zylon-ai/private-gpt · warning · ValueError

Output is not a string, number, pd.DataFrame, or chart

Error message

Output is not a string, number, pd.DataFrame, or chart

What it means

Raised by PandasAIOutput.get_output_value() when _determine_response_type() returns None or an unknown key: the value is none of DataFrame, ChartResponse, str, or a number type. _determine_response_type runs the type_checks list; a miss means the analysis produced an exotic value (dict, list, None, datetime, etc.) the dispatch table does not cover.

Source

Thrown at private_gpt/components/tabular/pandasai_service.py:184

            if check_func():
                return type_name

        return None

    def get_raw(self) -> Any | None:
        """Get the raw output value in its appropriate format."""
        handlers: dict[str, Callable[[], Any]] = {
            "dataframe": lambda: None,
            "chart": self.get_chart,
            "string": lambda: str(self.value),
            "number": lambda: None,
        }

        response_type = self._determine_response_type()
        if response_type in handlers:
            return handlers[response_type]()

        raise ValueError("Output is not a string, number, pd.DataFrame, or chart")

    def __str__(self) -> str:
        """Convert the output to a string representation."""
        str_converters: dict[str, Callable[[], str]] = {
            "dataframe": lambda: (
                df_to_minimal_markdown(self.get_dataframe()) or "No results."
            ),
            "chart": lambda: (
                "Generated chart successfully. Plot was attached to the conversation."
                "Don't create placeholders for charts, just reply that the chart was generated."
            ),
            "string": lambda: str(self.value),
            "number": lambda: format_number(self.value),
        }

        response_type = self._determine_response_type()
        if response_type and response_type in str_converters:
            return str_converters[response_type]()

View on GitHub (pinned to 4a030776a3)

Solutions

  1. Log repr(self.value) to identify the uncovered type.
  2. Handle dict/list by str()-ing or json-serializing before dispatch, or extend type_checks in a subclass.
  3. Prompt the model to end with an explicit DataFrame/string/number expression.
  4. Fall back to str(output) for display when get_output_value() raises.

Example fix

# before
value = output.get_output_value()  # ValueError

# after
try:
    value = output.get_output_value()
except ValueError:
    value = str(output)
Defensive patterns

Strategy: fallback

Validate before calling

SUPPORTED = (str, int, float, complex, bool)

def is_representable(output) -> bool:
    import pandas as pd
    v = output.value
    return isinstance(v, pd.DataFrame) or output.is_chart() or isinstance(v, SUPPORTED)

Type guard

def classify_output(output):
    t = output._determine_response_type()
    return t if t in {"dataframe", "chart", "string", "number"} else "other"

Try / catch

try:
    value = output.get_output_value()
except ValueError:
    value = str(output)

Prevention

When it happens

Trigger: Generated code's final value is a dict, list, tuple, None, or a numpy type not matched by the number check; execution produced no value at all so self.value is None.

Common situations: LLM returns a list of tuples or a dict of aggregates; model assigns to a variable instead of leaving a final expression so value is None; pandasai version change altering what value is propagated.

Related errors

AI-assisted analysis of zylon-ai/private-gpt@4a030776a3 (2026-08-15). Data as JSON: /api/errors/e02518fd2ec30637. Report an issue: GitHub.