[BUGFIX] argilla: review datasest import with new export flow

argilla/src/argilla/datasets/_io/_disk.py

@@ -114,6 +114,7 @@ def from_disk(
                 name=dataset_model.name, workspace_id=workspace.id
             )
             dataset = cls.from_model(model=dataset_model, client=client)
+            dataset.get()
         else:
             # Create a new dataset and load the settings and records
             if not os.path.exists(settings_path):

argilla/src/argilla/datasets/_io/_hub.py

@@ -220,8 +220,11 @@ def _log_dataset_records(hf_dataset: "HFDataset", dataset: "Dataset"):
         for col in responses_columns:
             question_name = col.split(".")[0]
             if col.endswith("users"):
-                response_questions[question_name]["users"] = hf_dataset[col]
-                user_ids.update({UUID(user_id): UUID(user_id) for user_id in set(sum(hf_dataset[col], []))})
+                response_questions[question_name]["users"] = hf_dataset[col] or []
+                for users in hf_dataset[col]:
+                    if users is None:
+                        continue
+                    user_ids.update({UUID(user_id): user_id for user_id in users})
             elif col.endswith("responses"):
                 response_questions[question_name]["responses"] = hf_dataset[col]
             elif col.endswith("status"):
@@ -240,7 +243,15 @@ def _log_dataset_records(hf_dataset: "HFDataset", dataset: "Dataset"):
             user_ids[unknown_user_id] = my_user.id
 
         # Create a mapper to map the Hugging Face dataset to a Record object
-        mapping = {col: col for col in hf_dataset.column_names if ".suggestion" in col}
+        mapping = {}
+        for col in hf_dataset.column_names:
+            if ".suggestion" in col:
+                mapping[col] = col
+            elif col.startswith("metadata.") and col.replace("metadata.", "") in dataset.schema:
+                mapping[col] = col.replace("metadata.", "")
+            elif col.startswith("vector.") and col.replace("vector.", "") in dataset.schema:
+                mapping[col] = col.replace("vector.", "")
+
         mapper = IngestedRecordMapper(dataset=dataset, mapping=mapping, user_id=my_user.id)
 
         # Extract responses and create Record objects
@@ -249,14 +260,17 @@ def _log_dataset_records(hf_dataset: "HFDataset", dataset: "Dataset"):
         for idx, row in enumerate(hf_dataset):
             record = mapper(row)
             for question_name, values in response_questions.items():
-                response_values = values["responses"][idx]
-                response_users = values["users"][idx]
-                response_status = values["status"][idx]
+                response_values = values["responses"][idx] or []
+                response_users = values["users"][idx] or []
+                response_status = values["status"][idx] or []
+
+                used_users = set()
                 for value, user_id, status in zip(response_values, response_users, response_status):
                     user_id = user_ids[UUID(user_id)]
-                    if user_id in response_users:
+                    if user_id in used_users:
                         continue
-                    response_users[user_id] = True
+
+                    used_users.add(user_id)
                     response = Response(
                         user_id=user_id,
                         question_name=question_name,

argilla/src/argilla/responses.py

@@ -61,16 +61,16 @@ def __init__(
             status (Union[ResponseStatus, str]): The status of the response as "draft", "submitted", "discarded".
         """
 
+        if isinstance(status, str):
+            status = ResponseStatus(status)
+
         if question_name is None:
             raise ValueError("question_name is required")
-        if value is None:
+        if value is None and status == ResponseStatus.submitted:
             raise ValueError("value is required")
         if user_id is None:
             raise ValueError("user_id is required")
 
-        if isinstance(status, str):
-            status = ResponseStatus(status)
-
         self._record = _record
         self.question_name = question_name
         self.value = value
@@ -253,7 +253,7 @@ def _compute_user_id_from_responses(responses: List[Response]) -> Optional[UUID]
     @staticmethod
     def __responses_as_model_values(responses: List[Response]) -> Dict[str, Dict[str, Any]]:
         """Creates a dictionary of response values from a list of Responses"""
-        return {answer.question_name: {"value": answer.value} for answer in responses}
+        return {answer.question_name: {"value": answer.value} for answer in responses if answer.value is not None}
 
     @classmethod
     def __model_as_responses_list(cls, model: UserResponseModel, record: "Record") -> List[Response]:
@@ -276,4 +276,12 @@ def __ranking_from_model_value(cls, value: List[Dict[str, Any]]) -> List[str]:
 
     @classmethod
     def __ranking_to_model_value(cls, value: List[str]) -> List[Dict[str, str]]:
-        return [{"value": v} for v in value]
+        values = []
+        for v in value or []:
+            if isinstance(v, dict):
+                values.append(v)
+            elif isinstance(v, str):
+                values.append({"value": v})
+            else:
+                raise RecordResponsesError(f"Invalid value for ranking question: {v}")
+        return values

argilla/src/argilla/settings/_field.py

@@ -29,8 +29,7 @@
     FieldSettings,
 )
 from argilla.settings._common import SettingsPropertyBase
-from argilla.settings._metadata import MetadataField, MetadataType
-from argilla.settings._vector import VectorField
+
 
 try:
     from typing import Self
@@ -296,21 +295,6 @@ def _field_from_model(model: FieldModel) -> Field:
         raise ArgillaError(f"Unsupported field type: {model.settings.type}")
 
 
-def _field_from_dict(data: dict) -> Union[Field, VectorField, MetadataType]:
+def _field_from_dict(data: dict) -> Field:
     """Create a field instance from a field dictionary"""
-    field_type = data["type"]
-
-    if field_type == "text":
-        return TextField.from_dict(data)
-    elif field_type == "image":
-        return ImageField.from_dict(data)
-    elif field_type == "chat":
-        return ChatField.from_dict(data)
-    elif field_type == "custom":
-        return CustomField.from_dict(data)
-    elif field_type == "vector":
-        return VectorField.from_dict(data)
-    elif field_type == "metadata":
-        return MetadataField.from_dict(data)
-    else:
-        raise ArgillaError(f"Unsupported field type: {field_type}")
+    return _field_from_model(FieldModel(**data))