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Guardrails

GuardrailsOutputParser #

Bases: ChainableOutputParser

Guardrails output parser.

Source code in llama-index-integrations/output_parsers/llama-index-output-parsers-guardrails/llama_index/output_parsers/guardrails/base.py 
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class GuardrailsOutputParser(ChainableOutputParser):
    """Guardrails output parser."""

    def __init__(
        self,
        guard: Guard,
        llm: Optional["BaseLLM"] = None,
        format_key: Optional[str] = None,
    ):
        """Initialize a Guardrails output parser."""
        self.guard: Guard = guard
        self.llm = llm
        self.format_key = format_key

    @classmethod
    @deprecated(version="0.8.46")
    def from_rail(
        cls, rail: str, llm: Optional["BaseLLM"] = None
    ) -> "GuardrailsOutputParser":
        """From rail."""
        if Guard is None:
            raise ImportError(
                "Guardrails is not installed. Run `pip install guardrails-ai`. "
            )

        return cls(Guard.from_rail(rail), llm=llm)

    @classmethod
    @deprecated(version="0.8.46")
    def from_rail_string(
        cls, rail_string: str, llm: Optional["BaseLLM"] = None
    ) -> "GuardrailsOutputParser":
        """From rail string."""
        if Guard is None:
            raise ImportError(
                "Guardrails is not installed. Run `pip install guardrails-ai`. "
            )

        return cls(Guard.from_rail_string(rail_string), llm=llm)

    def parse(
        self,
        output: str,
        llm: Optional["BaseLLM"] = None,
        num_reasks: Optional[int] = 1,
        *args: Any,
        **kwargs: Any
    ) -> Any:
        """Parse, validate, and correct errors programmatically."""
        llm = llm or self.llm
        llm_fn = get_callable(llm)

        return self.guard.parse(
            output, llm_api=llm_fn, num_reasks=num_reasks, *args, **kwargs
        )

    def format(self, query: str) -> str:
        """Format a query with structured output formatting instructions."""
        output_schema_text = deepcopy(self.guard.rail.prompt)

        # Add format instructions here.
        format_instructions_tmpl = self.guard.raw_prompt.format_instructions
        # NOTE: output_schema is fixed
        format_instructions = format_instructions_tmpl.format(
            output_schema=output_schema_text
        )

        if self.format_key is not None:
            fmt_query = query.format(**{self.format_key: format_instructions})
        else:
            fmt_query = query + "\n\n" + format_instructions

        return fmt_query

from_rail classmethod #

from_rail(rail: str, llm: Optional[BaseLLM] = None) -> GuardrailsOutputParser

From rail.

Source code in llama-index-integrations/output_parsers/llama-index-output-parsers-guardrails/llama_index/output_parsers/guardrails/base.py 
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@classmethod
@deprecated(version="0.8.46")
def from_rail(
    cls, rail: str, llm: Optional["BaseLLM"] = None
) -> "GuardrailsOutputParser":
    """From rail."""
    if Guard is None:
        raise ImportError(
            "Guardrails is not installed. Run `pip install guardrails-ai`. "
        )

    return cls(Guard.from_rail(rail), llm=llm)

from_rail_string classmethod #

from_rail_string(rail_string: str, llm: Optional[BaseLLM] = None) -> GuardrailsOutputParser

From rail string.

Source code in llama-index-integrations/output_parsers/llama-index-output-parsers-guardrails/llama_index/output_parsers/guardrails/base.py 
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@classmethod
@deprecated(version="0.8.46")
def from_rail_string(
    cls, rail_string: str, llm: Optional["BaseLLM"] = None
) -> "GuardrailsOutputParser":
    """From rail string."""
    if Guard is None:
        raise ImportError(
            "Guardrails is not installed. Run `pip install guardrails-ai`. "
        )

    return cls(Guard.from_rail_string(rail_string), llm=llm)

parse #

parse(output: str, llm: Optional[BaseLLM] = None, num_reasks: Optional[int] = 1, *args: Any, **kwargs: Any) -> Any

Parse, validate, and correct errors programmatically.

Source code in llama-index-integrations/output_parsers/llama-index-output-parsers-guardrails/llama_index/output_parsers/guardrails/base.py 
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def parse(
    self,
    output: str,
    llm: Optional["BaseLLM"] = None,
    num_reasks: Optional[int] = 1,
    *args: Any,
    **kwargs: Any
) -> Any:
    """Parse, validate, and correct errors programmatically."""
    llm = llm or self.llm
    llm_fn = get_callable(llm)

    return self.guard.parse(
        output, llm_api=llm_fn, num_reasks=num_reasks, *args, **kwargs
    )

format #

format(query: str) -> str

Format a query with structured output formatting instructions.

Source code in llama-index-integrations/output_parsers/llama-index-output-parsers-guardrails/llama_index/output_parsers/guardrails/base.py 
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def format(self, query: str) -> str:
    """Format a query with structured output formatting instructions."""
    output_schema_text = deepcopy(self.guard.rail.prompt)

    # Add format instructions here.
    format_instructions_tmpl = self.guard.raw_prompt.format_instructions
    # NOTE: output_schema is fixed
    format_instructions = format_instructions_tmpl.format(
        output_schema=output_schema_text
    )

    if self.format_key is not None:
        fmt_query = query.format(**{self.format_key: format_instructions})
    else:
        fmt_query = query + "\n\n" + format_instructions

    return fmt_query