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from config import *
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from zhipuai import ZhipuAI
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def create_chat_completion(messages, model=MODEL):
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"""
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Create a chat completion using the provided messages and model.
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Parameters:
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messages (list): A list of message dictionaries to pass to the model.
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model (str): The model name to use.
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Returns:
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response (dict): The response from the chat completion endpoint.
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"""
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response = client.chat.completions.create(
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model=model,
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stream=False,
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messages=messages,
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temperature=0,
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seed=42,
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)
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return response
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def create_chat_completion_change_zhushi(fact_info, table_info, model=MODEL):
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few_shot_examples = """
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user:已查询获得事实: ('预处理程序通过表格：ConstantDB.SecuMain 查询到以下内容：\n [\n {\n "InnerCode": 86721,\n "CompanyCode": 98001,\n "SecuCode": "601128"}\n] \n', ['ConstantDB.SecuMain'])
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获取的表结构如下:{'数据表名': 'AStockOperationsDB.LC_Staff', '数据表结构': '\n列名 注释 数据示例 \n\nID ID 619397672060 \nCompanyCode 公司代码 159 \nEndDate 日期 2019-06-30 12:00:00.000]}
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assistant:{'数据表名': 'AStockOperationsDB.LC_Staff', '数据表结构': '\n列名 注释 数据示例 \n\nID ID 619397672060 \nCompanyCode 公司代码 98001 \nEndDate 日期 2019-06-30 12:00:00.000]}
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"""
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template = """
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你将根据“已查询获得事实”，更新“获取的表结构”里对应的数据示例列且对应的行的值，没有则不更新。只输出更新后的表结构，不需要额外解释和过程。
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以下是几个示例：{few_shot_examples}
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已查询获得事实: {fact_info}
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获取的表结构如下：{table_info}
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"""
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def format_prompt(few_shot_examples, fact_info, table_info, template):
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return template.format(few_shot_examples=few_shot_examples,
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fact_info=fact_info,
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table_info=table_info)
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formatted_prompt = format_prompt(few_shot_examples, fact_info, table_info, template)
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messages = [
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{'role': 'system', 'content': '你将根据“已查询获得事实”，更新“获取的表结构”里对应的数据示例列且对应的行的值，没有则不更新。只输出更新后的表结构，不需要额外解释和过程。'},
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{'role': 'user', 'content': formatted_prompt}
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]
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response = client.chat.completions.create(
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model=model,
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stream=False,
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messages=messages,
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temperature=0,
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seed=42,
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)
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result = response.choices[0].message.content
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return result
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