lint

Signed-off-by: potthoffjan <[email protected]>

functions/chain.py

@@ -1,4 +1,3 @@
-import concurrent.futures
 from os import environ
 
 from get_google_docs import get_inital_prompt
@@ -69,13 +68,13 @@ def hist_aware_answers(llm_list, input_string, message_history):
     which might reference context in the chat history, formulate a standalone question \
     which can be understood without the chat history. Do NOT answer the question, \
     just reformulate it if needed and otherwise return it as is."""
-    
+
     # add in custom user info: -----------------------------
     # custom_istructions = get_custom_instructions_callable()
     # user_info = " "
     # if custom_istructions:
-    #     user_info = f"""Here is some information about the user, including the user's name, 
-    #     their profile description and style instructions on how they want you to answer stylewise: 
+    #     user_info = f"""Here is some information about the user, including the user's name,
+    #     their profile description and style instructions on how they want you to answer stylewise:
     #     User Name: {custom_istructions['name']}
     #     Style Instrctions: {custom_istructions['styleInstructions']}
     #     Personal Info: {custom_istructions['personalInstructions']}
@@ -89,14 +88,13 @@ def hist_aware_answers(llm_list, input_string, message_history):
         to answer accurately. write your response in markdown form and also add reference url
         so user can know from which source you are answering the questions.
         """
-    
-    context_str ="""
+
+    context_str = """
         CONTEXT:
         {context}
 
         """
 
-
     # health_ai_template = f'{init_prompt}{agent_str}{user_info}{context_str}'
     health_ai_template = f'{init_prompt}{agent_str}{context_str}'
 
@@ -128,10 +126,16 @@ def hist_aware_answers(llm_list, input_string, message_history):
                 ('human', '{input}'),
             ]
         )
-        history_aware_retriever = create_history_aware_retriever(llm, retriever, contextualize_q_prompt)
+        history_aware_retriever = create_history_aware_retriever(
+            llm, retriever, contextualize_q_prompt
+        )
 
         qa_prompt = ChatPromptTemplate.from_messages(
-            [('system', health_ai_template), MessagesPlaceholder('chat_history'), ('human', '{input}')]
+            [
+                ('system', health_ai_template),
+                MessagesPlaceholder('chat_history'),
+                ('human', '{input}'),
+            ]
         )
 
         question_answer_chain = create_stuff_documents_chain(llm, qa_prompt)