feat: add skill learning runtime

skills_learning/config.py

@@ -0,0 +1,61 @@
+from __future__ import annotations
+
+import os
+from collections.abc import Mapping
+from dataclasses import dataclass
+
+DEFAULT_DIVERGENCE_TOLERANCE = 0.0
+DEFAULT_EMBEDDING_MODEL = "text-embedding-3-small"
+DEFAULT_TOP_K = 5
+
+ENABLED_ENV = "SKILL_AUTHORING_ENABLED"
+DIVERGENCE_TOLERANCE_ENV = "SKILL_DIVERGENCE_TOLERANCE"
+EMBEDDING_MODEL_ENV = "SKILL_EMBEDDING_MODEL"
+TOP_K_ENV = "SKILL_RETRIEVAL_TOP_K"
+
+
+@dataclass(frozen=True)
+class SkillAuthoringConfig:
+    enabled: bool = False
+    divergence_tolerance: float = DEFAULT_DIVERGENCE_TOLERANCE
+    embedding_model: str = DEFAULT_EMBEDDING_MODEL
+    top_k: int = DEFAULT_TOP_K
+
+
+def load_config(env: Mapping[str, str] | None = None) -> SkillAuthoringConfig:
+    values = env or os.environ
+    return SkillAuthoringConfig(
+        enabled=_parse_bool(values.get(ENABLED_ENV), default=False),
+        divergence_tolerance=_parse_float(
+            values.get(DIVERGENCE_TOLERANCE_ENV),
+            default=DEFAULT_DIVERGENCE_TOLERANCE,
+        ),
+        embedding_model=values.get(EMBEDDING_MODEL_ENV) or DEFAULT_EMBEDDING_MODEL,
+        top_k=_parse_int(values.get(TOP_K_ENV), default=DEFAULT_TOP_K),
+    )
+
+
+def _parse_bool(value: str | None, *, default: bool) -> bool:
+    if value is None:
+        return default
+    return value.strip().lower() in {"1", "true", "yes", "on", "enabled"}
+
+
+def _parse_float(value: str | None, *, default: float) -> float:
+    if value is None:
+        return default
+    try:
+        parsed = float(value)
+    except ValueError:
+        return default
+    return parsed if parsed >= 0 else default
+
+
+def _parse_int(value: str | None, *, default: int) -> int:
+    if value is None:
+        return default
+    try:
+        parsed = int(value)
+    except ValueError:
+        return default
+    return parsed if parsed > 0 else default