nemoguardrails.testing.fake_model

Framework-agnostic fake LLM model for testing guardrails configurations.

This module exposes :class:FakeLLMModel, a lightweight implementation of the LLMModel protocol used by NeMo Guardrails. It is intended for use in tests where a deterministic, scripted set of responses is preferable to calling out to a real model provider.

Module Contents

Classes

Name	Description
`FakeLLMModel`	Framework-agnostic fake LLM for testing. Implements the `LLMModel` protocol.

API

class nemoguardrails.testing.fake_model.FakeLLMModel(
    responses: typing.Optional[typing.List[str]] = None,
    llm_responses: typing.Optional[typing.List[nemoguardrails.types.LLMResponse]] = None,
    llm_exception: typing.Optional[Exception] = None,
    token_usage: typing.Optional[typing.List[typing.Dict[str, int]]] = None,
    should_return_token_usage: bool = False
)

Framework-agnostic fake LLM for testing. Implements the LLMModel protocol.

Parameters:

responses

Optional[List[str]]Defaults to None

A list of plain string responses. Each call to :meth:generate_async (or :meth:stream_async) consumes the next entry. Mutually exclusive with llm_responses.

llm_responses

Optional[List[LLMResponse]]Defaults to None

A list of :class:~nemoguardrails.types.LLMResponse objects. Useful when tool calls or structured fields need to be asserted. Takes precedence over responses when provided.

llm_exception

Optional[Exception]Defaults to None

An exception instance to raise on every generation, useful for exercising error-handling paths.

token_usage

Optional[List[Dict[str, int]]]Defaults to None

Optional list of token usage dictionaries (one per response). Each entry may include prompt_tokens, completion_tokens and total_tokens keys.

should_return_token_usage

boolDefaults to False

When True, populate :attr:LLMResponse.usage from token_usage.

inference_count

= 0

model_name

str

provider_name

Optional[str]

provider_url

Optional[str]

responses

nemoguardrails.testing.fake_model.FakeLLMModel._get_usage() -> typing.Optional[nemoguardrails.types.UsageInfo]

nemoguardrails.testing.fake_model.FakeLLMModel._next_response() -> nemoguardrails.types.LLMResponse

nemoguardrails.testing.fake_model.FakeLLMModel.generate_async(
    prompt,
    stop = None,
    kwargs = {}
) -> nemoguardrails.types.LLMResponse

async

nemoguardrails.testing.fake_model.FakeLLMModel.stream_async(
    prompt,
    stop = None,
    kwargs = {}
)

async