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Guards

Guard​

class Guard(IGuard, Generic[OT])

The Guard class.

This class is the main entry point for using Guardrails. It can be initialized by one of the following patterns:

  • Guard().use(...)
  • Guard().use_many(...)
  • Guard.for_string(...)
  • Guard.for_pydantic(...)
  • Guard.for_rail(...)
  • Guard.for_rail_string(...)

The __call__ method functions as a wrapper around LLM APIs. It takes in an LLM API, and optional prompt parameters, and returns a ValidationOutcome class that contains the raw output from the LLM, the validated output, as well as other helpful information.

__init__​

def __init__(*,
             id: Optional[str] = None,
             name: Optional[str] = None,
             description: Optional[str] = None,
             validators: Optional[List[ValidatorReference]] = None,
             output_schema: Optional[Dict[str, Any]] = None)

Initialize the Guard with serialized validator references and an output schema.

Output schema must be a valid JSON Schema.

configure​

def configure(*,
              num_reasks: Optional[int] = None,
              tracer: Optional[Tracer] = None,
              allow_metrics_collection: Optional[bool] = None)

Configure the Guard.

Arguments:

  • num_reasks int, optional - The max times to re-ask the LLM if validation fails. Defaults to None.
  • tracer Tracer, optional - An OpenTelemetry tracer to use for sending traces to your OpenTelemetry sink. Defaults to None.
  • allow_metrics_collection bool, optional - Whether to allow Guardrails to collect anonymous metrics. Defaults to None, and falls back to waht is set via the guardrails configure command.

for_rail​

@classmethod
def for_rail(cls,
             rail_file: str,
             *,
             num_reasks: Optional[int] = None,
             tracer: Optional[Tracer] = None,
             name: Optional[str] = None,
             description: Optional[str] = None)

Create a Guard using a .rail file to specify the output schema, prompt, etc.

Arguments:

  • rail_file - The path to the .rail file.
  • num_reasks int, optional - The max times to re-ask the LLM if validation fails. Deprecated
  • tracer Tracer, optional - An OpenTelemetry tracer to use for metrics and traces. Defaults to None.
  • name str, optional - A unique name for this Guard. Defaults to gr- + the object id.
  • description str, optional - A description for this Guard. Defaults to None.

Returns:

An instance of the Guard class.

for_rail_string​

@classmethod
def for_rail_string(cls,
                    rail_string: str,
                    *,
                    num_reasks: Optional[int] = None,
                    tracer: Optional[Tracer] = None,
                    name: Optional[str] = None,
                    description: Optional[str] = None)

Create a Guard using a .rail string to specify the output schema, prompt, etc..

Arguments:

  • rail_string - The .rail string.
  • num_reasks int, optional - The max times to re-ask the LLM if validation fails. Deprecated
  • tracer Tracer, optional - An OpenTelemetry tracer to use for metrics and traces. Defaults to None.
  • name str, optional - A unique name for this Guard. Defaults to gr- + the object id.
  • description str, optional - A description for this Guard. Defaults to None.

Returns:

An instance of the Guard class.

for_pydantic​

@classmethod
def for_pydantic(cls,
                 output_class: ModelOrListOfModels,
                 *,
                 num_reasks: Optional[int] = None,
                 reask_messages: Optional[List[Dict]] = None,
                 messages: Optional[List[Dict]] = None,
                 tracer: Optional[Tracer] = None,
                 name: Optional[str] = None,
                 description: Optional[str] = None,
                 output_formatter: Optional[Union[str, BaseFormatter]] = None)

Create a Guard instance using a Pydantic model to specify the output schema.

Arguments:

  • output_class - (Union[Type[BaseModel], List[Type[BaseModel]]]): The pydantic model that describes the desired structure of the output.
  • messages List[Dict], optional - A list of messages to give to the llm. Defaults to None.
  • reask_messages List[Dict], optional - A list of messages to use during reasks. Defaults to None.
  • num_reasks int, optional - The max times to re-ask the LLM if validation fails. Deprecated
  • tracer Tracer, optional - An OpenTelemetry tracer to use for metrics and traces. Defaults to None.
  • name str, optional - A unique name for this Guard. Defaults to gr- + the object id.
  • description str, optional - A description for this Guard. Defaults to None.
  • output_formatter str | Formatter, optional - 'none' (default), 'jsonformer', or a Guardrails Formatter.

for_string​

@classmethod
def for_string(cls,
               validators: Sequence[Validator],
               *,
               string_description: Optional[str] = None,
               reask_messages: Optional[List[Dict]] = None,
               messages: Optional[List[Dict]] = None,
               num_reasks: Optional[int] = None,
               tracer: Optional[Tracer] = None,
               name: Optional[str] = None,
               description: Optional[str] = None)

Create a Guard instance for a string response.

Arguments:

  • validators - (List[Validator]): The list of validators to apply to the string output.
  • string_description str, optional - A description for the string to be generated. Defaults to None.
  • messages List[Dict], optional - A list of messages to pass to llm. Defaults to None.
  • reask_messages List[Dict], optional - A list of messages to use during reasks. Defaults to None.
  • num_reasks int, optional - The max times to re-ask the LLM if validation fails. Deprecated
  • tracer Tracer, optional - An OpenTelemetry tracer to use for metrics and traces. Defaults to None.
  • name str, optional - A unique name for this Guard. Defaults to gr- + the object id.
  • description str, optional - A description for this Guard. Defaults to None.

__call__​

@trace(name="/guard_call", origin="Guard.__call__")
def __call__(
        llm_api: Optional[Callable] = None,
        *args,
        prompt_params: Optional[Dict] = None,
        num_reasks: Optional[int] = 1,
        messages: Optional[List[Dict]] = None,
        metadata: Optional[Dict] = None,
        full_schema_reask: Optional[bool] = None,
        **kwargs
) -> Union[ValidationOutcome[OT], Iterator[ValidationOutcome[OT]]]

Call the LLM and validate the output.

Arguments:

  • llm_api - The LLM API to call (e.g. openai.completions.create or openai.Completion.acreate)
  • prompt_params - The parameters to pass to the prompt.format() method.
  • num_reasks - The max times to re-ask the LLM for invalid output.
  • messages - The message history to pass to the LLM.
  • metadata - Metadata to pass to the validators.
  • full_schema_reask - When reasking, whether to regenerate the full schema or just the incorrect values. Defaults to True if a base model is provided, False otherwise.

Returns:

ValidationOutcome

parse​

@trace(name="/guard_call", origin="Guard.parse")
def parse(llm_output: str,
          *args,
          metadata: Optional[Dict] = None,
          llm_api: Optional[Callable] = None,
          num_reasks: Optional[int] = None,
          prompt_params: Optional[Dict] = None,
          full_schema_reask: Optional[bool] = None,
          **kwargs) -> ValidationOutcome[OT]

Alternate flow to using Guard where the llm_output is known.

Arguments:

  • llm_output - The output being parsed and validated.
  • metadata - Metadata to pass to the validators.
  • llm_api - The LLM API to call (e.g. openai.completions.create or openai.Completion.acreate)
  • num_reasks - The max times to re-ask the LLM for invalid output.
  • prompt_params - The parameters to pass to the prompt.format() method.
  • full_schema_reask - When reasking, whether to regenerate the full schema or just the incorrect values.

Returns:

ValidationOutcome

error_spans_in_output​

def error_spans_in_output() -> List[ErrorSpan]

Get the error spans in the last output.

use​

@overload
def use(validator: Validator, *, on: str = "output") -> "Guard"

use​

@overload
def use(validator: Type[Validator],
        *args,
        on: str = "output",
        **kwargs) -> "Guard"

use​

def use(validator: UseValidatorSpec,
        *args,
        on: str = "output",
        **kwargs) -> "Guard"

Use a validator to validate either of the following:

  • The output of an LLM request
  • The message history

Arguments:

  • validator - The validator to use. Either the class or an instance.
  • on - The part of the LLM request to validate. Defaults to "output".

use_many​

@overload
def use_many(*validators: Validator, on: str = "output") -> "Guard"

use_many​

@overload
def use_many(*validators: UseManyValidatorTuple,
             on: str = "output") -> "Guard"

use_many​

def use_many(*validators: UseManyValidatorSpec, on: str = "output") -> "Guard"

Use multiple validators to validate results of an LLM request.

validate​

@trace(name="/guard_call", origin="Guard.validate")
def validate(llm_output: str, *args, **kwargs) -> ValidationOutcome[OT]

to_runnable​

def to_runnable() -> Runnable

Convert a Guard to a LangChain Runnable.

to_dict​

def to_dict() -> Dict[str, Any]

json_function_calling_tool​

def json_function_calling_tool(
        tools: Optional[list] = None) -> List[Dict[str, Any]]

Appends an OpenAI tool that specifies the output structure using JSON Schema for chat models.

from_dict​

@classmethod
def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional["Guard"]

AsyncGuard​

class AsyncGuard(Guard, Generic[OT])

The AsyncGuard class.

This class one of the main entry point for using Guardrails. It is initialized from one of the following class methods:

  • for_rail
  • for_rail_string
  • for_pydantic
  • for_string

The __call__ method functions as a wrapper around LLM APIs. It takes in an Async LLM API, and optional prompt parameters, and returns the raw output stream from the LLM and the validated output stream.

for_pydantic​

@classmethod
def for_pydantic(cls,
                 output_class: ModelOrListOfModels,
                 *,
                 messages: Optional[List[Dict]] = None,
                 num_reasks: Optional[int] = None,
                 reask_messages: Optional[List[Dict]] = None,
                 tracer: Optional[Tracer] = None,
                 name: Optional[str] = None,
                 description: Optional[str] = None)

for_string​

@classmethod
def for_string(cls,
               validators: Sequence[Validator],
               *,
               string_description: Optional[str] = None,
               messages: Optional[List[Dict]] = None,
               reask_messages: Optional[List[Dict]] = None,
               num_reasks: Optional[int] = None,
               tracer: Optional[Tracer] = None,
               name: Optional[str] = None,
               description: Optional[str] = None)

from_dict​

@classmethod
def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional["AsyncGuard"]

use​

def use(validator: UseValidatorSpec,
        *args,
        on: str = "output",
        **kwargs) -> "AsyncGuard"

use_many​

def use_many(*validators: UseManyValidatorSpec,
             on: str = "output") -> "AsyncGuard"

__call__​

@async_trace(name="/guard_call", origin="AsyncGuard.__call__")
async def __call__(
    llm_api: Optional[Callable[..., Awaitable[Any]]] = None,
    *args,
    prompt_params: Optional[Dict] = None,
    num_reasks: Optional[int] = 1,
    messages: Optional[List[Dict]] = None,
    metadata: Optional[Dict] = None,
    full_schema_reask: Optional[bool] = None,
    **kwargs
) -> Union[
        ValidationOutcome[OT],
        Awaitable[ValidationOutcome[OT]],
        AsyncIterator[ValidationOutcome[OT]],
]

Call the LLM and validate the output. Pass an async LLM API to return a coroutine.

Arguments:

  • llm_api - The LLM API to call (e.g. openai.completions.create or openai.chat.completions.create)
  • prompt_params - The parameters to pass to the prompt.format() method.
  • num_reasks - The max times to re-ask the LLM for invalid output.
  • messages - The message history to pass to the LLM.
  • metadata - Metadata to pass to the validators.
  • full_schema_reask - When reasking, whether to regenerate the full schema or just the incorrect values. Defaults to True if a base model is provided, False otherwise.

Returns:

The raw text output from the LLM and the validated output.

parse​

@async_trace(name="/guard_call", origin="AsyncGuard.parse")
async def parse(llm_output: str,
                *args,
                metadata: Optional[Dict] = None,
                llm_api: Optional[Callable[..., Awaitable[Any]]] = None,
                num_reasks: Optional[int] = None,
                prompt_params: Optional[Dict] = None,
                full_schema_reask: Optional[bool] = None,
                **kwargs) -> Awaitable[ValidationOutcome[OT]]

Alternate flow to using AsyncGuard where the llm_output is known.

Arguments:

  • llm_output - The output being parsed and validated.
  • metadata - Metadata to pass to the validators.
  • llm_api - The LLM API to call (e.g. openai.completions.create or openai.Completion.acreate)
  • num_reasks - The max times to re-ask the LLM for invalid output.
  • prompt_params - The parameters to pass to the prompt.format() method.
  • full_schema_reask - When reasking, whether to regenerate the full schema or just the incorrect values.

Returns:

The validated response. This is either a string or a dictionary, determined by the object schema defined in the RAILspec.

validate​

@async_trace(name="/guard_call", origin="AsyncGuard.validate")
async def validate(llm_output: str, *args,
                   **kwargs) -> Awaitable[ValidationOutcome[OT]]

ValidationOutcome​

class ValidationOutcome(IValidationOutcome, ArbitraryModel, Generic[OT])

The final output from a Guard execution.

Attributes:

  • call_id - The id of the Call that produced this ValidationOutcome.
  • raw_llm_output - The raw, unchanged output from the LLM call.
  • validated_output - The validated, and potentially fixed, output from the LLM call after passing through validation.
  • reask - If validation continuously fails and all allocated reasks are used, this field will contain the final reask that would have been sent to the LLM if additional reasks were available.
  • validation_passed - A boolean to indicate whether or not the LLM output passed validation. If this is False, the validated_output may be invalid.
  • error - If the validation failed, this field will contain the error message

from_guard_history​

@classmethod
def from_guard_history(cls, call: Call)

Create a ValidationOutcome from a history Call object.