================================
@@ -235,7 +239,7 @@ const handleLogout = () => {
     <footer v-if="!isLoginPage" class="app-footer">
       <span class="version-tag">
         <el-icon style="vertical-align: middle; margin-right: 4px"><InfoFilled /></el-icon>
-        当前版本:V3.29（添加AI助手版）
+        当前版本:V3.30（添加AI助手版）
       </span>
     </footer>
 

inventory-web/src/api/common/upload.ts

@@ -3,7 +3,6 @@ import request from '@/utils/request'
 /**
  * 上传文件通用接口
  * @param data File 对象 或 FormData 对象
- * 适配说明：list.vue 中 customUpload 已经封装了 FormData，所以这里支持直接传 FormData
  */
 export function uploadFile(data: File | FormData) {
     let formData: FormData
@@ -11,14 +10,12 @@ export function uploadFile(data: File | FormData) {
     if (data instanceof FormData) {
         formData = data
     } else {
-        // 如果传入的是原始 File 对象，则手动封装
         formData = new FormData()
         // @ts-ignore
         formData.append('file', data)
     }
 
     return request({
-        // 注意：这里 /v1/common/upload 需要与后端 BluePrint 注册的 url_prefix 对应
         url: '/v1/common/upload',
         method: 'post',
         data: formData,
@@ -29,13 +26,50 @@ export function uploadFile(data: File | FormData) {
 }
 
 /**
- * 删除文件通用接口 (新增)
+ * 删除文件通用接口
  * @param filename 文件名 (例如: a1b2c3d4.jpg)
  */
 export function deleteFile(filename: string) {
     return request({
-        // 对应后端路由: @upload_bp.route('/files/<filename>', methods=['DELETE'])
         url: `/v1/common/files/${filename}`,
         method: 'delete'
     })
+}
+
+// ============================================================================
+// 以图搜图 API
+// ============================================================================
+
+/** 以图搜图返回的物料项 */
+export interface ImageSearchItem {
+    product_id: number
+    product_name: string
+    spec_model: string
+    image_url: string
+    similarity: number
+}
+
+/** 以图搜图响应结构 */
+export interface ImageSearchResponse {
+    code: number
+    msg: string
+    data: ImageSearchItem[]
+}
+
+/**
+ * 以图搜图
+ * @param file 图片文件 (File 对象或 Blob)
+ */
+export function imageSearch(file: File | Blob) {
+    const formData = new FormData()
+    formData.append('file', file)
+
+    return request<ImageSearchResponse>({
+        url: '/v1/common/image-search',
+        method: 'post',
+        data: formData,
+        headers: {
+            'Content-Type': 'multipart/form-data'
+        }
+    })
 }

inventory-web/src/components/ImageSearchDialog.vue

@@ -0,0 +1,458 @@
+<template>
+  <el-dialog
+      v-model="visible"
+      title="以图搜图"
+      width="680px"
+      destroy-on-close
+      :close-on-click-modal="false"
+      @close="handleClose"
+  >
+    <div class="image-search-body">
+      <!-- 左侧：图片上传 -->
+      <div class="upload-section">
+        <el-upload
+            ref="uploadRef"
+            class="image-uploader"
+            drag
+            :auto-upload="false"
+            :show-file-list="false"
+            accept="image/*"
+            :on-change="handleFileChange"
+        >
+          <div v-if="!previewUrl" class="upload-placeholder">
+            <el-icon class="upload-icon" :size="48"><UploadFilled /></el-icon>
+            <div class="upload-text">点击或拖拽图片上传</div>
+            <div class="upload-hint">支持 jpg/png/gif 等格式</div>
+          </div>
+          <div v-else class="preview-wrapper">
+            <img :src="previewUrl" class="preview-image" />
+            <div class="preview-overlay">
+              <el-button size="small" @click.stop="clearImage">重新选择</el-button>
+            </div>
+          </div>
+        </el-upload>
+
+        <div v-if="searching" class="loading-tip">
+          <el-icon class="is-loading"><Loading /></el-icon>
+          <span>正在识别图片并检索...</span>
+        </div>
+      </div>
+
+      <!-- 右侧：搜索结果 -->
+      <div class="result-section">
+        <div v-if="!searched && !searching" class="result-empty">
+          <el-icon :size="40" color="#c0c4cc"><Picture /></el-icon>
+          <p>上传图片后自动检索</p>
+        </div>
+
+        <div v-else-if="searched && results.length === 0" class="result-empty">
+          <el-icon :size="40" color="#c0c4cc"><WarningFilled /></el-icon>
+          <p>未找到相似物料</p>
+          <p class="result-hint">请尝试更换图片</p>
+        </div>
+
+        <div v-else class="result-list">
+          <div
+              v-for="(item, index) in results"
+              :key="item.product_id"
+              class="result-item"
+          >
+            <div class="item-rank">{{ index + 1 }}</div>
+            <div class="item-image">
+              <img
+                  v-if="item.image_url"
+                  :src="fullImageUrl(item.image_url)"
+                  @error="handleImgError($event)"
+              />
+              <div v-else class="image-placeholder">
+                <el-icon :size="24" color="#c0c4cc"><Picture /></el-icon>
+              </div>
+            </div>
+            <div class="item-info">
+              <div class="item-name">{{ item.product_name || '未命名物料' }}</div>
+              <div class="item-spec">{{ item.spec_model || '无规格' }}</div>
+              <div class="item-similarity">
+                <span class="similarity-label">相似度</span>
+                <span class="similarity-value">{{ (item.similarity * 100).toFixed(2) }}%</span>
+              </div>
+            </div>
+            <div class="item-actions">
+              <el-button
+                  type="primary"
+                  size="small"
+                  @click="handleUse(item)"
+              >
+                使用此物料
+              </el-button>
+              <el-button
+                  size="small"
+                  @click="handleView(item)"
+              >
+                查看详情
+              </el-button>
+            </div>
+          </div>
+        </div>
+      </div>
+    </div>
+
+    <template #footer>
+      <el-button @click="handleClose">关闭</el-button>
+    </template>
+  </el-dialog>
+</template>
+
+<script setup lang="ts">
+import { ref, watch } from 'vue'
+import { UploadFilled, Loading, Picture, WarningFilled } from '@element-plus/icons-vue'
+import { ElMessage } from 'element-plus'
+import { imageSearch, type ImageSearchItem } from '@/api/common/upload'
+
+interface Props {
+  modelValue: boolean
+}
+
+const props = defineProps<Props>()
+
+const emit = defineEmits<{
+  (e: 'update:modelValue', val: boolean): void
+  (e: 'use', item: ImageSearchItem): void
+  (e: 'view', item: ImageSearchItem): void
+}>()
+
+const visible = ref(props.modelValue)
+const uploadRef = ref()
+const previewUrl = ref('')
+const currentFile = ref<File | null>(null)
+const searching = ref(false)
+const searched = ref(false)
+const results = ref<ImageSearchItem[]>([])
+
+watch(() => props.modelValue, (val) => {
+  visible.value = val
+  if (!val) {
+    resetState()
+  }
+})
+
+watch(visible, (val) => {
+  emit('update:modelValue', val)
+})
+
+const handleFileChange = (uploadFile: any) => {
+  const file = uploadFile.raw
+  if (!file) return
+
+  // 校验格式
+  const allowedTypes = ['image/jpeg', 'image/png', 'image/gif', 'image/webp', 'image/bmp']
+  if (!allowedTypes.includes(file.type)) {
+    ElMessage.warning('仅支持 jpg/png/gif/webp/bmp 格式')
+    return
+  }
+
+  currentFile.value = file
+  previewUrl.value = URL.createObjectURL(file)
+
+  // 自动触发搜索
+  doSearch(file)
+}
+
+const doSearch = async (file: File) => {
+  if (searching.value) return
+
+  searching.value = true
+  searched.value = false
+  results.value = []
+
+  try {
+    const res = await imageSearch(file)
+    if (res.code === 200) {
+      results.value = res.data || []
+    } else {
+      ElMessage.error(res.msg || '检索失败')
+    }
+  } catch (err: any) {
+    console.error('image search error:', err)
+    ElMessage.error(err.message || '网络错误，请重试')
+  } finally {
+    searching.value = false
+    searched.value = true
+  }
+}
+
+const clearImage = () => {
+  previewUrl.value = ''
+  currentFile.value = null
+  results.value = []
+  searched.value = false
+  uploadRef.value?.clearFiles()
+}
+
+const fullImageUrl = (path: string) => {
+  if (!path) return ''
+  // 相对路径转完整 URL
+  if (path.startsWith('http')) return path
+  const baseUrl = import.meta.env.VITE_API_BASE_URL || window.location.origin
+  return baseUrl + path
+}
+
+const handleImgError = (e: Event) => {
+  const img = e.target as HTMLImageElement
+  img.style.display = 'none'
+}
+
+const handleUse = (item: ImageSearchItem) => {
+  emit('use', item)
+  handleClose()
+}
+
+const handleView = (item: ImageSearchItem) => {
+  emit('view', item)
+}
+
+const handleClose = () => {
+  visible.value = false
+}
+
+const resetState = () => {
+  previewUrl.value = ''
+  currentFile.value = null
+  searching.value = false
+  searched.value = false
+  results.value = []
+}
+</script>
+
+<style scoped>
+.image-search-body {
+  display: flex;
+  gap: 24px;
+  min-height: 380px;
+}
+
+/* ── 左侧上传区 ── */
+.upload-section {
+  flex: 0 0 220px;
+  display: flex;
+  flex-direction: column;
+  gap: 12px;
+}
+
+.image-uploader {
+  width: 100%;
+}
+
+:deep(.el-upload) {
+  width: 100%;
+}
+
+:deep(.el-upload-dragger) {
+  width: 100%;
+  height: 280px;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  background: #fafafa;
+  border: 2px dashed #dcdfe6;
+  border-radius: 8px;
+  transition: border-color 0.2s;
+}
+
+:deep(.el-upload-dragger:hover) {
+  border-color: #409eff;
+}
+
+.upload-placeholder {
+  text-align: center;
+  color: #909399;
+}
+
+.upload-icon {
+  color: #c0c4cc;
+  margin-bottom: 12px;
+}
+
+.upload-text {
+  font-size: 14px;
+  font-weight: 500;
+  margin-bottom: 6px;
+}
+
+.upload-hint {
+  font-size: 12px;
+  color: #c0c4cc;
+}
+
+.preview-wrapper {
+  position: relative;
+  width: 100%;
+  height: 280px;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  border-radius: 8px;
+  overflow: hidden;
+}
+
+.preview-image {
+  max-width: 100%;
+  max-height: 100%;
+  object-fit: contain;
+}
+
+.preview-overlay {
+  position: absolute;
+  bottom: 0;
+  left: 0;
+  right: 0;
+  padding: 10px;
+  background: rgba(0, 0, 0, 0.5);
+  text-align: center;
+}
+
+.preview-overlay .el-button {
+  color: #fff;
+  border-color: rgba(255, 255, 255, 0.6);
+}
+
+.loading-tip {
+  display: flex;
+  align-items: center;
+  gap: 8px;
+  color: #409eff;
+  font-size: 13px;
+}
+
+/* ── 右侧结果区 ── */
+.result-section {
+  flex: 1;
+  overflow-y: auto;
+}
+
+.result-empty {
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  justify-content: center;
+  height: 280px;
+  color: #909399;
+  text-align: center;
+}
+
+.result-empty p {
+  margin: 8px 0 0;
+  font-size: 14px;
+}
+
+.result-hint {
+  font-size: 12px !important;
+  color: #c0c4cc;
+}
+
+.result-list {
+  display: flex;
+  flex-direction: column;
+  gap: 12px;
+}
+
+.result-item {
+  display: flex;
+  align-items: center;
+  gap: 12px;
+  padding: 10px 12px;
+  background: #f5f7fa;
+  border-radius: 8px;
+  transition: background 0.2s;
+}
+
+.result-item:hover {
+  background: #ecf5ff;
+}
+
+.item-rank {
+  flex: 0 0 24px;
+  width: 24px;
+  height: 24px;
+  background: #409eff;
+  color: #fff;
+  border-radius: 50%;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  font-size: 12px;
+  font-weight: 700;
+}
+
+.item-image {
+  flex: 0 0 60px;
+  width: 60px;
+  height: 60px;
+  border-radius: 6px;
+  overflow: hidden;
+  background: #fff;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  border: 1px solid #ebeef5;
+}
+
+.item-image img {
+  width: 100%;
+  height: 100%;
+  object-fit: cover;
+}
+
+.image-placeholder {
+  display: flex;
+  align-items: center;
+  justify-content: center;
+}
+
+.item-info {
+  flex: 1;
+  min-width: 0;
+}
+
+.item-name {
+  font-size: 14px;
+  font-weight: 600;
+  color: #303133;
+  white-space: nowrap;
+  overflow: hidden;
+  text-overflow: ellipsis;
+}
+
+.item-spec {
+  font-size: 12px;
+  color: #909399;
+  margin-top: 4px;
+  white-space: nowrap;
+  overflow: hidden;
+  text-overflow: ellipsis;
+}
+
+.item-similarity {
+  display: flex;
+  align-items: center;
+  gap: 6px;
+  margin-top: 6px;
+}
+
+.similarity-label {
+  font-size: 12px;
+  color: #909399;
+}
+
+.similarity-value {
+  font-size: 14px;
+  font-weight: 700;
+  color: #67c23a;
+}
+
+.item-actions {
+  display: flex;
+  flex-direction: column;
+  gap: 6px;
+  flex: 0 0 auto;
+}
+</style>

inventory-web/src/views/material/list.vue

@@ -84,6 +84,9 @@
 
           <el-button type="primary" plain @click="handleQuery">搜索</el-button>
           <el-button plain @click="resetQuery">重置</el-button>
+          <el-button type="primary" plain @click="imageSearchVisible = true">
+            <el-icon style="margin-right: 5px"><Picture /></el-icon>拍照识图
+          </el-button>
           <el-popover
               v-model:visible="advancedFilterVisible"
               placement="bottom"
@@ -564,6 +567,12 @@
         />
       </el-dialog>
 
+      <!-- 拍照识图弹窗 -->
+      <ImageSearchDialog
+          v-model="imageSearchVisible"
+          @use="handleImageSearchUse"
+      />
+
       <!-- 预警设置弹窗 -->
       <el-dialog v-model="warningDialog.visible" :title="warningDialog.title" width="500px" append-to-body destroy-on-close>
         <el-form ref="warningFormRef" :model="warningForm" :rules="warningRules" label-width="100px">
@@ -633,7 +642,7 @@
 
 <script setup lang="ts">
 import { ref, reactive, onMounted, nextTick, computed } from 'vue';
-import { Plus, Document, Refresh, Setting, Rank, Camera, Link, Download, Bell, CircleCheck, Files, ZoomIn, Delete } from '@element-plus/icons-vue';
+import { Plus, Document, Refresh, Setting, Rank, Camera, Link, Download, Bell, CircleCheck, Files, ZoomIn, Delete, Picture } from '@element-plus/icons-vue';
 import { ElMessage, ElMessageBox, ElLoading } from 'element-plus';
 import type { FormInstance, FormRules } from 'element-plus';
 import { useUserStore } from '@/stores/user';
@@ -655,6 +664,8 @@ import {
 import { uploadFile, deleteFile } from '@/api/common/upload';
 import { usePasteUpload } from '@/hooks/usePasteUpload';
 import WebRtcCamera from '@/components/Camera/WebRtcCamera.vue';
+import ImageSearchDialog from '@/components/ImageSearchDialog.vue';
+import { imageSearch as imageSearchApi, type ImageSearchItem } from '@/api/common/upload';
 
 const userStore = useUserStore();
 
@@ -716,6 +727,7 @@ const isUploading = ref(false);
 
 const tableSize = ref<'large' | 'default' | 'small'>('large');
 const advancedFilterVisible = ref(false);
+const imageSearchVisible = ref(false);
 const advancedConditions = ref([{ field: '', operator: '', value: '' }]);
 const fieldOptions = computed(() => {
   const allFields = [
@@ -1585,15 +1597,8 @@ const customUpload = async (options: any, targetField: 'generalImage' | 'general
     if (res.code === 200) {
       const newUrl = res.data.url
       form.value[targetField].push(newUrl)
-      // 同步更新 fileList，触发 el-upload UI 刷新
-      const fileObj = { name: newUrl.split('/').pop(), url: getImageUrl(newUrl) }
-      if (targetField === 'generalImage') {
-        fileListImage.value.push(fileObj)
-      } else {
-        fileListManual.value.push(fileObj)
-      }
       ElMessage.success('上传成功')
-      onSuccess(res)
+      onSuccess(res)  // el-upload v-model 自动更新 fileList，无需手动 push
     } else {
       ElMessage.error(res.msg || '上传失败');
       onError(new Error(res.msg))
@@ -1693,6 +1698,13 @@ const handleCameraConfirm = async (file: File) => {
   }
 };
 
+// 以图搜图 - 使用物料
+const handleImageSearchUse = (item: ImageSearchItem) => {
+  // 跳转到该物料详情页，或填充到表单
+  router.push({ path: '/material/list', query: { keyword: item.spec_model } });
+  ElMessage.success(`已定位物料: ${item.product_name}`);
+};
+
 const addCondition = () => {
   advancedConditions.value.push({ field: '', operator: '', value: '' });
 };