Refactor exercise storage and structured grading criterion handling; …

…remove debug prints, update caching logic, and change serialization method for structured grading instructions

athena/athena/storage/exercise_storage.py

@@ -42,13 +42,10 @@ def store_exercises(exercises: List[Exercise], lms_url: Optional[str] = None):
     if lms_url is None:
         lms_url = get_lms_url()
 
-    print("Exercise lms_url 2: ", lms_url, "\n\n\n\n")
-
     with get_db() as db:
         for e in exercises:
             exercise_model = e.to_model()
             exercise_model.lms_url = lms_url
-            print("Exercise model: ", exercise_model.lms_url, "\n\n\n\n")
             db.merge(exercise_model)
         db.commit()
 

athena/athena/storage/structured_grading_criterion_storage.py

@@ -6,15 +6,14 @@
 from athena.schemas import StructuredGradingCriterion
 
 def get_structured_grading_criterion(exercise_id: int, current_hash: Optional[str] = None) -> Optional[StructuredGradingCriterion]:
-    lms_url = get_lms_url()
-    with get_db() as db:
-        cache_entry = db.query(DBStructuredGradingCriterion).filter_by(
-            exercise_id=exercise_id, lms_url=lms_url
-        ).first()
-        if cache_entry is not None and (current_hash is None or cache_entry.instructions_hash.is_(current_hash)):
-            return StructuredGradingCriterion.parse_raw(cache_entry.cached_instructions)
-
-        return None
+       lms_url = get_lms_url()
+       with get_db() as db:
+           cache_entry = db.query(DBStructuredGradingCriterion).filter_by(
+               exercise_id=exercise_id, lms_url=lms_url
+           ).first()
+           if cache_entry is not None and (current_hash is None or cache_entry.instructions_hash == current_hash): # type: ignore
+               return StructuredGradingCriterion.parse_obj(cache_entry.structured_grading_criterion)
+       return None
 
 def store_structured_grading_criterion(
         exercise_id: int, hash: str, structured_instructions: StructuredGradingCriterion
@@ -26,7 +25,7 @@ def store_structured_grading_criterion(
                 exercise_id=exercise_id,
                 lms_url=lms_url,
                 instructions_hash=hash,
-                cached_instructions=structured_instructions.json(),
+                structured_grading_criterion=structured_instructions.dict(),
             )
         )
         db.commit()

modules/modeling/module_modeling_llm/module_modeling_llm/core/get_structured_grading_instructions.py

@@ -28,6 +28,7 @@ async def get_structured_grading_instructions(
     current_hash = get_grading_instructions_hash(exercise_model)
     cached_instructions = get_structured_grading_criterion(exercise_model.exercise_id, current_hash)
     if cached_instructions:
+        print("Using cached instructions")
         return cached_instructions
 
     chat_prompt = ChatPromptTemplate.from_messages([