added a wrapper function for run()

ai_ta_backend/filtering_contexts.py

@@ -155,9 +155,16 @@ def run(contexts, user_query, max_tokens_to_return=3000, max_time_before_return=
       if not result_futures:
         break
 
+
+def ray_run(contexts, user_query, max_time_before_return=45, max_concurrency=100):
+  ray.init()
+  filtered_passages = list(run(contexts, user_query, max_time_before_return=max_time_before_return, max_concurrency=max_concurrency))
+  return filtered_passages
+
+
 # ! CONDA ENV: llm-serving
 if __name__ == "__main__":
-  ray.init() 
+  #ray.init() 
   start_time = time.monotonic()
   # print(len(CONTEXTS))
 

ai_ta_backend/vector_database.py

@@ -43,7 +43,7 @@
 from ai_ta_backend.extreme_context_stuffing import OpenAIAPIProcessor
 from ai_ta_backend.utils_tokenization import count_tokens_and_cost
 from ai_ta_backend.parallel_context_processing import context_processing
-from ai_ta_backend.filtering_contexts import run
+from ai_ta_backend.filtering_contexts import run, ray_run
 
 
 MULTI_QUERY_PROMPT = hub.pull("langchain-ai/rag-fusion-query-generation")
@@ -1164,8 +1164,9 @@ def getTopContextsWithMQR(self, search_query: str, course_name: str, token_limit
       # count tokens at start and end, then also count each context.
       token_counter, _ = count_tokens_and_cost(pre_prompt + '\n\nNow please respond to my query: ' + search_query) # type: ignore
 
-      ray.init()
-      filtered_docs = list(run(contexts=final_docs, user_query=search_query, max_time_before_return=45, max_concurrency=100))
+      filtered_docs = ray_run(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)}")
 
       valid_docs = []