feat: 添加以图搜图功能（CLIP ONNX + pgvector）+ Dify会话修复 + 版本升至V3.30

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

@@ -0,0 +1,126 @@
+# -*- 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
+    # ---------------------------------------------------------
+    try:
+        # 将 Python list 转为 PostgreSQL 向量格式: '[0.1, 0.2, ...]'
+        query_vector_str = '[' + ','.join(str(v) for v in embedding) + ']'
+
+        sql = text("""
+            SELECT id, name, spec_model, product_image,
+                   (1 - (img_embedding <=> :query_vector)) AS similarity
+            FROM material_base
+            WHERE img_embedding IS NOT NULL
+            ORDER BY img_embedding <=> :query_vector
+            LIMIT 5
+        """)
+
+        result = db.session.execute(sql, {"query_vector": query_vector_str})
+        rows = result.fetchall()
+
+        results = []
+        for row in rows:
+            product_id = row[0]
+            product_name = row[1] or ""
+            spec_model = row[2] or ""
+            product_image = row[3]
+
+            # 解析图片 URL 列表，取第一张
+            image_url = ""
+            if product_image:
+                try:
+                    image_list = json.loads(product_image)
+                    if image_list and len(image_list) > 0:
+                        image_url = image_list[0]
+                except Exception:
+                    image_url = str(product_image)
+
+            results.append({
+                "product_id": product_id,
+                "product_name": product_name,
+                "spec_model": spec_model,
+                "image_url": image_url,
+                "similarity": round(float(row[4]), 4)
+            })
+
+        print(f"✅ [ImageSearch] 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