Update main.py

main.py

@@ -13,6 +13,7 @@ from langchain import PromptTemplate, LLMChain
 from langchain import HuggingFaceHub
 from langchain.document_loaders import TextLoader
 import torch
+from sentence_transformers.util import semantic_search
 
 import requests
 import random
@@ -110,6 +111,7 @@ async def pdf_file_qa_process(user_question: str, request: Request, file_to_proc
     texts=temp_texts
     initial_embeddings=get_embeddings(temp_texts)
     db_embeddings = torch.FloatTensor(initial_embeddings) 
+    print(db_embeddings)
     print("db_embeddings created...")
 
     #question = var_query.query    
@@ -117,14 +119,19 @@ async def pdf_file_qa_process(user_question: str, request: Request, file_to_proc
     print("API Call Query Received: "+question)
     q_embedding=get_embeddings(question)
     final_q_embedding = torch.FloatTensor(q_embedding)
-    from sentence_transformers.util import semantic_search
+    print(final_q_embedding)
+    
+    print("Semantic Similarity Search Starts...")        
+    start_1 = timeit.default_timer()
     hits = semantic_search(final_q_embedding, torch.FloatTensor(db_embeddings), top_k=5)
+    end_1 = timeit.default_timer()  
+    print("Semantic Similarity Search Ends...")  
+    print(f'Semantic Similarity Search共耗时： @ {end_1 - start_1}')     
    
     page_contents = []
     for i in range(len(hits[0])):
         page_content = texts[hits[0][i]['corpus_id']]
-        page_contents.append(page_content)
-        
+        page_contents.append(page_content)        
     print(page_contents)
     
     temp_page_contents=str(page_contents)        
@@ -136,8 +143,14 @@ async def pdf_file_qa_process(user_question: str, request: Request, file_to_proc
 
     loader = TextLoader(file_path, encoding="utf-8")
     loaded_documents = loader.load()
+    print(loaded_documents)
 
+    print("LLM Chain Starts...")        
+    start_2 = timeit.default_timer()    
     temp_ai_response = chain({"input_documents": loaded_documents, "question": question}, return_only_outputs=False)
+    end_2 = timeit.default_timer()  
+    print("LLM Chain Ends...")  
+    print(f'LLM Chain共耗时： @ {end_2 - start_2}')  
     
     initial_ai_response=temp_ai_response['output_text']