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- Skill optimization framework with training loop analogy - 11 benchmarks, 4 model backends (Azure OpenAI, Claude, Codex, Qwen) - WebUI for browser-based training control - Pluggable architecture for extending benchmarks and backends
82 lines
1.5 KiB
Markdown
82 lines
1.5 KiB
Markdown
# API Reference
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## Core Classes
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### `EnvAdapter`
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Abstract base class for benchmark environments.
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```python
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class EnvAdapter(ABC):
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async def execute(self, item, skill, model) -> TaskResult
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def evaluate(self, prediction, ground_truth) -> float
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def build_prompt(self, item, skill) -> str
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```
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### `DataLoader`
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Abstract base class for data loading and splitting.
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```python
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class DataLoader(ABC):
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def setup(self, cfg: dict) -> None
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def get_split_items(self, split: str) -> list[DataItem]
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```
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### `ModelBackend`
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Abstract base class for LLM backends.
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```python
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class ModelBackend(ABC):
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async def generate(self, messages, **kwargs) -> ModelResponse
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async def generate_with_tools(self, messages, tools, **kwargs) -> ModelResponse
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```
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### `Trainer`
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Main training loop orchestrator.
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```python
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class Trainer:
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def __init__(self, cfg: dict)
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async def train(self) -> TrainResult
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async def evaluate(self, skill: str, split: str) -> EvalResult
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```
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## Data Classes
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### `DataItem`
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```python
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@dataclass
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class DataItem:
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id: str
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input: str
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ground_truth: str
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metadata: dict = field(default_factory=dict)
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```
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### `TaskResult`
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```python
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@dataclass
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class TaskResult:
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item_id: str
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prediction: str
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score: float
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trajectory: list[dict]
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```
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### `ModelResponse`
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```python
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@dataclass
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class ModelResponse:
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content: str
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usage: dict
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model: str
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```
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For detailed source code, see the [`skillopt/`](https://github.com/microsoft/SkillOpt/tree/main/skillopt) directory.
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