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