feat: add governed embedding client with batching

src/polygateway/config.py

@@ -341,3 +341,48 @@ def _guard_stall(settings: GatewaySettings) -> None:
 def _load_lease_ttl(env: Mapping[str, str]) -> float:
     found = _first(env, "PGW_LEASE_TTL_S")
     return float(_cast(found[1], "float", found[0])) if found else _DEFAULT_LEASE_TTL_S
+
+
+@dataclass(frozen=True)
+class EmbeddingSettings:
+    """Embedding scope 装配配置(M2 §7): 复用 GatewaySettings + embedding 专用键。
+
+    专用键不进 GatewaySettings(LLM scope 不受影响): `{SCOPE}__BATCH_SIZE`
+    必填(分批是行为关键,不设默认)、`{SCOPE}__NORMALIZE`/`{SCOPE}__EXPECTED_DIM`
+    可选。cache/structured 键对 embedding 无意义,装配时忽略。
+    """
+
+    gateway: GatewaySettings
+    batch_size: int
+    normalize: bool = False
+    expected_dim: int | None = None
+
+    @classmethod
+    def from_env(
+        cls,
+        scope: str = "EMBED",
+        env: Mapping[str, str] | None = None,
+        *,
+        env_file: str = ".env",
+    ) -> EmbeddingSettings:
+        if env is None:
+            env = {
+                k: v for k, v in {**dotenv_values(env_file), **os.environ}.items() if v is not None
+            }
+        scope_u = scope.upper()
+        gateway = GatewaySettings.from_env(scope_u, env=env)
+        key, raw = _require(env, f"{scope_u}__BATCH_SIZE")
+        batch_size = int(_cast(raw, "int", key))
+        if batch_size < 1:
+            raise ValueError(f"{scope_u}__BATCH_SIZE 必须 ≥ 1")
+        norm = _first(env, f"{scope_u}__NORMALIZE")
+        dim = _first(env, f"{scope_u}__EXPECTED_DIM")
+        expected_dim = int(_cast(dim[1], "int", dim[0])) if dim else None
+        if expected_dim is not None and expected_dim < 1:
+            raise ValueError(f"{scope_u}__EXPECTED_DIM 必须 ≥ 1")
+        return cls(
+            gateway=gateway,
+            batch_size=batch_size,
+            normalize=bool(_cast(norm[1], "bool", norm[0])) if norm else False,
+            expected_dim=expected_dim,
+        )