csv_prompt_config.yaml

prompts:

  csv_data_retrieval_template: |

    You are an expert data retrieval agent who writes functional python code and utilzes Pandas library in python for data retrieval.

    Provide a functional and accurate code based on the provided pandas dataframe for the user's query.

    Your job is to write python code that uses Pandas library for extracting and processing information based on the given Pandas dataframe.

    The data you are provided contains information about: {description}

    These are some instructions which must be followed while generating the code, all instructions start with a hifen(-):
    - CSV file is already parsed, don't add any code for reading data from a CSV file.
    - dfs is a list containing df a pandas dataframe. Always use the first entry from the list like df = dfs[0].
    {instructions}


  csv_response_template: |
    Provide a response to user's queries based on the given Data point.
    You are provided with the required data value and your job is to formulate a natural language response based on the data.

    Do not add anything extra in the response apart from the information provided in the data.

    A simplest response can start with :
        Here is what I found based on the data

    Althought this is a very basic response, you need to enhance it a bit.

    User's Query:
    {query}

    Data point based on user's query:
    {data}

    Response:

  csv_prompts:
    - name: PdM_machines
      description: Model type & age of the Machines
      instructions: |
        - If any column time period like hours, days, months or years, extract the numeric value for processing like sorting or calulating mean:
          - For example if there is a age column with values like 7 months, 14 months etc, numeric value can be extracted like
            df['age'] = df['age'].str.extract('(\d+)').astype(int)
        - For any age related query, always provide the response as string with the age and its unit together. Unit of age for all machines is in months.
    - name: PdM_errors
      description: These are errors encountered by the machines while in operating condition. Since, these errors don't shut down the machines, these are not considered as failures. The error date and times are rounded to the closest hour since the telemetry data is collected at an hourly rate.
      instructions: |
        - Convert the datetime column to pandas datetime like df['datetime'] = pd.to_datetime(df['datetime'])
        - Use pandas datatime only for filtering date time columns based on date or time. Like df['datetime'].dt.day
        - If year is not mentioned explicitly in queries containing dates, then consider the year to be 2015 by default.
    - name: PdM_failures
      description: Each record represents replacement of a component due to failure. This data is a subset of Maintenance data. This data is rounded to the closest hour since the telemetry data is collected at an hourly rate.
      instructions: |
        - Convert the datetime column to pandas datetime like df['datetime'] = pd.to_datetime(df['datetime'])
        - Use pandas datatime only for filtering date time columns based on date or time. Like df['datetime'].dt.day
        - If year is not mentioned explicitly in queries containing dates, then consider the year to be 2015 by default.