Update chat.py

chat.py

@@ -82,9 +82,21 @@ retriever = vectorstore_persisted.as_retriever(
 )
 
 
-def make_completion(messages: List[Message]) -> Optional[str]:
+def make_completion(input:str, history: List[Message]) -> Optional[str]:
 
-    prompt = [{'role':'system', 'content': qna_system_message}] + messages
+    relevant_document_chunks = retriever.invoke(input)
+    context_list = [d.page_content for d in relevant_document_chunks]
+    context_for_query = "\n".join(context_list)
+
+    user_message = [{
+        'role': 'user', 
+        'content': qna_user_message_template.format(
+            context=context_for_query,
+            question=input
+        )
+    }]
+
+    prompt = [{'role':'system', 'content': qna_system_message}] + history + user_message
     
     try:
         
@@ -107,28 +119,13 @@ def predict(input, history):
     Predict the response of the chatbot and complete a running list of chat history.
     """
 
-    relevant_document_chunks = retriever.invoke(input)
-    context_list = [d.page_content for d in relevant_document_chunks]
-    context_for_query = "\n".join(context_list)
-
-    user_message = {
-        'role': 'user', 
-        'content': qna_user_message_template.format(
-            context=context_for_query,
-            question=input
-        )
-    }
-
-    history.append(user_message)
-
-    response = make_completion(history)
+    response = make_completion(input, history)
 
+    history.append({"role": "user", "content": input})
     history.append({"role": "assistant", "content": response})
 
-    # Extract only the user question from the input
-
     messages = [
-        (history[i]["content"].split("###Question")[1].strip('\n'), history[i+1]["content"]) 
+        (history[i]["content"], history[i+1]["content"]) 
         for i in range(0, len(history)-1, 2)
     ]