refactor: 改用 pydantic-ai 框架调用豆包，结构化输出 PlateOrder

app.py

@@ -1,6 +1,6 @@
 """
 开版订单 AI 解析服务
-Flask + 豆包 API（/api/v3/responses 格式）
+Flask + pydantic-ai + 豆包（方舟 OpenAI 兼容协议 /chat/completions）
 
 功能：
 1. 代理企微消息接口（解决 CORS）
@@ -17,12 +17,19 @@ import json
 import logging
 import threading
 import time
+from typing import Optional
+
 import requests
 from datetime import datetime, timedelta
 from flask import Flask, request, jsonify
 from flask_cors import CORS
 from dotenv import load_dotenv
 
+from pydantic import BaseModel, Field, field_validator
+from pydantic_ai import Agent
+from pydantic_ai.models.openai import OpenAIChatModel
+from pydantic_ai.providers.openai import OpenAIProvider
+
 load_dotenv()
 
 # ─── 日志 ─────────────────────────────────────────────────────────────────────
@@ -36,15 +43,10 @@ logger = logging.getLogger(__name__)
 app = Flask(__name__)
 CORS(app, origins="*")
 
-# ─── 豆包 AI 配置 ─────────────────────────────────────────────────────────────
+# ─── 豆包 AI 配置（通过 pydantic-ai + OpenAI 兼容协议调用方舟）─────────────────
 AI_API_KEY  = os.getenv("AI_API_KEY", "")
 AI_BASE_URL = os.getenv("AI_BASE_URL", "https://ark.cn-beijing.volces.com/api/v3")
 AI_MODEL    = os.getenv("AI_MODEL", "doubao-seed-2-0-mini-260428")
-DOUBAO_URL  = f"{AI_BASE_URL}/responses"
-DOUBAO_HEADERS = {
-    "Authorization": f"Bearer {AI_API_KEY}",
-    "Content-Type": "application/json",
-}
 
 # ─── 企微消息服务配置 ─────────────────────────────────────────────────────────
 WECHAT_API_BASE = "https://test.ruicaiyinhua.online"
@@ -331,37 +333,57 @@ def build_system_prompt() -> str:
 3. 一律返回 JSON 对象，不要任何其它文字"""
 
 
+class PlateOrder(BaseModel):
+    """开版订单解析结果。所有字段均可为 null（无法判断时返回 None）。"""
+    customer_name: Optional[str] = Field(None, description="客户名称（自由文本，消息开头）")
+    plate_type: Optional[str] = Field(None, description="起版情况枚举")
+    urgency_level: Optional[str] = Field(None, description="紧急程度枚举")
+    production_method: Optional[str] = Field(None, description="做货方式枚举")
+    plate_method: Optional[str] = Field(None, description="开版方式枚举")
+    area: Optional[str] = Field(None, description="客户区域枚举")
+    fabric: Optional[str] = Field(None, description="面料名称（自由文本，保留克重与布类）")
+    width: Optional[str] = Field(None, description="面料幅宽枚举")
+    fabric_source: Optional[str] = Field(None, description="面料来源枚举")
+    drawing_rating: Optional[str] = Field(None, description="画图评级枚举")
+    color_matching_rating: Optional[str] = Field(None, description="调色评级枚举")
+    sample_rating: Optional[str] = Field(None, description="套样评级枚举")
+    difficulty_rating: Optional[str] = Field(None, description="难度评级枚举")
+    process_name: Optional[str] = Field(None, description="关联流程名称")
+    plate_notes: Optional[str] = Field(None, description="打版备注（自由文本，原文保留）")
+    style_name: Optional[str] = Field(None, description="款号名称")
+    original_design_code: Optional[str] = Field(None, description="原订单ID（5-7位纯数字）")
+    merchandiser_name: Optional[str] = Field(None, description="跟单员（取 sender 原值）")
+
+    @field_validator("*", mode="before")
+    @classmethod
+    def _normalize_empty(cls, v):
+        """模型有时会填字符串 'null'/'none'/空串，统一归一为 None"""
+        if isinstance(v, str) and v.strip().lower() in ("", "null", "none", "n/a", "无"):
+            return None
+        return v
+
+
+# pydantic-ai Agent：通过 OpenAI 兼容协议调用方舟（豆包）
+_provider = OpenAIProvider(base_url=AI_BASE_URL, api_key=AI_API_KEY)
+_model    = OpenAIChatModel(AI_MODEL, provider=_provider)
+plate_agent = Agent(
+    _model,
+    output_type=PlateOrder,
+    model_settings={"temperature": 0.1, "max_tokens": 4096},
+    retries=2,
+)
+
+
+@plate_agent.system_prompt
+def _dynamic_system_prompt() -> str:
+    """系统提示词依赖运行时加载的字典缓存，故每次调用动态生成"""
+    return build_system_prompt()
+
+
 def call_doubao(user_text: str) -> dict:
-    """调用豆包 /v3/responses，返回解析后的 dict"""
-    payload = {
-        "model": AI_MODEL,
-        "input": [
-            {"role": "system", "content": [{"type": "input_text", "text": build_system_prompt()}]},
-            {"role": "user",   "content": [{"type": "input_text", "text": user_text}]},
-        ],
-        "temperature": 0.1,
-        "max_output_tokens": 4096,
-    }
-    r = requests.post(DOUBAO_URL, headers=DOUBAO_HEADERS, json=payload, timeout=60)
-    r.raise_for_status()
-    data = r.json()
-
-    raw_text = ""
-    for item in data.get("output", []):
-        if item.get("type") == "message":
-            for c in item.get("content", []):
-                if c.get("type") == "output_text":
-                    raw_text = c.get("text", "").strip()
-                    break
-            break
-    if not raw_text:
-        raise ValueError("豆包返回内容为空")
-
-    start = raw_text.find("{")
-    end   = raw_text.rfind("}") + 1
-    if start == -1 or end == 0:
-        raise ValueError(f"响应中未找到 JSON：{raw_text}")
-    return json.loads(raw_text[start:end])
+    """通过 pydantic-ai 调用豆包，返回结构化结果 dict"""
+    result = plate_agent.run_sync(user_text)
+    return result.output.model_dump()
 
 
 # ─── 路由 ─────────────────────────────────────────────────────────────────────

requirements.txt

@@ -2,3 +2,4 @@ flask==3.0.3
 flask-cors==4.0.1
 requests==2.32.3
 python-dotenv==1.0.1
+pydantic-ai-slim[openai]==1.107.0