python - Exception has occurred: ValueError in langchain - Stack Overflow

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def standardize_column_names(df, target_columns):
    """
    Uses a language model to standardize column names dynamically based on semantic similarity.
    Args:
        df (pd.DataFrame): The DataFrame whose columns need standardization.
        target_columns (list): A list of target column names to map the DataFrame's columns to.
    Returns:
        pd.DataFrame: DataFrame with standardized column names.
    """
    raw_columns = list(df.columns)  # Extract the raw column names
    raw_columns_str = ", ".join(raw_columns)  # Convert to a comma-separated string
    target_columns_str = ", ".join(target_columns)  # Convert target columns to a string

    # Define the LLM prompt
    prompt = PromptTemplate(
        input_variables=["raw_columns", "target_columns"],  # Match keys exactly with dictionary passed to `invoke`
        template=(
            "You are tasked with standardizing column names. Here are the raw column names:\n"
            "{raw_columns}\n"
            "And here is the list of target column names to map to:\n"
            "{target_columns}\n"
            "Provide a mapping of raw column names to target column names as a dictionary in this format:\n"
            "{'raw_column': 'target_column', ...}"
        ),
    )

    # Initialize LLMChain
    chain = LLMChain(llm=llm, prompt=prompt)
    try:
        # Use `invoke` with correctly matched keys
        response = chain.invoke({"raw_columns": raw_columns_str, "target_columns": target_columns_str})
        mapping_result = response["text"]  # Extract the LLM's generated text
        column_mapping = eval(mapping_result)  # Convert the string response into a Python dictionary
    except Exception as e:
        raise ValueError(f"Error in LLM-based column mapping: {e}")

    # Apply the generated mapping to rename columns
    df.rename(columns=column_mapping, inplace=True)
    return df

The above code is projecting an error:

Exception has occurred: ValueError
Error in LLM-based column mapping: Missing some input keys: {"'raw_column'"}
  File "/Users/pro/Desktop/Technology/Bicycle AI/Data_analysis_AI.py", line 57, in standardize_column_names
    response = chain.invoke({"raw_columns": raw_columns_str, "target_columns": target_columns_str})

I don't know why it is mapping like that one.

def standardize_column_names(df, target_columns):
    """
    Uses a language model to standardize column names dynamically based on semantic similarity.
    Args:
        df (pd.DataFrame): The DataFrame whose columns need standardization.
        target_columns (list): A list of target column names to map the DataFrame's columns to.
    Returns:
        pd.DataFrame: DataFrame with standardized column names.
    """
    raw_columns = list(df.columns)  # Extract the raw column names
    raw_columns_str = ", ".join(raw_columns)  # Convert to a comma-separated string
    target_columns_str = ", ".join(target_columns)  # Convert target columns to a string

    # Define the LLM prompt
    prompt = PromptTemplate(
        input_variables=["raw_columns", "target_columns"],  # Match keys exactly with dictionary passed to `invoke`
        template=(
            "You are tasked with standardizing column names. Here are the raw column names:\n"
            "{raw_columns}\n"
            "And here is the list of target column names to map to:\n"
            "{target_columns}\n"
            "Provide a mapping of raw column names to target column names as a dictionary in this format:\n"
            "{'raw_column': 'target_column', ...}"
        ),
    )

    # Initialize LLMChain
    chain = LLMChain(llm=llm, prompt=prompt)
    try:
        # Use `invoke` with correctly matched keys
        response = chain.invoke({"raw_columns": raw_columns_str, "target_columns": target_columns_str})
        mapping_result = response["text"]  # Extract the LLM's generated text
        column_mapping = eval(mapping_result)  # Convert the string response into a Python dictionary
    except Exception as e:
        raise ValueError(f"Error in LLM-based column mapping: {e}")

    # Apply the generated mapping to rename columns
    df.rename(columns=column_mapping, inplace=True)
    return df

The above code is projecting an error:

Exception has occurred: ValueError
Error in LLM-based column mapping: Missing some input keys: {"'raw_column'"}
  File "/Users/pro/Desktop/Technology/Bicycle AI/Data_analysis_AI.py", line 57, in standardize_column_names
    response = chain.invoke({"raw_columns": raw_columns_str, "target_columns": target_columns_str})

I don't know why it is mapping like that one.

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