todos

llama_stack/providers/utils/common/data_schema_validator.py

@@ -75,29 +75,31 @@ VALID_SCHEMAS_FOR_EVAL = [
 ]
 
 
-def get_valid_schemas(api_str: str):
-    if api_str == Api.scoring.value:
-        return VALID_SCHEMAS_FOR_SCORING
-    elif api_str == Api.eval.value:
-        return VALID_SCHEMAS_FOR_EVAL
-    else:
-        raise ValueError(f"Invalid API string: {api_str}")
+# TODO(xiyan): add this back
+
+# def get_valid_schemas(api_str: str):
+#     if api_str == Api.scoring.value:
+#         return VALID_SCHEMAS_FOR_SCORING
+#     elif api_str == Api.eval.value:
+#         return VALID_SCHEMAS_FOR_EVAL
+#     else:
+#         raise ValueError(f"Invalid API string: {api_str}")
 
 
-def validate_dataset_schema(
-    dataset_schema: Dict[str, Any],
-    expected_schemas: List[Dict[str, Any]],
-):
-    if dataset_schema not in expected_schemas:
-        raise ValueError(f"Dataset {dataset_schema} does not have a correct input schema in {expected_schemas}")
+# def validate_dataset_schema(
+#     dataset_schema: Dict[str, Any],
+#     expected_schemas: List[Dict[str, Any]],
+# ):
+#     if dataset_schema not in expected_schemas:
+#         raise ValueError(f"Dataset {dataset_schema} does not have a correct input schema in {expected_schemas}")
 
 
-def validate_row_schema(
-    input_row: Dict[str, Any],
-    expected_schemas: List[Dict[str, Any]],
-):
-    for schema in expected_schemas:
-        if all(key in input_row for key in schema):
-            return
+# def validate_row_schema(
+#     input_row: Dict[str, Any],
+#     expected_schemas: List[Dict[str, Any]],
+# ):
+#     for schema in expected_schemas:
+#         if all(key in input_row for key in schema):
+#             return
 
-    raise ValueError(f"Input row {input_row} does not match any of the expected schemas in {expected_schemas}")
+#     raise ValueError(f"Input row {input_row} does not match any of the expected schemas in {expected_schemas}")