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Some updates and fixes for async streaming and telemetry #1100
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            zsimjee
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  fix_server_export_mismatch
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  Sep 30, 2024 
      
    
  
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      9b7cfc3
              
                [Fix] #1091. Prevent confusion between guardrails.cli.hub.install and…
              
              
                JosephCatrambone 0ff4f6f
              
                remove unnecessary awaits on telem decorator (#1095)
              
              
                dtam e26316f
              
                wip some updates and fixes for async streaming and telem
              
              
                dtam 13b3498
              
                one more print
              
              
                dtam 9c7ee3e
              
                cleanup test
              
              
                dtam ebc7b45
              
                fix typing
              
              
                dtam 70d4f8c
              
                cleanup
              
              
                dtam File filter
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              | Original file line number | Diff line number | Diff line change | 
|---|---|---|
| @@ -0,0 +1,184 @@ | ||
| # 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 | ||
|  | ||
|  | ||
| @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 | ||
|  | ||
|  | ||
| class Response: | ||
| def __init__(self, chunks): | ||
| self.chunks = chunks | ||
|  | ||
| async def gen(): | ||
| for chunk in self.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 | ||
|  | ||
| self.completion_stream = gen() | ||
|  | ||
|  | ||
| 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.", | ||
| ] | ||
|  | ||
|  | ||
| @pytest.mark.asyncio | ||
| async def test_filter_behavior(mocker): | ||
| mocker.patch( | ||
| "litellm.acompletion", | ||
| return_value=Response(POETRY_CHUNKS), | ||
| ) | ||
|  | ||
| guard = gd.AsyncGuard().use_many( | ||
| 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, | ||
| prompt=prompt, | ||
| ) | ||
|  | ||
| text = "" | ||
| final_res = None | ||
| async for res in gen: | ||
| final_res = res | ||
| text += res.validated_output | ||
|  | ||
| assert final_res.raw_llm_output == ", he's always THERE." | ||
| # TODO deep dive this | ||
| assert text == ( | ||
| "John, under GOLDEN bridges, roams,\n" | ||
| "SAN Francisco's Dreams of FOG, and salty AIR,\n" | ||
| "In his HEART" | ||
| ) | 
      
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@nichwch any insights?
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If you're asking about the None check on line 157, it might be a remnant of before we implemented generators all the way down. Before, it was possible for validation to return None of the validators haven't accumulated enough chunks to validate yet. For sync streaming, we've changed this so that the validation logic takes place in a generator and only emits results when enough chunks have been accumulated.
Looking into the async streaming code, it looks like we never changed that to use generators for the validation logic, so validators still emit Nones before they have accumulated enough chunks