hangshuo652
50 lines
1.6 KiB
Python
50 lines
1.6 KiB
Python
from dataclasses import dataclass, field
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from pathlib import Path
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from .mapping import MappingConfig, FieldMapping
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@dataclass
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class Config:
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project_name: str = ""
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copybook_paths: list = field(default_factory=lambda: ["./copybooks"])
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dialect: str = "ibm"
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llm_model: str = "gpt-4o-mini"
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llm_timeout: int = 15
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llm_cache_dir: str = ".cache/llm"
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coverage_default: str = "boundary"
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rounding_mode: str = "TRUNCATE"
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tolerance: float = 0.01
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runner_mode: str = "native"
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spark_master: str = "local[*]"
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spark_input_format: str = "json"
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num_records: int = 1000
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branch_pass: float = 0.80
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max_llm_cost: float = 0.50
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@classmethod
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def from_toml(cls, path="aurak.toml"):
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import tomllib
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try:
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with open(path, "rb") as f:
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d = tomllib.load(f)
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except:
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return cls()
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c = cls()
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p = d.get("project", {})
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c.project_name = p.get("name", "")
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c.copybook_paths = p.get("copybook_paths", c.copybook_paths)
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c.dialect = p.get("dialect", "ibm")
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ll = d.get("llm", {})
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c.llm_model = ll.get("model", c.llm_model)
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co = d.get("coverage", {})
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c.coverage_default = co.get("default_target", "boundary")
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cp = d.get("comparison", {})
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c.rounding_mode = cp.get("rounding_mode", "TRUNCATE")
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c.tolerance = cp.get("default_tolerance", c.tolerance)
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r = d.get("runner", {})
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c.runner_mode = r.get("mode", "native")
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s = d.get("spark", {})
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c.spark_master = s.get("master", "local[*]")
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c.num_records = s.get("num_records", c.num_records)
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return c
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