minor changes to print statements

ai_ta_backend/filtering_contexts.py

@@ -69,13 +69,13 @@ def batch_context_filtering(batch_docs, user_query, max_time_before_return=45, m
   """
 
   start_time = time.monotonic()
-  
+
   partial_func = partial(list_context_filtering, user_query=user_query, max_time_before_return=max_time_before_return, max_concurrency=max_concurrency)
   with ProcessPoolExecutor(max_workers=5) as executor:
     processed_docs = list(executor.map(partial_func, batch_docs))
 
   processed_docs = list(processed_docs)
-  print(f"⏰ Context filtering runtime: {(time.monotonic() - start_time):.2f} seconds")
+  print(f"⏰ Batch context filtering runtime: {(time.monotonic() - start_time):.2f} seconds")
 
   return processed_docs
 

ai_ta_backend/vector_database.py

@@ -1148,11 +1148,12 @@ def getTopContexts(self, search_query: str, course_name: str, token_limit: int =
 
   def getTopContextsWithMQR(self, search_query: str, course_name: str, token_limit: int = 4_000) -> Union[List[Dict], str]:
     """
-    New info-retrieval pipeline that uses multi-query retrieval + reciprocal rank fusion + context padding.
+    New info-retrieval pipeline that uses multi-query retrieval + filtering + reciprocal rank fusion + context padding.
     1. Generate multiple queries based on the input search query.
     2. Retrieve relevant docs for each query.
-    3. Rank the docs based on the relevance score.
-    4. Pad the top 5 docs with context from the original document.
+    3. Filter the relevant docs based on the user query and pass them to the rank fusion step.
+    4. Rank the docs based on the relevance score.
+    5. Pad the top 5 docs with context from the original document.
     """
     try:
       top_n = 80 # HARD CODE TO ENSURE WE HIT THE MAX TOKENS
@@ -1175,7 +1176,7 @@ def getTopContextsWithMQR(self, search_query: str, course_name: str, token_limit
 
       batch_found_docs: list[list[Document]] = self.batch_vector_search(search_queries=generated_queries, course_name=course_name)
 
-      # use the below filtering code for batch context filtering - List[List[Document]] (only use at this point in the pipeline)
+      # use the below filtering code for batch context filtering - List[List[Document]] (only use between batch search and rank fusion)
       filtered_docs = batch_context_filtering(batch_docs=batch_found_docs, user_query=search_query, max_time_before_return=45, max_concurrency=100)
 
       filtered_count = 0
@@ -1209,11 +1210,11 @@ def getTopContextsWithMQR(self, search_query: str, course_name: str, token_limit
       #filtered_docs = list_context_filtering(contexts=final_docs, user_query=search_query, max_time_before_return=45, max_concurrency=100)
       #filtered_docs = list(run(contexts=final_docs, user_query=search_query, max_time_before_return=45, max_concurrency=100))
       # print(f"Number of docs after context filtering: {len(filtered_docs)}")
-      # if len(filtered_docs) > 0:
-      #   final_docs_used = filtered_docs
-      # else:
+      # if 0 <= len(filtered_docs) <= 5:
       #   final_docs_used = final_docs
       #   print("No docs passed context filtering, using all docs retrieved.")
+      # else:
+      #   final_docs_used = filtered_docs
 
       valid_docs = []
       num_tokens = 0
@@ -1232,13 +1233,11 @@ def getTopContextsWithMQR(self, search_query: str, course_name: str, token_limit
           break
 
       print("Length of valid docs: ", len(valid_docs))
-       # Context filtering
-      #filtered_docs = list(run(contexts=valid_docs, user_query=search_query, max_time_before_return=45, max_concurrency=100))
-      #print(f"Number of docs after context filtering: {len(filtered_docs)}")
 
       print(f"Total tokens used: {token_counter} total docs: {len(found_docs)} num docs used: {len(valid_docs)}")
       print(f"Course: {course_name} ||| search_query: {search_query}")
       print(f"⏰ ^^ Runtime of getTopContextsWithMQR: {(time.monotonic() - start_time_overall):.2f} seconds")
+
       if len(valid_docs) == 0:
         return []
       return self.format_for_json_mqr(valid_docs)