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85 lines
3.5 KiB
Python
85 lines
3.5 KiB
Python
import json
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from pilot.prompts.prompt_new import PromptTemplate
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from pilot.configs.config import Config
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from pilot.scene.base import ChatScene
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from pilot.scene.chat_data.chat_excel.excel_learning.out_parser import (
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LearningExcelOutputParser,
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)
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from pilot.common.schema import SeparatorStyle
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CFG = Config()
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_PROMPT_SCENE_DEFINE_EN = "You are a data analysis expert. "
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_DEFAULT_TEMPLATE_EN = """
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This is an example data，please learn to understand the structure and content of this data:
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{data_example}
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Explain the meaning and function of each column, and give a simple and clear explanation of the technical terms， If it is a Date column, please summarize the Date format like: yyyy-MM-dd HH:MM:ss.
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Please do not modify or translate the column names, make sure they are consistent with the given data column names.
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Provide some analysis options,please think step by step.
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Please return your answer in JSON format, the return format is as follows:
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{response}
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"""
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_PROMPT_SCENE_DEFINE_ZH = "你是一个数据分析专家. "
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_DEFAULT_TEMPLATE_ZH = """
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下面是一份示例数据，请学习理解该数据的结构和内容:
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{data_example}
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分析各列数据的含义和作用，并对专业术语进行简单明了的解释, 如果是时间类型请给出时间格式类似:yyyy-MM-dd HH:MM:ss.
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请不要修改或者翻译列名，确保和给出数据列名一致.
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提供一些分析方案思路，请一步一步思考。
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请以JSON格式返回您的答案，返回格式如下：
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{response}
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"""
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_RESPONSE_FORMAT_SIMPLE_ZH = {
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"DataAnalysis": "数据内容分析总结",
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"ColumnAnalysis": [{"column name1": "字段1介绍，专业术语解释(请尽量简单明了)"}],
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"AnalysisProgram": ["1.分析方案1，图表展示方式1", "2.分析方案2，图表展示方式2"],
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}
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_RESPONSE_FORMAT_SIMPLE_EN = {
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"DataAnalysis": "Data content analysis summary",
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"ColumnAnalysis": [{"column name1": "Introduction to Column 1 and explanation of professional terms (please try to be as simple and clear as possible)"}],
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"AnalysisProgram": ["1. Analysis plan 1, chart display type 1", "2. Analysis plan 2, chart display type 2"],
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}
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RESPONSE_FORMAT_SIMPLE =(_RESPONSE_FORMAT_SIMPLE_EN if CFG.LANGUAGE == "en" else _RESPONSE_FORMAT_SIMPLE_ZH)
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_DEFAULT_TEMPLATE = (
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_DEFAULT_TEMPLATE_EN if CFG.LANGUAGE == "en" else _DEFAULT_TEMPLATE_ZH
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)
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PROMPT_SCENE_DEFINE = (
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_PROMPT_SCENE_DEFINE_EN if CFG.LANGUAGE == "en" else _PROMPT_SCENE_DEFINE_ZH
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)
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PROMPT_SEP = SeparatorStyle.SINGLE.value
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PROMPT_NEED_NEED_STREAM_OUT = False
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# Temperature is a configuration hyperparameter that controls the randomness of language model output.
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# A high temperature produces more unpredictable and creative results, while a low temperature produces more common and conservative output.
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# For example, if you adjust the temperature to 0.5, the model will usually generate text that is more predictable and less creative than if you set the temperature to 1.0.
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PROMPT_TEMPERATURE = 0.5
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prompt = PromptTemplate(
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template_scene=ChatScene.ExcelLearning.value(),
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input_variables=["data_example"],
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response_format=json.dumps(RESPONSE_FORMAT_SIMPLE, ensure_ascii=False, indent=4),
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template_define=PROMPT_SCENE_DEFINE,
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template=_DEFAULT_TEMPLATE,
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stream_out=PROMPT_NEED_NEED_STREAM_OUT,
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output_parser=LearningExcelOutputParser(
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sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_NEED_STREAM_OUT
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),
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# example_selector=sql_data_example,
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temperature=PROMPT_TEMPERATURE,
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)
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CFG.prompt_template_registry.register(prompt, is_default=True)
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