mirror of
https://github.com/msoedov/agentic_security.git
synced 2026-06-24 06:09:55 +02:00
Alexander Myasoedov
55 lines
1.4 KiB
Python
55 lines
1.4 KiB
Python
from dataclasses import dataclass
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from cache_to_disk import cache_to_disk # noqa
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# TODO: refactor this class to use from .data
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@dataclass
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class ProbeDataset:
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dataset_name: str
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metadata: dict
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prompts: list[str]
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tokens: int
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approx_cost: float
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lazy: bool = False
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def metadata_summary(self):
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return {
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"dataset_name": self.dataset_name,
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"num_prompts": len(self.prompts),
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"tokens": self.tokens,
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"approx_cost": self.approx_cost,
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}
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# @cache_to_disk(n_days_to_cache=1)
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def load_dataset_generic(name, getter=lambda x: x["train"]["prompt"]):
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from datasets import load_dataset
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dataset = load_dataset(name)
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mjs_prompts = getter(dataset)
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return ProbeDataset(
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dataset_name=name,
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metadata={},
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prompts=mjs_prompts,
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tokens=0,
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approx_cost=0.0,
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)
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def prepare_prompts(
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dataset_names=[], budget=-1, tools_inbox=None
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) -> list[ProbeDataset]:
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# fka/awesome-chatgpt-prompts
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# data-is-better-together/10k_prompts_ranked
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# alespalla/chatbot_instruction_prompts
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dataset_map = {
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"data-is-better-together/10k_prompts_ranked": load_dataset_generic(
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"data-is-better-together/10k_prompts_ranked"
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),
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"fka/awesome-chatgpt-prompts": load_dataset_generic(
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"fka/awesome-chatgpt-prompts"
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),
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}
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return [dataset_map[name] for name in dataset_map]
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