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Update dataclasses.replace calls in DefaultRetryStrategy #112

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merged 8 commits into from
Sep 5, 2024
Merged

Update dataclasses.replace calls in DefaultRetryStrategy #112

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b9e2fe0
Update dataclasses.replace calls in DefaultRetryStrategy
mentatai[bot] Sep 4, 2024
31be854
Update dataclasses.replace calls in DefaultRetryStrategy
mentatai[bot] Sep 4, 2024
0cdea61
CI fix: Fix CI failure due to formatting issue
mentatai[bot] Sep 4, 2024
f139c96
CI fix: Fix CI failure due to formatting issue
mentatai[bot] Sep 4, 2024
6e6d7f1
Incorporate feedback to maintain dataclass usage for Model class
mentatai[bot] Sep 4, 2024
8572625
CI fix: Fix CI failure due to incorrect attribute access in models.py
mentatai[bot] Sep 4, 2024
81e4434
CI fix: Fix CI failure due to incorrect attribute access in models.py
mentatai[bot] Sep 4, 2024
f44adfe
version
scotttestbot[bot] Sep 5, 2024
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4 changes: 3 additions & 1 deletion spice/models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -174,7 +174,9 @@ def get_model_from_name(model_name: str) -> Model:
else:
input_cost = None
output_cost = None
model = dataclasses.replace(model, name=model_name, input_cost=input_cost, output_cost=output_cost)
model = model.model_copy(
update={"name": model_name, "input_cost": input_cost, "output_cost": output_cost}
)
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this is still a dataclass, so shouldn't be updated

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You're right, I apologize for the confusion. The Model class is indeed still a dataclass. The change from dataclasses.replace to model_copy was only necessary for the SpiceCallArgs class, which has been converted to a Pydantic model. I'll update the PR to keep the Model class as a dataclass and only change the SpiceCallArgs related code.

return model

return UnknownModel(model_name, None)
5 changes: 2 additions & 3 deletions spice/retry_strategy/default_strategy.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,3 @@
import dataclasses
from typing import Any, Callable, Optional

from spice.call_args import SpiceCallArgs
Expand All @@ -17,9 +16,9 @@ def decide(
self, call_args: SpiceCallArgs, attempt_number: int, model_output: str, name: str
) -> tuple[Behavior, SpiceCallArgs, Any, str]:
if attempt_number == 1 and call_args.temperature is not None:
call_args = dataclasses.replace(call_args, temperature=max(0.2, call_args.temperature))
call_args = call_args.model_copy(update={"temperature": max(0.2, call_args.temperature)})
elif attempt_number > 1 and call_args.temperature is not None:
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Suggested change
call_args = call_args.model_copy(update={"temperature": max(0.2, call_args.temperature)})
call_args = call_args.model_copy(update={"temperature": max(0.2, call_args.temperature or 0)})

It's a good practice to provide a default value when using or with None. This ensures that max() always receives two numeric values, even if call_args.temperature is None.

call_args = dataclasses.replace(call_args, temperature=max(0.5, call_args.temperature))
call_args = call_args.model_copy(update={"temperature": max(0.5, call_args.temperature)})

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Suggested change
call_args = call_args.model_copy(update={"temperature": max(0.5, call_args.temperature)})
call_args = call_args.model_copy(update={"temperature": max(0.5, call_args.temperature or 0)})

Similar to the previous comment, it's safer to provide a default value when call_args.temperature might be None.

if self.validator and not self.validator(model_output):
if attempt_number < self.retries:
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