Fix/custom eval dimension test

docs/pages/concepts/evaluation_dimension.md

@@ -36,6 +36,9 @@ The [`EvaluationDimensionBuilder`](/python_API/database/evaluation_dimensions) i
 ### Initialize the database
 The default evaluation metric is still `SotopiaDimensions` in `sotopia.env.evaluators`.There is no `CustomEvaluationDimension` in the database by default. To initialize the database, please refer to `examples/use_custom_dimensions.py`.
 
+### Use the SotopiaDimension
+The `SotopiaDimension` can be used directly without initializing the database. It provides a set of predefined evaluation dimensions that are ready to use for evaluating social interactions.
+
 ### Use the custom evaluation dimensions
 After you initialize your customized evaluation dimensions, you can choose to use any one of these methods provided below:
 

docs/pages/python_API/database/evaluation_dimensions.md

@@ -0,0 +1,54 @@
+# `evaluation_dimensions.py`
+
+This module provides classes and utilities for defining and managing custom evaluation dimensions within the Sotopia environment. It includes classes for individual dimensions, lists of dimensions, and a builder for creating dimension models.
+
+## Classes
+
+### `CustomEvaluationDimension`
+
+Represents a custom evaluation dimension with specific attributes such as name, description, and score range.
+
+#### Attributes
+- `name`: `str`. The name of the dimension.
+- `description`: `str`. A brief description of the dimension.
+- `range_low`: `int`. The minimum score for the dimension.
+- `range_high`: `int`. The maximum score for the dimension.
+
+### `CustomEvaluationDimensionList`
+
+Groups multiple custom evaluation dimensions together.
+
+#### Attributes
+- `name`: `str`. The name of the dimension list.
+- `dimension_pks`: `list[str]`. A list of primary keys for the dimensions included in the list.
+
+### `EvaluationDimensionBuilder`
+
+Provides utility methods to create and manage evaluation dimension models.
+
+#### Methods
+- `create_range_validator(low: int, high: int)`: Creates a validator for score ranges.
+
+  **Arguments:**
+  - `low`: `int`. The minimum score allowed.
+  - `high`: `int`. The maximum score allowed.
+
+- `build_dimension_model(dimension_ids: list[str])`: Builds a dimension model from primary keys.
+
+  **Arguments:**
+  - `dimension_ids`: `list[str]`. A list of dimension primary keys.
+
+- `build_dimension_model_from_dict(dimensions: list[dict[str, Union[str, int]]])`: Builds a dimension model from a dictionary.
+
+  **Arguments:**
+  - `dimensions`: `list[dict[str, Union[str, int]]]`. A list of dictionaries specifying dimension attributes.
+
+- `select_existing_dimension_model_by_name(dimension_names: list[str])`: Selects a dimension model by dimension names.
+
+  **Arguments:**
+  - `dimension_names`: `list[str]`. A list of dimension names.
+
+- `select_existing_dimension_model_by_list_name(list_name: str)`: Selects a dimension model by list name.
+
+  **Arguments:**
+  - `list_name`: `str`. The name of the dimension list.

sotopia/database/evaluation_dimensions.py

@@ -17,6 +17,11 @@ class CustomEvaluationDimensionList(JsonModel):
 
 
 class EvaluationDimensionBuilder:
+    """
+    EvaluationDimensionBuilder is a utility class for creating and managing evaluation dimensions.
+    It provides methods to build evaluation dimension models from various inputs such as primary keys, dictionaries, and names.
+    """
+
     @staticmethod
     def create_range_validator(
         low: int, high: int

tests/database/test_database.py

@@ -52,7 +52,7 @@ def test_create_custom_dimension() -> None:
     )
     custom_dimension.save()
     pk = custom_dimension.pk
-    dimension = CustomEvaluationDimension(uuid_str=pk)
+    dimension = CustomEvaluationDimension.get(pk)
     assert (
         dimension.name == custom_dimension.name
         and dimension.description == custom_dimension.description