[doc change] address the error when the pred is 0 and we did not pass the eval data

adalflow/adalflow/utils/__init__.py

@@ -19,6 +19,9 @@
 from .config import new_components_from_config, new_component
 from .lazy_import import LazyImport, OptionalPackages, safe_import
 from .setup_env import setup_env
+from .data import DataLoader, Dataset, Subset
+from .global_config import get_adalflow_default_root_path
+from .cache import CachedEngine
 
 
 __all__ = [
@@ -43,4 +46,9 @@
     "write_list_to_jsonl",
     "safe_import",
     "setup_env",
+    "DataLoader",
+    "Dataset",
+    "Subset",
+    "get_adalflow_default_root_path",
+    "CachedEngine",
 ]

docs/source/get_started/adalflow_in_15mins.rst

@@ -379,7 +379,7 @@ Here’s the minimum code required to get started on evaluating the task pipelin
             self, sample: Example, y_pred: adal.GeneratorOutput
         ) -> float:
             y_label = -1
-            if y_pred and y_pred.data:
+            if (y_pred is not None and y_pred.data is not None):  # if y_pred and y_pred.data: might introduce bug when the data is 0
                 y_label = y_pred.data
             return self.eval_fn(y=y_label, y_gt=sample.answer)
 

docs/source/tutorials/lightrag_design_philosophy.rst

@@ -3,12 +3,12 @@
 Design Philosophy
 ====================================
 
-Right from the begining, `LightRAG` follows three fundamental principles.
+Right from the begining, `AdalFlow` follows three fundamental principles.
 
 
 Principle 1:  Simplicity over Complexity
 -----------------------------------------------------------------------
- We put these three hard rules while designing LightRAG:
+ We put these three hard rules while designing AdalFlow:
 
 - Every layer of abstraction needs to be adjusted and overall we do not allow more than 3 layers of abstraction.
 - We minimize the lines of code instead of maximizing the lines of code.

docs/source/use_cases/question_answering.rst

@@ -396,7 +396,7 @@ Here’s the minimum code required to get started on evaluating the task pipelin
 
         def prepare_eval(self, sample: Example, y_pred: adal.GeneratorOutput) -> float:
             y_label = -1
-            if y_pred and y_pred.data:
+            if (y_pred is not None and y_pred.data is not None):  # if y_pred and y_pred.data: might introduce bug when the data is 0
                 y_label = y_pred.data
             return self.eval_fn, {"y": y_label, "y_gt": sample.answer}
 

docs/source/use_cases/question_answering_word_sort.rst

@@ -375,7 +375,7 @@ Here’s the minimum code required to get started on evaluating the task pipelin
             self, sample: Example, y_pred: adal.GeneratorOutput
         ) -> float:
             y_label = -1
-            if y_pred and y_pred.data:
+            if (y_pred is not None and y_pred.data is not None):  # if y_pred and y_pred.data: might introduce bug when the data is 0
                 y_label = y_pred.data
             return self.eval_fn(y=y_label, y_gt=sample.answer)