Update app.py

app.py

@@ -16,7 +16,6 @@ from langchain_community.llms import HuggingFaceHub
 from langchain_openai import ChatOpenAI
 from langchain_openai import OpenAIEmbeddings
 from langchain.memory import ConversationBufferMemory
-from langchain.prompts import PromptTemplate
 from langchain.chains import ConversationalRetrievalChain
 
 
@@ -88,33 +87,13 @@ def get_conversation_chain(vectorstore):
     Returns:
     - ConversationalRetrievalChain: An initialized conversational chain object.
     """
-
-    # Define a strict prompt template that makes the model answer only based on the document
-    prompt_template = """
-                    You are a helpful assistant. Use the following document context to answer the question.
-                    If the answer is not in the document, simply respond with "I cannot provide an answer based on the document."
-
-                    Document: {context}
-
-                    Question: {question}
-                    """
-
-    prompt = PromptTemplate(
-        input_variables=["context", "question"], template=prompt_template
-    )
-
     try:
         llm = ChatOpenAI(model_name="gpt-4o")
         memory = ConversationBufferMemory(
             memory_key="chat_history", return_messages=True
         )
         conversation_chain = ConversationalRetrievalChain.from_llm(
-            llm=llm,
-            retriever=vectorstore.as_retriever(),
-            memory=memory,
-            return_source_documents=True,
-            combine_docs_chain_kwargs={"prompt": prompt},
-            document_variable_name="context",  # Specify the variable name for the document context
+            llm=llm, retriever=vectorstore.as_retriever(), memory=memory
         )
         return conversation_chain
     except Exception as e:
@@ -122,15 +101,11 @@ def get_conversation_chain(vectorstore):
 
 
 def handle_userinput(user_question):
-    # response = st.session_state.conversation(
-    #     {
-    #         "question": f"Based on the memory and the provided document, answer the following user question: {user_question}. If the question is unrelated to memory or the document, just mention that you cannot provide an answer."
-    #     }
-    # )
-
-    # Retrieve the response from the conversation chain
-    response = st.session_state.conversation({"question": user_question})
-
+    response = st.session_state.conversation(
+        {
+            "question": f"Based on the memory and the provided document, answer the following user question: {user_question}. If the question is unrelated to memory or the document, just mention that you cannot provide an answer."
+        }
+    )
     st.session_state.chat_history = response["chat_history"]
 
     for i, message in reversed(list(enumerate(st.session_state.chat_history))):
@@ -144,10 +119,6 @@ def handle_userinput(user_question):
                 bot_template.replace("{{MSG}}", message.content), unsafe_allow_html=True
             )
 
-    # Ensure the bot only uses the document and replies accordingly
-    if not response["chat_history"][-1].content:
-        st.write("I cannot provide an answer based on the document.")
-
 
 def get_user_chat_count(user_id):
     """
@@ -247,7 +218,6 @@ def chat(slug, user_id):
     Restricts chat based on user group and chat count.
     """
 
-    # Show the user instruction at the top of the chat interface
     st.write(
         "**Please note:** Due to processing limitations, the chat may not fully comprehend the whole document."
     )