Getting Key error for most use cases in a simple dataframe using open source lama3:8b - instruct model via ollama

[sktg84]

Getting Key error for most use cases in a simple dataframe using open source lama3:8b - instruct model via ollama

[dosubot]

Hey there, @sktg84! 👋 I'm here to help you out with any bugs or questions you have. I'm a bot that can assist with solving bugs, answering questions, and becoming a contributor. Let's tackle this issue together!

To resolve the KeyError related to the 'software_version' key and successfully parse the DataFrame while using the lama3:8b-instruct model via Ollama, you need to ensure that the 'software_version' key is included in the extras dictionary when calling the serialize method. Here is an example of how you can do this:

import pandas as pd
from pandasai.helpers.dataframe_serializer import DataframeSerializer, DataframeSerializerType

# Sample DataFrame
data = {
    'column1': [1, 2, 3],
    'column2': [4, 5, 6]
}
df = pd.DataFrame(data)

# Create an instance of DataframeSerializer
serializer = DataframeSerializer()

# Define the extras dictionary with the 'software_version' key
extras = {
    'index': 0,
    'type': 'example_type',
    'software_version': '1.0.0'  # Ensure this key is included
}

# Serialize the DataFrame to JSON
json_output = serializer.serialize(df, extras, type_=DataframeSerializerType.JSON)

print(json_output)

This code ensures that the 'software_version' key is present in the extras dictionary, which should prevent the KeyError and allow the DataFrame to be successfully parsed ^[1].

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[dosubot]

To resolve the KeyError: 'software_version', you need to ensure that the 'software_version' column exists in the DataFrame before performing operations like groupby. Since the code is auto-generated by the LLM, you can add a check in the execute_code method to handle this gracefully.

Here is an updated version of the execute_code method that includes a check for the 'software_version' column:

def execute_code(self, code: str, context: CodeExecutionContext) -> Any:
    """
    Execute the python code generated by LLMs to answer the question
    about the input dataframe. Run the code in the current context and return the
    result.

    Args:
        code (str): Python code to execute.
        context (CodeExecutionContext): Code Execution Context
                with prompt id and skills.

    Returns:
        Any: The result of the code execution. The type of the result depends
            on the generated code.

    """
    # List the required dfs, so we can avoid to run the connectors
    # if the code does not need them
    dfs = self._required_dfs(code)
    environment: dict = get_environment(self._additional_dependencies)
    environment["dfs"] = self._get_originals(dfs)
    if len(environment["dfs"]) == 1:
        environment["df"] = environment["dfs"][0]

    if self._config.direct_sql:
        environment["execute_sql_query"] = self._dfs[0].execute_direct_sql_query

    # Add skills to the env
    if context.skills_manager.used_skills:
        for skill_func_name in context.skills_manager.used_skills:
            skill = context.skills_manager.get_skill_by_func_name(skill_func_name)
            environment[skill_func_name] = skill

    # Check if 'software_version' column exists in the DataFrame
    if 'software_version' not in environment['df'].columns:
        raise KeyError("The 'software_version' column is missing from the DataFrame.")

    # Execute the code
    exec(code, environment)

    # Get the result
    if "result" not in environment:
        raise NoResultFoundError("No result returned")

    return environment["result"]

This code snippet adds a check to ensure that the 'software_version' column exists in the DataFrame before executing the code. If the column is missing, it raises a KeyError with a descriptive message ^[1].

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[sktg84]

this code is auto gen from llm.

[sktg84]

@dosu any updates?