fixed load nested config from env

pydantic_settings/sources/providers/env.py

@@ -186,7 +186,10 @@ class Cfg(BaseSettings):
             type_has_key = self.next_field(type_, key, case_sensitive)
             if type_has_key:
                 return type_has_key
-        if is_model_class(annotation) or is_pydantic_dataclass(annotation):  # type: ignore[arg-type]
+        if _lenient_issubclass(get_origin(annotation), dict):
+            # get value type if it's a dict
+            return get_args(annotation)[-1]
+        elif is_model_class(annotation) or is_pydantic_dataclass(annotation):  # type: ignore[arg-type]
             fields = _get_model_fields(annotation)
             # `case_sensitive is None` is here to be compatible with the old behavior.
             # Has to be removed in V3.

tests/test_settings.py

@@ -3282,3 +3282,23 @@ class StrictSettings(BaseSettings, env_nested_delimiter='__', strict=True):
             'my_int': 1,
         },
     }
+
+
+def test_env_source_when_load_multi_nested_config(env):
+    class EmbeddingModel(BaseModel):
+        model: str = 'text-embedding-3-small'
+        keys: list[str] = Field(default_factory=list)
+
+    class LLM(BaseModel):
+        embeddings: dict[str, EmbeddingModel] = Field(default_factory=dict)
+
+    class LLMSettings(BaseSettings):
+        llm: LLM = Field(default_factory=lambda: LLM())
+
+        model_config = SettingsConfigDict(env_prefix='my_prefix_', env_nested_delimiter='__')
+
+    env.set('my_prefix_llm__embeddings__openai__keys', '["sk-..."]')
+    env.set('my_prefix_llm__embeddings__qwen__keys', '["sk-..."]')
+    llm_setting = LLMSettings()
+    assert llm_setting.llm.embeddings['openai'].keys == ['sk-...']
+    assert llm_setting.llm.embeddings['qwen'].keys == ['sk-...']