541 lines
28 KiB
Python
541 lines
28 KiB
Python
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from flask import Flask, jsonify, request
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from pydantic import BaseModel, Field
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from werkzeug.exceptions import HTTPException
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from typing import List
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from pydantic import ValidationError
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from intentRecognition import IntentRecognition
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from slotRecognition import SlotRecognition
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from utils import CheckResult, load_standard_name, generate_project_prompt, \
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load_standard_data, text_to_pinyin, multiple_standardize_single_name, \
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standardize_company_and_projectDepartment
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from constants import PROJECT_NAME, PROJECT_DEPARTMENT, SIMILARITY_VALUE, IMPLEMENTATION_ORG, RISK_LEVEL
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from langchain_openai import OpenAIEmbeddings
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from config import *
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# MODEL_ERNIE_PATH = R"../ernie/output/checkpoint-16470"
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# MODEL_UIE_PATH = R"../uie/output_temp/checkpoint-17060"
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# 类别名称列表
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labels = [
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"天气查询", "互联网查询", "页面切换", "日计划数量查询", "周计划数量查询",
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"日计划作业内容", "周计划作业内容", "施工人数", "作业考勤人数", "知识问答",
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"通用对话", "作业面查询","班组人数查询","班组数查询","作业面内容","班组详情"
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]
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# 标签映射
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label_map = {
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0: 'O', # 非实体
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1: 'B-date', 14: 'I-date',
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2: 'B-projectName', 15: 'I-projectName',
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3: 'B-projectType', 16: 'I-projectType',
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4: 'B-constructionUnit', 17: 'I-constructionUnit',
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5: 'B-implementationOrganization', 18: 'I-implementationOrganization',
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6: 'B-projectDepartment', 19: 'I-projectDepartment',
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7: 'B-projectManager', 20: 'I-projectManager',
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8: 'B-subcontractor', 21: 'I-subcontractor',
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9: 'B-teamLeader', 22: 'I-teamLeader',
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10: 'B-riskLevel', 23: 'I-riskLevel',
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11: 'B-page', 24: 'I-page',
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12: 'B-operating', 25: 'I-operating',
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13: 'B-teamName', 26: 'I-teamName',
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}
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# # 初始化工具类
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# intent_recognizer = IntentRecognition(MODEL_ERNIE_PATH, labels)
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#
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# # 初始化槽位识别工具类
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# slot_recognizer = SlotRecognition(MODEL_UIE_PATH, label_map)
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# 设置Flask应用
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#标准公司名和项目名
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standard_company_program = load_standard_data("./standard_data/standard_company_program.json")
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# 标准工程名,标准工程名拼音和工程名映射，标准工程名拼音
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standard_project_name_list = load_standard_name('./standard_data/standard_project.txt')
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pinyin_to_standard_project_name_map = {text_to_pinyin(kw): kw for kw in standard_project_name_list}
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standard_project_name_pinyin_list = list(pinyin_to_standard_project_name_map.keys())
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#标准分公司名，标准分公司名拼音和分公司名映射，标公司名拼音
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standard_company_name_list = list(standard_company_program.keys())
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pinyin_to_standard_company_name_map = {text_to_pinyin(kw): kw for kw in standard_company_name_list}
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standard_company_name_pinyin_list = list(pinyin_to_standard_company_name_map.keys())
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print(f"标准化的工程名是：{standard_project_name_list}", flush=True)
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print(f"pinyin标准化的工程名是 list：{standard_project_name_pinyin_list}", flush=True)
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print(f"pinyin-工程民对应关系 map：{pinyin_to_standard_company_name_map}", flush=True)
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app = Flask(__name__)
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# 统一的异常处理函数
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@app.errorhandler(Exception)
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def handle_exception(e):
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"""统一异常处理"""
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if isinstance(e, HTTPException):
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return jsonify({
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"error": {
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"type": e.name,
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"message": e.description,
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"status_code": e.code
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}
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}), e.code
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return jsonify({
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"error": {
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"type": "InternalServerError",
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"message": str(e)
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}
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}), 500
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def validate_user(data):
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"""验证用户ID"""
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if data.get("user_id") != '3bb66776-1722-4c36-b14a-73dd210fe750':
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return jsonify(
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code=401,
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msg='权限验证失败，请联系接口开发人员',
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label=-1,
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probability=-1
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), 401
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return None
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class LabelMessage(BaseModel):
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text: str = Field(..., description="消息内容")
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user_id: str = Field(..., description="消息内容")
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# 每条消息的结构
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class Message(BaseModel):
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role: str = Field(..., description="消息内容")
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content: str = Field(..., description="消息内容")
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# 请求数据的结构
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class RequestData(BaseModel):
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messages: List[Message] = Field(..., description="消息列表")
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user_id: str = Field(..., description="用户ID")
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# 意图识别
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@app.route('/intent_reco', methods=['POST'])
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def intent_reco():
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"""意图识别"""
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try:
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# 获取请求中的 JSON 数据
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data = request.get_json()
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request_data = LabelMessage(**data) # Pydantic 会验证数据结构
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text = request_data.text
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user_id = request_data.user_id
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# 检查必需字段
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if not text:
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return jsonify({"error": "text is required"}), 400
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if not user_id:
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return jsonify({"error": "user_id is required"}), 400
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# 验证用户ID
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user_validation_error = validate_user(data)
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if user_validation_error:
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return user_validation_error
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# 调用predict方法进行意图识别
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predicted_label, predicted_probability, predicted_id = intent_recognizer.predict(text)
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return jsonify(
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code=200,
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msg="成功",
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int=predicted_id,
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label=predicted_label,
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probability=float(predicted_probability)
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)
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except Exception as e:
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return jsonify({"error": str(e)}), 500
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# 槽位抽取
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@app.route('/slot_reco', methods=['POST'])
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def slot_reco():
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"""槽位识别"""
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try:
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# 获取请求中的 JSON 数据
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data = request.get_json()
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request_data = LabelMessage(**data) # Pydantic 会验证数据结构
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text = request_data.text
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user_id = request_data.user_id
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# 检查必需字段
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if not text:
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return jsonify({"error": "text is required"}), 400
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if not user_id:
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return jsonify({"error": "user_id is required"}), 400
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# 验证用户ID
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user_validation_error = validate_user(data)
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if user_validation_error:
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return user_validation_error
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# 调用 recognize 方法进行槽位识别
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entities = slot_recognizer.recognize(text)
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return jsonify(
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code=200,
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msg="成功",
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slot=entities)
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except Exception as e:
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return jsonify({"error": str(e)}), 500
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@app.route('/agent', methods=['POST'])
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def agent():
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try:
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data = request.get_json()
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except Exception as e:
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print(f"body不是一个有效的json")
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return jsonify({"error": str(e)}), 500 # 捕捉其他错误并返回
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try:
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# 使用 Pydantic 来验证数据结构
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request_data = RequestData(**data) # Pydantic 会验证数据结构
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messages = request_data.messages
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user_id = request_data.user_id
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# 检查必需字段是否存在
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if not messages:
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return jsonify({"error": "messages is required"}), 400
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if not user_id:
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return jsonify({"error": "user_id is required"}), 400
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# 验证用户ID（假设这个函数已经定义）
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user_validation_error = validate_user(data)
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if user_validation_error:
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return user_validation_error
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if len(messages) == 1: # 首轮
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query = messages[0].content # 使用 Message 对象的 .content 属性
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# 先进行意图识别
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predicted_label, predicted_probability, predicted_id = intent_recognizer.predict(query)
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# 再进行槽位抽取
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entities = slot_recognizer.recognize(query)
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print(
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f"第一轮意图识别后的label:{predicted_label}, id:{predicted_id},槽位抽取后的实体：{entities},message:{messages}",flush=True)
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# 多轮
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else:
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res = extract_multi_chat(messages)
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predicted_label, predicted_probability, predicted_id = intent_recognizer.predict(res)
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#0:天气，1：互联网查询，9：知识问答，10：通用对话
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if predicted_id in [0, 1, 9, 10]:
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print(f"多轮意图识别后的label:{predicted_label}, id:{predicted_id},message:{messages}",
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flush=True)
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return jsonify({
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"code": 200, "msg": "成功",
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"answer": {"int": predicted_id, "label": predicted_label, "probability": predicted_probability},
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"finalQuery": res
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})
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entities = slot_recognizer.recognize(res)
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print(
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f"多轮意图识别后的label:{predicted_label}, id:{predicted_id},槽位抽取后的实体：{entities},message:{messages}",flush=True)
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#必须槽位缺失检查
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status, sk = check_lost(predicted_id, entities)
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if status == CheckResult.NEEDS_MORE_ROUNDS:
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return jsonify({"code": 10001, "msg": "成功",
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"answer": {"miss": sk},
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})
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#工程名和项目名标准化
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result, information = check_project_standard_slot(predicted_id, entities)
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if result == CheckResult.NEEDS_MORE_ROUNDS:
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return jsonify({
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"code": 10001, "msg": "成功",
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"answer": {"miss": information},
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})
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return jsonify({
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"code": 200, "msg": "成功",
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"answer": {"int": predicted_id, "label": predicted_label, "probability": predicted_probability,
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"slot": entities},
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})
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except ValidationError as e:
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return jsonify({"error": e.errors()}), 400 # 捕捉 Pydantic 错误并返回
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except Exception as e:
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return jsonify({"error": str(e)}), 500 # 捕捉其他错误并返回
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def extract_multi_chat(messages):
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from openai import OpenAI
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client = OpenAI(base_url=api_base_url, api_key=api_key)
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# 格式化对话历史
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chat_history = "\n".join([f"{msg.role}: {msg.content}" for msg in messages])
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prompt = f'''你是一个智能助手，需要从以下对话记录中提取用户最近一次提问的完整问题：
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1. **仅关注用户的最后一个问题**，无论之前用户提问了什么，**不要受到之前用户问题的影响**。
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2. **如果用户的最后一个问题包含指代词**（如“作业计划分别是什么”、“具体是哪2项”、“刚刚那个故事”、“明天呢”、“合肥中心变工程呢”等），请结合用户上一次的问题和**AI（助手）回答**，补充信息，使问题成为完整的句子。
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3. **如果用户的最后一个问题的主语是“公司”这个字眼（如“公司今天有多少四级风险作业计划”或“公司今天有多少4级风险的作业面”）则不要参考对话历史进行补全，保持用户原始表达，不要替换为具体的公司名，工程名或项目部名等。**
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4. **如果用户的最后一个问题本身是完整的**（即未使用上述2里的指代词），直接输出该问题，不要受前文影响。
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5. **如果问题缺少上下文信息**（如工程、项目部和时间等），仅在**最近的 AI 回答**提供了明确的上下文时进行补全，否则保持用户的原始输入，不要添加错误的补全信息。
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6. **如果用户的最新问题包含时间信息**（如“今天、明天、本周”），请确保其被保留，并且不改变时间表达方式。
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- **如果用户的提问本身省略了时间信息，但最近 AI 回答包含时间信息，则补全时间**。
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- **例如：用户问“具体是哪20项”时，最近 AI 回答是“今天送1分公司第二项目管理部有20项作业计划”，那么补全后的问题应为“今天送1分公司第二项目管理部具体是哪20项作业计划”**。
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7. **不要改写问题的主体和语序**，仅在需要时补全信息，避免误修改用户原始表达。
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8. 直接输出补全后的完整问题，不需要额外解释，也不需要输出“用户想了解的问题”这样的字眼。
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9. **当用户的最后一条消息使用了“第一个”、“第1个”、“第2个”……等指代方式，且上一条 AI 回复中列出了多个选项（如多个工程名、公司名、项目部等），你需要：**
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- 精确提取用户所指的序号（如“第3个”指第3个工程名、公司名或项目部名）；
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- 将该工程、公司或项目部的完整名称（包括括号中的编号）提取出来；
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|
|
- **用完整名称替换掉用户上一个问题中出现的简称或模糊表达，并保留用户问题中的其它部分（如时间、计划数、内容）不变**；
|
|||
|
|
- 示例：
|
|||
|
|
- 原始问题：`2025年南苑调相机检修(PROJ-2023-0179)今天有多少作业计划`
|
|||
|
|
- AI 回答：列出多个工程，第1个是`检修公司调相机一二次设备检修维护和改造服务框架-2025年南苑调相机检修(PROJ-2023-0179)`
|
|||
|
|
- 用户回复：“第1个”
|
|||
|
|
- 则最终提问应为：
|
|||
|
|
`检修公司调相机一二次设备检修维护和改造服务框架-2025年南苑调相机检修(PROJ-2023-0179)今天有多少作业计划`
|
|||
|
|
**对话记录：**
|
|||
|
|
{chat_history}
|
|||
|
|
|
|||
|
|
请提取并补全用户的最新问题：'''
|
|||
|
|
|
|||
|
|
|
|||
|
|
# prompt = f'''你是一个智能助手，需要从以下对话记录中提取用户最近一次提问的完整问题：
|
|||
|
|
# 1. **仅关注用户的最后一个问题**，无论之前用户提问了什么，**不要受到之前用户问题的影响**。
|
|||
|
|
# 2. **如果用户的最后一个问题包含指代词**（如“作业计划分别是什么”、“具体是哪2项”、“刚刚那个故事”、“明天呢”、“合肥中心变工程呢”等），请结合用户上一次的问题和**AI（助手）回答**，补充信息，使问题成为完整的句子。
|
|||
|
|
# 3. **如果用户的最后一个问题本身是完整的**（即未使用上述2里的指代词），直接输出该问题，不要受前文影响。
|
|||
|
|
# - **如果用户的最后一个问题包含“公司”字眼并且“公司”单独出现（如“公司今天有多少作业计划”)**,则不要参考对话历史进行补全，保持用户原始表达。
|
|||
|
|
# - **如果用户的最后一个问题里公司，工程，项目部等都不出现（如“今天有多少作业计划”），则不要参考对话历史进行补全，保持用户原始表达。
|
|||
|
|
# 4. **如果问题缺少上下文信息**（如工程、项目部和时间等），仅在**最近的 AI 回答**提供了明确的上下文时进行补全，否则保持用户的原始输入，不要添加错误的补全信息。
|
|||
|
|
# 5. **如果用户的最新问题包含时间信息**（如“今天、明天、本周”），请确保其被保留，并且不改变时间表达方式。
|
|||
|
|
# - **如果用户的提问本身省略了时间信息，但最近 AI 回答包含时间信息，则补全时间**。
|
|||
|
|
# - **例如：用户问“具体是哪20项”时，最近 AI 回答是“今天送1分公司第二项目管理部有20项作业计划”，那么补全后的问题应为“今天送1分公司第二项目管理部具体是哪20项作业计划”**。
|
|||
|
|
# 6. **不要改写问题的主体和语序**，仅在需要时补全信息，避免误修改用户原始表达。
|
|||
|
|
# 7. 直接输出补全后的完整问题，不需要额外解释，也不需要输出“用户想了解的问题”这样的字眼。
|
|||
|
|
#
|
|||
|
|
# **对话记录：**
|
|||
|
|
# {chat_history}
|
|||
|
|
#
|
|||
|
|
# 请提取并补全用户的最新问题：'''
|
|||
|
|
|
|||
|
|
message = [
|
|||
|
|
{"role": "system", "content": "你是一个智能助手，负责提取用户最近的问题，并自动补全缺失信息，使其成为完整的问题句子。"},
|
|||
|
|
{"role": "user", "content": prompt}
|
|||
|
|
]
|
|||
|
|
|
|||
|
|
response = client.chat.completions.create(
|
|||
|
|
messages=message,
|
|||
|
|
model=model_name,
|
|||
|
|
max_tokens=100,
|
|||
|
|
temperature=0.3, # 降低随机性，提高确定性
|
|||
|
|
stream=False
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
res = response.choices[0].message.content.strip()
|
|||
|
|
print(f"多轮意图后用户想要的问题是：{res}", flush=True)
|
|||
|
|
return res
|
|||
|
|
|
|||
|
|
# def multi_slot_recognizer(intention_id, messages):
|
|||
|
|
# from openai import OpenAI
|
|||
|
|
# client = OpenAI(base_url=api_base_url, api_key=api_key)
|
|||
|
|
#
|
|||
|
|
# # prompt = f'''
|
|||
|
|
# # 根据用户的输入{messages}，抽取出用户最近最想了解的一个问题，要求：保持客观真实，简单明了，不要多余解释和阐述，不需要输出如“用户想了解的问题”类似的字眼
|
|||
|
|
# # '''
|
|||
|
|
# prompt = f'''根据以下对话记录，提取用户最近一次提问的核心意图，根据关键信息和上下文的回答内容并且关注用户最后的问题，提取出的意图需表述为完整的问题句式：
|
|||
|
|
# 对话记录：{messages}'''
|
|||
|
|
#
|
|||
|
|
# message = [{"role": "system", "content": prompt}]
|
|||
|
|
# message.extend(messages)
|
|||
|
|
# # print(message)
|
|||
|
|
# response = client.chat.completions.create(
|
|||
|
|
# messages=message,
|
|||
|
|
# model=model_name,
|
|||
|
|
# max_tokens=1000,
|
|||
|
|
# temperature=0.001,
|
|||
|
|
# stream=False
|
|||
|
|
# )
|
|||
|
|
# res = response.choices[0].message.content
|
|||
|
|
#
|
|||
|
|
# print(f"多轮意图后用户想要的问题是{res}",flush=True)
|
|||
|
|
# entries = slot_recognizer.recognize(res)
|
|||
|
|
#
|
|||
|
|
# return entries
|
|||
|
|
|
|||
|
|
def check_lost(int_res, slot):
|
|||
|
|
#labels: ["天气查询","通用对话","页面切换","日计划数量查询","周计划数量查询","日计划作业内容","周计划作业内容","施工人数","作业考勤人数","知识问答"]
|
|||
|
|
mapping = {
|
|||
|
|
2: [['page'], ['app'], ['module']],
|
|||
|
|
3: [['date']],
|
|||
|
|
4: [['date']],
|
|||
|
|
5: [['date']],
|
|||
|
|
6: [['date']],
|
|||
|
|
7: [['date']],
|
|||
|
|
8: [['date']],
|
|||
|
|
11: [['date']],
|
|||
|
|
12: [['date']],
|
|||
|
|
13: [['date']],
|
|||
|
|
14: [['date']],
|
|||
|
|
15: [['date']],
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
intention_mapping = {2: "页面切换", 3: "日计划数量查询", 4: "周计划数量查询", 5: "日计划作业内容",
|
|||
|
|
6: "周计划作业内容", 7: "施工人数", 8: "作业考勤人数", 11: "作业面查询",
|
|||
|
|
12:"班组人数查询", 13:"班组数查询", 14:"作业面内容", 15:"班组详情"}
|
|||
|
|
if not mapping.__contains__(int_res):
|
|||
|
|
return 0, ""
|
|||
|
|
#提取的槽位信息
|
|||
|
|
cur_k = list(slot.keys())
|
|||
|
|
idx = -1
|
|||
|
|
idx_len = 99
|
|||
|
|
for i in range(len(mapping[int_res])):
|
|||
|
|
sk = mapping[int_res][i]
|
|||
|
|
#不在提取的槽位信息里，但是在必须槽位表里
|
|||
|
|
miss_params = [x for x in sk if x not in cur_k]
|
|||
|
|
#不在必须槽位表里，但是在提取的槽位信息里
|
|||
|
|
extra_params = [x for x in cur_k if x not in sk]
|
|||
|
|
if len(extra_params) >= 0 and len(miss_params) == 0:
|
|||
|
|
idx = i
|
|||
|
|
idx_len = 0
|
|||
|
|
break
|
|||
|
|
if len(miss_params) < idx_len:
|
|||
|
|
idx = i
|
|||
|
|
idx_len = len(miss_params)
|
|||
|
|
|
|||
|
|
if idx_len == 0: # 匹配通过
|
|||
|
|
return CheckResult.NO_MATCH, cur_k
|
|||
|
|
#符合当前意图的的必须槽位，但是不在提取的槽位信息里
|
|||
|
|
left = [x for x in mapping[int_res][idx] if x not in cur_k]
|
|||
|
|
print(f"符合当前意图的的必须槽位，但是不在提取的槽位信息里, {left}",flush=True)
|
|||
|
|
apologize_str = "非常抱歉，"
|
|||
|
|
if int_res == 2:
|
|||
|
|
return CheckResult.NEEDS_MORE_ROUNDS, f"{apologize_str}请问你想查询哪个页面？"
|
|||
|
|
elif int_res in [3, 4, 5, 6, 7, 8, 11, 12, 13, 14, 15]:
|
|||
|
|
return CheckResult.NEEDS_MORE_ROUNDS, f"{apologize_str}请问你想查询什么时间的{intention_mapping[int_res]}？"
|
|||
|
|
|
|||
|
|
|
|||
|
|
#标准化工程名
|
|||
|
|
def check_project_standard_slot(int_res, slot) -> tuple:
|
|||
|
|
intention_list = {3, 4, 5, 6, 7, 8, 11, 12, 13, 14, 15}
|
|||
|
|
if int_res not in intention_list:
|
|||
|
|
return CheckResult.NO_MATCH, ""
|
|||
|
|
|
|||
|
|
#项目名 当项目名存在时需要一定存在分公司（实施组织）名
|
|||
|
|
if PROJECT_DEPARTMENT in slot:
|
|||
|
|
if IMPLEMENTATION_ORG not in slot:
|
|||
|
|
return CheckResult.NEEDS_MORE_ROUNDS, "请补充该项目部所属的分公司名称"
|
|||
|
|
else:
|
|||
|
|
standard_company, matched_projectDepartment = standardize_company_and_projectDepartment(slot[IMPLEMENTATION_ORG], slot[PROJECT_DEPARTMENT], standard_company_name_list, standard_company_program, pinyin_to_standard_company_name_map)
|
|||
|
|
print(f"check_project_standard_slot : {slot[IMPLEMENTATION_ORG]}, {slot[PROJECT_DEPARTMENT]}")
|
|||
|
|
# if not standard_company:
|
|||
|
|
# return CheckResult.NEEDS_MORE_ROUNDS, f"未匹配到您说的分公司名：{slot[IMPLEMENTATION_ORG]}，请提供更准确的分公司名"
|
|||
|
|
# if not matched_projectDepartment:
|
|||
|
|
# return CheckResult.NEEDS_MORE_ROUNDS, f"未匹配到您说的项目名：{slot[PROJECT_DEPARTMENT]}，请提供更准确的项目名"
|
|||
|
|
# if len(standard_company) > 1:
|
|||
|
|
# prompt = generate_project_prompt(matched_projectDepartment)
|
|||
|
|
# return CheckResult.NEEDS_MORE_ROUNDS, prompt
|
|||
|
|
# if len(matched_projectDepartment) == 1:
|
|||
|
|
# slot[IMPLEMENTATION_ORG] = standard_company[0]
|
|||
|
|
# slot[PROJECT_DEPARTMENT] = matched_projectDepartment[0]
|
|||
|
|
# elif len(matched_projectDepartment) > 1:
|
|||
|
|
# prompt = generate_project_prompt(standard_company, original_name=slot[IMPLEMENTATION_ORG], type="分公司名")
|
|||
|
|
# return CheckResult.NEEDS_MORE_ROUNDS, prompt
|
|||
|
|
if not standard_company:
|
|||
|
|
return CheckResult.NEEDS_MORE_ROUNDS, f"未匹配到您说的分公司名：{slot[IMPLEMENTATION_ORG]}，请提供更准确的分公司名"
|
|||
|
|
if not matched_projectDepartment:
|
|||
|
|
return CheckResult.NEEDS_MORE_ROUNDS, f"未匹配到您说的项目名：{slot[PROJECT_DEPARTMENT]}，请提供更准确的项目名"
|
|||
|
|
if standard_company and len(matched_projectDepartment) == 1:
|
|||
|
|
slot[IMPLEMENTATION_ORG] = standard_company
|
|||
|
|
slot[PROJECT_DEPARTMENT] = matched_projectDepartment[0]
|
|||
|
|
elif standard_company and len(matched_projectDepartment) > 1:
|
|||
|
|
prompt = generate_project_prompt(matched_projectDepartment)
|
|||
|
|
return CheckResult.NEEDS_MORE_ROUNDS, prompt
|
|||
|
|
|
|||
|
|
#工程名和分公司名标准化
|
|||
|
|
for key, value in slot.items():
|
|||
|
|
if key == PROJECT_NAME:
|
|||
|
|
print(f"check_project_standard_slot original project : {slot[PROJECT_NAME]}")
|
|||
|
|
match_results = multiple_standardize_single_name(value, standard_project_name_list, standard_project_name_pinyin_list, pinyin_to_standard_project_name_map,20,70)
|
|||
|
|
print(f"standardize_single_name 工程名 ：result:{match_results}",flush=True)
|
|||
|
|
if match_results and len(match_results) == 1:
|
|||
|
|
slot[key] = match_results[0]
|
|||
|
|
else:
|
|||
|
|
prompt = generate_project_prompt(match_results, original_name=slot[PROJECT_NAME], type="工程名")
|
|||
|
|
return CheckResult.NEEDS_MORE_ROUNDS, prompt
|
|||
|
|
|
|||
|
|
if key == IMPLEMENTATION_ORG and slot[key] != "公司":
|
|||
|
|
print(f"check_project_standard_slot original company : {slot[IMPLEMENTATION_ORG]}")
|
|||
|
|
match_results = multiple_standardize_single_name(value, standard_company_name_list, standard_company_name_pinyin_list, pinyin_to_standard_company_name_map, lower_score=50, high_score=80, isArabicNumConv = True)
|
|||
|
|
print(f"check_project_standard_slot 分公司名： result:{match_results}",flush=True)
|
|||
|
|
if match_results and len(match_results) == 1:
|
|||
|
|
slot[key] = match_results[0]
|
|||
|
|
else:
|
|||
|
|
prompt = generate_project_prompt(match_results, original_name=slot[IMPLEMENTATION_ORG], type="分公司名")
|
|||
|
|
return CheckResult.NEEDS_MORE_ROUNDS, prompt
|
|||
|
|
if key == RISK_LEVEL:
|
|||
|
|
if slot[RISK_LEVEL] not in["2级","3级","4级","5级"] and slot[RISK_LEVEL] not in["二级","三级","四级","五级"]:
|
|||
|
|
return CheckResult.NEEDS_MORE_ROUNDS, "您查询的风险等级在系统中未找到，请确认风险等级后再次提问"
|
|||
|
|
|
|||
|
|
return CheckResult.NO_MATCH, ""
|
|||
|
|
|
|||
|
|
# test_cases = [
|
|||
|
|
# ("安徽宏源电力建设有限公司", "第三项目管理部"), # 期望返回所有"第三项目管理部"
|
|||
|
|
# ("安徽宏源电力建设有限公司", "第九项目部"), # 期望返回 "第九项目管理部"
|
|||
|
|
# ("顺安电网公司", "第二项目部"), # 期望匹配"顺安电网建设有限公司"下的"第二项目管理部"
|
|||
|
|
# ("送电一公司", "第三项目部"), # 期望返回"第三项目管理部"
|
|||
|
|
# ("送电2公司", "第三项目部"), # 期望返回"第三项目管理部"
|
|||
|
|
# ("消防分公司", "第七项目部"), # 期望返回"第七项目管理部
|
|||
|
|
# ("建筑分公司", "第七项目部"), # 期望返回"第七项目管理部"
|
|||
|
|
# ("建筑消防分公司", "第七项目部"), # 期望返回"第七项目管理部"
|
|||
|
|
# ("建筑分公司消防分公司", "第七项目部") # 期望返回"第七项目管理部"
|
|||
|
|
# ]
|
|||
|
|
#
|
|||
|
|
# for company, project in test_cases:
|
|||
|
|
# # result = standardize_company_and_project(company, project,standard_company_program)
|
|||
|
|
# result = standardize_company_and_projectDepartment(company, project,standard_company_name_list, standard_company_program, pinyin_to_standard_company_name_map)
|
|||
|
|
# # result = multiple_standardize_single_name("company", standard_project_name_list, standard_project_name_pinyin_list, pinyin_to_standard_project_name_map,40,70)
|
|||
|
|
# print(f"输入: {company}, {project} -> 输出: {result}")
|
|||
|
|
#
|
|||
|
|
# result = standardize_single_name("送电一公司", standard_company_name_list)
|
|||
|
|
# print(f"输入: 送一分公司-> 输出: {result}")
|
|||
|
|
#
|
|||
|
|
# prompt = generate_project_prompt(result, "分公司名")
|
|||
|
|
# print(f"prompt:{prompt}")
|
|||
|
|
#
|
|||
|
|
# result = standardize_single_name("合肥中心变", standard_project_name_list)
|
|||
|
|
# print(f"输入: 合肥中心变-> 输出: {result}")
|
|||
|
|
#
|
|||
|
|
# prompt = generate_project_prompt(result, "工程名")
|
|||
|
|
# print(f"prompt:{prompt}")
|
|||
|
|
|
|||
|
|
# result = standardize_single_name("合肥中心变", standard_project_name_list, 60, 75)
|
|||
|
|
# print(f"输入: 合肥中心变-> 输出: {result}")
|
|||
|
|
#
|
|||
|
|
# result = standardize_single_name("阜阳阜四变电站工程", standard_project_name_list, 60, 75)
|
|||
|
|
# print(f"输入: 阜阳阜四变电站工程-> 输出: {result}")
|
|||
|
|
#
|
|||
|
|
# result = standardize_single_name("芦集变电站", standard_project_name_list, 20, 50)
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# print(f"输入: 芦集变电站-> 输出: {result}")
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#
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# match_results = multiple_standardize_single_name("宋轶分公司", standard_company_name_list, standard_company_name_pinyin_list, pinyin_to_standard_company_name_map,75,80)
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# print(f"standardize_pinyin_single_name 输入: 宋轶分公司-> 输出: {match_results}")
|
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|
|
# #
|
|||
|
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# match_results = multiple_standardize_single_name("合肥中心变", standard_project_name_list, standard_project_name_pinyin_list, pinyin_to_standard_project_name_map,40,70)
|
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# print(f"standardize_pinyin_single_name 输入: 合肥中心变-> 输出: {match_results}")
|
|||
|
|
#
|
|||
|
|
# match_results = multiple_standardize_single_name("淮南安丰", standard_project_name_list, standard_project_name_pinyin_list, pinyin_to_standard_project_name_map,40,70)
|
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|
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# print(f"standardize_pinyin_single_name 输入: 淮南安丰工程-> 输出: {match_results}")
|
|||
|
|
#
|
|||
|
|
# match_results = multiple_standardize_single_name("芦集变电站", standard_project_name_list, standard_project_name_pinyin_list, pinyin_to_standard_project_name_map,20,70)
|
|||
|
|
# print(f"standardize_pinyin_single_name 输入: 芦集变电站-> 输出: {match_results}")
|
|||
|
|
#
|
|||
|
|
# company, project = standardize_company_and_projectDepartment("变电分公司","第一项目部", standard_company_name_list, standard_company_program, pinyin_to_standard_company_name_map)
|
|||
|
|
# print(f"company:{company}, project:{project}")
|
|||
|
|
#
|
|||
|
|
# company, project = standardize_company_and_projectDepartment("变电分公司","第十一项目部", standard_company_name_list, standard_company_program, pinyin_to_standard_company_name_map)
|
|||
|
|
# print(f"company:{company}, project:{project}")
|
|||
|
|
# company, project = standardize_company_and_projectDepartment("试验分公司","电缆班", standard_company_name_list, standard_company_program, pinyin_to_standard_company_name_map)
|
|||
|
|
# print(f"company:{company}, project:{project}")
|
|||
|
|
company, project = standardize_company_and_projectDepartment("宏源电力投资有限公司","第三项目部", standard_company_name_list, standard_company_program, pinyin_to_standard_company_name_map)
|
|||
|
|
print(f"company:{company}, project:{project}")
|
|||
|
|
#
|
|||
|
|
# if __name__ == '__main__':
|
|||
|
|
# app.run(host='0.0.0.0', port=18073, debug=True)
|