build(agent): weasel-1#856f80 iteration
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#!/usr/bin/env python3
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"""Generate JSON Schema files from specs.ir Pydantic models.
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Writes one JSON file per model name into the output directory. Useful for
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external adapters to consume a stable IR schema artifact.
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"""
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import json
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import argparse
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from pathlib import Path
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import specs.ir as ir
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def main(out_dir: Path):
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out_dir.mkdir(parents=True, exist_ok=True)
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# ir.__all__ lists model names exported by the module
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for name in getattr(ir, "__all__", []):
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cls = getattr(ir, name, None)
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if cls is None:
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continue
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# pydantic v1/v2 compat: prefer model_json_schema if available
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schema = None
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if hasattr(cls, "model_json_schema"):
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try:
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schema = cls.model_json_schema()
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except TypeError:
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schema = cls.schema()
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else:
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schema = cls.schema()
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out_path = out_dir / f"{name}.json"
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with out_path.open("w", encoding="utf8") as f:
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json.dump(schema, f, indent=2, sort_keys=True)
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print(f"Wrote {out_path}")
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if __name__ == "__main__":
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p = argparse.ArgumentParser()
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p.add_argument("--out", dest="out", default="specs/schemas", help="Output directory")
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args = p.parse_args()
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main(Path(args.out))
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@ -1,2 +1,7 @@
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This directory can hold generated JSON Schema files for the IR models in specs/ir.py
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This directory holds generated JSON Schema files for the EnergiBridge IR
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Use the models' `.schema()` or `.schema_json()` in Python to export updated schemas.
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models defined in `specs/ir.py`.
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Generation
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- Use `python3 scripts/generate_schemas.py --out specs/schemas` to regenerate
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- The test suite contains a test that exercises the generator to ensure the
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schemas are JSON-serializable and consistent with the Pydantic models.
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from pathlib import Path
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import json
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import specs.ir as ir
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def test_generate_schemas(tmp_path: Path):
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out = tmp_path / "schemas"
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out.mkdir()
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# mimic the generator logic: iterate ir.__all__ and ensure JSON serializable
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for name in getattr(ir, "__all__", []):
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cls = getattr(ir, name, None)
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assert cls is not None, f"Missing model class for {name}"
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# get schema (compat v1/v2)
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if hasattr(cls, "model_json_schema"):
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try:
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schema = cls.model_json_schema()
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except TypeError:
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schema = cls.schema()
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else:
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schema = cls.schema()
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# ensure JSON serializable
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json_str = json.dumps(schema)
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assert "title" in json.loads(json_str) or "properties" in json.loads(json_str)
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# write out to disk and ensure file exists
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p = out / f"{name}.json"
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p.write_text(json_str, encoding="utf8")
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assert p.exists()
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