386 lines
12 KiB
Python
386 lines
12 KiB
Python
"""
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智慧监督AI文书写作服务 - 主应用
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"""
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from flask import Flask, request, jsonify, send_from_directory
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from flask_cors import CORS
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from flasgger import Swagger
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import os
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from datetime import datetime
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from dotenv import load_dotenv
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from services.ai_service import AIService
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from services.field_service import FieldService
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from utils.response import success_response, error_response
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# 加载环境变量
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load_dotenv()
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app = Flask(__name__)
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CORS(app) # 允许跨域请求
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# 配置Swagger
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swagger_config = {
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"headers": [],
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"specs": [
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{
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"endpoint": "apispec",
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"route": "/apispec.json",
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"rule_filter": lambda rule: True,
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"model_filter": lambda tag: True,
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}
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],
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"static_url_path": "/flasgger_static",
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"swagger_ui": True,
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"specs_route": "/api-docs"
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}
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swagger_template = {
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"swagger": "2.0",
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"info": {
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"title": "智慧监督AI文书写作服务 API",
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"description": "基于大模型的智能文书生成服务，支持从非结构化文本中提取结构化字段数据",
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"version": "1.0.0",
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"contact": {
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"name": "API支持"
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}
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},
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"basePath": "/",
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"schemes": ["http", "https"],
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"tags": [
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{
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"name": "AI解析",
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"description": "AI字段提取相关接口"
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},
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{
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"name": "字段配置",
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"description": "字段配置查询接口"
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}
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]
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}
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swagger = Swagger(app, config=swagger_config, template=swagger_template)
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# 初始化服务
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ai_service = AIService()
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field_service = FieldService()
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@app.route('/')
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def index():
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"""返回测试页面"""
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return send_from_directory('static', 'index.html')
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@app.route('/api/ai/extract', methods=['POST'])
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def extract():
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"""
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AI字段提取接口
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从输入的非结构化文本中提取结构化字段数据
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---
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tags:
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- AI解析
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summary: 从输入数据中提取结构化字段
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description: 使用AI大模型从输入文本中提取结构化字段，支持多种业务类型
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consumes:
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- application/json
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produces:
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- application/json
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parameters:
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- in: body
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name: body
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description: 请求参数
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required: true
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schema:
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type: object
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required:
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- businessType
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- inputData
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properties:
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businessType:
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type: string
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description: 业务类型
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example: INVESTIGATION
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inputData:
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type: array
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description: 输入数据列表
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items:
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type: object
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properties:
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fieldCode:
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type: string
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description: 字段编码
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example: clue_info
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fieldValue:
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type: string
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description: 字段值（原始文本）
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example: 被举报用户名称是张三，年龄30岁，某公司总经理
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responses:
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200:
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description: 解析成功
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schema:
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type: object
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properties:
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code:
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type: integer
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description: 响应码，0表示成功
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example: 0
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data:
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type: object
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properties:
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outData:
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type: array
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description: 提取的字段列表
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items:
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type: object
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properties:
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fieldCode:
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type: string
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description: 字段编码
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example: target_name
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fieldValue:
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type: string
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description: 提取的字段值
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example: 张三
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msg:
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type: string
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description: 响应消息
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example: ok
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isSuccess:
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type: boolean
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description: 是否成功
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example: true
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timestamp:
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type: string
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description: 时间戳
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errorMsg:
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type: string
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description: 错误信息（成功时为空）
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400:
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description: 请求参数错误
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schema:
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type: object
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properties:
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code:
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type: integer
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example: 400
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errorMsg:
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type: string
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example: 请求参数不能为空
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isSuccess:
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type: boolean
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example: false
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1001:
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description: 业务类型不存在
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schema:
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type: object
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properties:
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code:
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type: integer
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example: 1001
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errorMsg:
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type: string
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example: 未找到业务类型 INVESTIGATION 对应的字段配置
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isSuccess:
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type: boolean
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example: false
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2001:
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description: AI解析超时或发生错误
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schema:
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type: object
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properties:
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code:
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type: integer
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example: 2001
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errorMsg:
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type: string
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example: AI解析超时或发生错误
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isSuccess:
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type: boolean
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example: false
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2002:
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description: AI解析失败
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schema:
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type: object
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properties:
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code:
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type: integer
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example: 2002
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errorMsg:
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type: string
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example: AI解析失败，请检查输入文本质量
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isSuccess:
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type: boolean
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example: false
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"""
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try:
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data = request.get_json()
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# 验证请求参数
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if not data:
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return error_response(400, "请求参数不能为空")
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business_type = data.get('businessType')
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input_data = data.get('inputData', [])
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if not business_type:
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return error_response(400, "businessType参数不能为空")
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if not input_data or not isinstance(input_data, list):
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return error_response(400, "inputData参数必须是非空数组")
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# 获取业务类型对应的输出字段
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output_fields = field_service.get_output_fields_by_business_type(business_type)
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if not output_fields:
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return error_response(1001, f"未找到业务类型 {business_type} 对应的字段配置")
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# 构建AI提示词
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prompt = field_service.build_extract_prompt(input_data, output_fields, business_type)
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# 调用AI服务进行解析
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ai_result = ai_service.extract_fields(prompt, output_fields)
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if not ai_result:
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return error_response(2002, "AI解析失败，请检查输入文本质量")
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# 构建返回数据
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out_data = []
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for field in output_fields:
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field_code = field['field_code']
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field_value = ai_result.get(field_code, '')
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out_data.append({
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'fieldCode': field_code,
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'fieldValue': field_value
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})
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return success_response({'outData': out_data})
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except Exception as e:
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return error_response(2001, f"AI解析超时或发生错误: {str(e)}")
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@app.route('/api/fields', methods=['GET'])
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def get_fields():
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"""
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获取字段配置接口
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获取指定业务类型的输入和输出字段配置
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---
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tags:
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- 字段配置
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summary: 获取字段配置
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description: 获取指定业务类型的输入字段和输出字段配置，用于测试页面展示
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produces:
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- application/json
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parameters:
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- in: query
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name: businessType
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type: string
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required: false
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default: INVESTIGATION
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description: 业务类型
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example: INVESTIGATION
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responses:
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200:
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description: 获取成功
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schema:
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type: object
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properties:
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code:
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type: integer
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description: 响应码，0表示成功
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example: 0
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data:
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type: object
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properties:
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fields:
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type: object
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properties:
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input_fields:
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type: array
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description: 输入字段列表
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items:
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type: object
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properties:
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id:
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type: integer
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description: 字段ID
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name:
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type: string
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description: 字段名称
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example: 线索信息
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field_code:
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type: string
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description: 字段编码
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example: clue_info
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field_type:
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type: integer
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description: 字段类型（1=输入字段，2=输出字段）
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example: 1
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output_fields:
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type: array
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description: 输出字段列表
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items:
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type: object
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properties:
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id:
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type: integer
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description: 字段ID
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name:
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type: string
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description: 字段名称
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example: 被核查人姓名
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field_code:
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type: string
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description: 字段编码
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example: target_name
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field_type:
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type: integer
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description: 字段类型（1=输入字段，2=输出字段）
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example: 2
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msg:
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type: string
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description: 响应消息
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example: ok
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isSuccess:
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type: boolean
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description: 是否成功
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example: true
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500:
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description: 服务器错误
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schema:
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type: object
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properties:
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code:
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type: integer
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example: 500
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errorMsg:
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type: string
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example: 获取字段配置失败
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isSuccess:
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type: boolean
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example: false
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"""
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try:
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business_type = request.args.get('businessType', 'INVESTIGATION')
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fields = field_service.get_fields_by_business_type(business_type)
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return success_response({'fields': fields})
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except Exception as e:
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return error_response(500, f"获取字段配置失败: {str(e)}")
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if __name__ == '__main__':
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# 确保static目录存在
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os.makedirs('static', exist_ok=True)
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port = int(os.getenv('PORT', 7500))
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debug = os.getenv('DEBUG', 'False').lower() == 'true'
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print(f"服务启动在 http://localhost:{port}")
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print(f"测试页面: http://localhost:{port}/")
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print(f"Swagger API文档: http://localhost:{port}/api-docs")
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app.run(host='0.0.0.0', port=port, debug=debug)
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