linters

ffun/ffun/librarian/processors/openai_general.py

@@ -15,7 +15,8 @@
 
 
 system = """\
-You are an expert on semantic analysis, text summarization, and information extraction with PhD in Ontology-Driven Information Extraction.
+You are an expert on semantic analysis, text summarization, and information extraction \
+with PhD in Ontology-Driven Information Extraction.
 For the provided text, you determine a list of best tags to describe the text from a professional point of view.
 For each category, you provide 15 tags.
 

ffun/ffun/processors_quality/domain.py

@@ -81,7 +81,7 @@ def diff_processor_results(kb: KnowlegeBase, processor_name: str, entry_ids: lis
     return diffs
 
 
-def display_diffs(diffs: list[ProcessorResultDiff], show_tag_diffs: bool) -> None:
+def display_diffs(diffs: list[ProcessorResultDiff], show_tag_diffs: bool) -> None:  # noqa: CCR001
     table = []
 
     if not show_tag_diffs:
@@ -111,7 +111,6 @@ def display_diffs(diffs: list[ProcessorResultDiff], show_tag_diffs: bool) -> Non
             must_have = "ok"
         elif diff.actual_must_have_found > diff.last_must_have_found:
             must_have = ", ".join(f"?{tag}" for tag in diff.last_must_have_missing)
-            # must_have = f'missing: {diff.last_must_have_missing}'
             missing_must_have += 1
         else:
             raise NotImplementedError("We should not reach this point")
@@ -153,9 +152,9 @@ def display_diffs(diffs: list[ProcessorResultDiff], show_tag_diffs: bool) -> Non
 
     sys.stdout.write("should diffs:\n")
 
-    sys.stdout.write(
-        f"worst: {should_diffs[0]:.2%}, median: {should_diffs[len(should_diffs) // 2]:.2%}, best: {should_diffs[-1]:.2%}\n"
-    )
+    should_median = should_diffs[len(should_diffs) // 2]
+
+    sys.stdout.write(f"worst: {should_diffs[0]:.2%}, median: {should_median:.2%}, best: {should_diffs[-1]:.2%}\n")
 
     should_diff_average = sum(should_diffs) / len(should_diffs)
 

ffun/ffun/processors_quality/entities.py

@@ -42,20 +42,20 @@ class ProcessorResultDiff(BaseEntity):
 
     @pydantic.field_validator("actual_must_have_missing")
     @classmethod
-    def sort_actual_must_have_missing_tags(cls, v: str) -> str:
+    def sort_actual_must_have_missing_tags(cls, v: list[str]) -> list[str]:
         return list(sorted(v))
 
     @pydantic.field_validator("last_must_have_missing")
     @classmethod
-    def sort_last_must_have_missing_tags(cls, v: str) -> str:
+    def sort_last_must_have_missing_tags(cls, v: list[str]) -> list[str]:
         return list(sorted(v))
 
     @pydantic.field_validator("actual_has_and_last_not")
     @classmethod
-    def sort_actual_has_and_last_not_tags(cls, v: str) -> str:
+    def sort_actual_has_and_last_not_tags(cls, v: list[str]) -> list[str]:
         return list(sorted(v))
 
     @pydantic.field_validator("last_has_and_actual_not")
     @classmethod
-    def sort_last_has_and_actual_not_tags(cls, v: str) -> str:
+    def sort_last_has_and_actual_not_tags(cls, v: list[str]) -> list[str]:
         return list(sorted(v))