AI/참고/guardrails-main/tests/integration_tests/test_async_streaming.py

# 3 tests
# 1. Test streaming with OpenAICallable (mock openai.Completion.create)
# 2. Test streaming with OpenAIChatCallable (mock openai.ChatCompletion.create)
# 3. Test string schema streaming
# Using the LowerCase Validator, and a custom validator to show new streaming behavior
from typing import Any, Callable, Dict, List, Optional, Union

import asyncio
import pytest

import guardrails as gd
from guardrails.utils.casting_utils import to_int
from guardrails.validator_base import (
    ErrorSpan,
    FailResult,
    OnFailAction,
    PassResult,
    ValidationResult,
    Validator,
    register_validator,
)
from tests.integration_tests.test_assets.validators import LowerCase, MockDetectPII

POETRY_CHUNKS = [
    "John, under ",
    "GOLDEN bridges",
    ", roams,\n",
    "SAN Francisco's ",
    "hills, his HOME.\n",
    "Dreams of",
    " FOG, and salty AIR,\n",
    "In his HEART",
    ", he's always THERE.",
]


@register_validator(name="minsentencelength", data_type=["string", "list"])
class MinSentenceLengthValidator(Validator):
    def __init__(
        self,
        min: Optional[int] = None,
        max: Optional[int] = None,
        on_fail: Optional[Callable] = None,
    ):
        super().__init__(
            on_fail=on_fail,
            min=min,
            max=max,
        )
        self._min = to_int(min)
        self._max = to_int(max)

    def sentence_split(self, value):
        return list(map(lambda x: x + ".", value.split(".")[:-1]))

    def validate(self, value: Union[str, List], metadata: Dict) -> ValidationResult:
        sentences = self.sentence_split(value)
        error_spans = []
        index = 0
        for sentence in sentences:
            if len(sentence) < self._min:
                error_spans.append(
                    ErrorSpan(
                        start=index,
                        end=index + len(sentence),
                        reason=f"Sentence has length less than {self._min}. "
                        f"Please return a longer output, "
                        f"that is shorter than {self._max} characters.",
                    )
                )
            if len(sentence) > self._max:
                error_spans.append(
                    ErrorSpan(
                        start=index,
                        end=index + len(sentence),
                        reason=f"Sentence has length greater than {self._max}. "
                        f"Please return a shorter output, "
                        f"that is shorter than {self._max} characters.",
                    )
                )
            index = index + len(sentence)
        if len(error_spans) > 0:
            return FailResult(
                validated_chunk=value,
                error_spans=error_spans,
                error_message=f"Sentence has length less than {self._min}. "
                f"Please return a longer output, "
                f"that is shorter than {self._max} characters.",
            )
        return PassResult(validated_chunk=value)

    def validate_stream(self, chunk: Any, metadata: Dict, **kwargs) -> ValidationResult:
        return super().validate_stream(chunk, metadata, **kwargs)


class Delta:
    content: str

    def __init__(self, content):
        self.content = content


class Choice:
    text: str
    finish_reason: str
    index: int
    delta: Delta

    def __init__(self, text, delta, finish_reason, index=0):
        self.index = index
        self.delta = delta
        self.text = text
        self.finish_reason = finish_reason


class MockOpenAIV1ChunkResponse:
    choices: list
    model: str

    def __init__(self, choices, model):
        self.choices = choices
        self.model = model


def Response(chunks):
    async def gen():
        for chunk in chunks:
            yield MockOpenAIV1ChunkResponse(
                choices=[
                    Choice(
                        delta=Delta(content=chunk),
                        text=chunk,
                        finish_reason=None,
                    )
                ],
                model="OpenAI model name",
            )
        await asyncio.sleep(0)  # Yield control to the event loop

    return gen()


@pytest.mark.asyncio
async def test_async_streaming_fix_behavior_two_validators(mocker):
    mocker.patch(
        "litellm.acompletion",
        return_value=Response(POETRY_CHUNKS),
    )

    guard = gd.AsyncGuard().use(
        MockDetectPII(
            on_fail=OnFailAction.FIX,
            pii_entities="pii",
            replace_map={"John": "<PERSON>", "SAN Francisco's": "<LOCATION>"},
        ),
        LowerCase(on_fail=OnFailAction.FIX),
    )
    prompt = """Write me a 4 line poem about John in San Francisco. 
    Make every third word all caps."""
    gen = await guard(
        model="gpt-3.5-turbo",
        max_tokens=10,
        temperature=0,
        stream=True,
        messages=[
            {
                "role": "user",
                "content": prompt,
            }
        ],
    )
    text = ""
    original = ""
    async for res in gen:
        original = original + res.raw_llm_output
        text = text + res.validated_output

    assert (
        text
        == """<PERSON>, under golden bridges, roams,
<LOCATION> hills, his home.dreams of fog, and salty air,
in his heart, he's always there."""
    )
    assert (
        original
        == """John, under GOLDEN bridges, roams,
SAN Francisco's hills, his HOME.
Dreams of FOG, and salty AIR,
In his HEART, he's always THERE."""
    )


@pytest.mark.asyncio
async def test_async_streaming_filter_behavior(mocker):
    mocker.patch(
        "litellm.acompletion",
        return_value=Response(POETRY_CHUNKS),
    )

    guard = gd.AsyncGuard().use(
        MockDetectPII(
            on_fail=OnFailAction.FIX,
            pii_entities="pii",
            replace_map={"John": "<PERSON>", "SAN Francisco's": "<LOCATION>"},
        ),
        LowerCase(on_fail=OnFailAction.FILTER),
    )
    prompt = """Write me a 4 line poem about John in San Francisco. 
    Make every third word all caps."""
    gen = await guard(
        model="gpt-3.5-turbo",
        max_tokens=10,
        temperature=0,
        stream=True,
        messages=[
            {
                "role": "user",
                "content": prompt,
            }
        ],
    )

    validated = ""
    raw_llm_output = ""

    async for res in gen:
        validated += res.validated_output
        raw_llm_output += res.raw_llm_output

    assert validated == ""
    assert (
        raw_llm_output
        == """John, under GOLDEN bridges, roams,
SAN Francisco's hills, his HOME.
Dreams of FOG, and salty AIR,
In his HEART, he's always THERE."""
    )