Update utils/database.py

utils/database.py

@@ -335,7 +335,8 @@ def display_vector_store_info():
     except Exception as e:
         st.error(f"Error displaying vector store info: {e}")
         st.error(traceback.format_exc())
-        
+
+
 def initialize_qa_system(vector_store):
     """Initialize QA system with proper chat handling."""
     try:
@@ -344,35 +345,31 @@ def initialize_qa_system(vector_store):
             model_name="gpt-4",
             api_key=os.environ.get("OPENAI_API_KEY")
         )
-        
+
         # Create retriever function
         retriever = vector_store.as_retriever(search_kwargs={"k": 2})
-        
+
         # Create a template that enforces clean formatting
         prompt = ChatPromptTemplate.from_messages([
             ("system", """You are a helpful assistant analyzing RFP documents. 
-            Format your responses in a clean, professional manner using markdown formatting:
+            Format your responses in a clean, professional manner:
             
-            1. Start with a brief executive summary in plain text
-            2. Use proper markdown headers for sections (e.g., ## Key Points)
+            1. Start with a brief executive summary
+            2. Use clear section headers
             3. Use bullet points for lists
-            4. Include source attribution at the end in italics
-            5. Don't include any technical metadata or delimiters
-            6. Use bold for emphasis on important points
-            7. Keep the formatting clean and professional"""),
+            4. Include source attribution at the end
+            5. Keep the formatting clean and professional"""),
             MessagesPlaceholder(variable_name="chat_history"),
             ("human", "{input}\n\nContext: {context}")
         ])
 
         def get_chat_history(inputs):
-            """Get formatted chat history."""
             chat_history = inputs.get("chat_history", [])
             if not isinstance(chat_history, list):
                 return []
             return [msg for msg in chat_history if isinstance(msg, BaseMessage)]
 
         def get_context(inputs):
-            """Get formatted context from documents."""
             docs = retriever.get_relevant_documents(inputs["input"])
             context_parts = []
             for doc in docs:
@@ -380,20 +377,6 @@ def initialize_qa_system(vector_store):
                 context_parts.append(f"\nFrom {source}:\n{doc.page_content}")
             return "\n".join(context_parts)
 
-        def format_response(response_text):
-            """Clean up the response formatting."""
-            # Remove technical metadata
-            if 'content=' in response_text:
-                response_text = response_text.split('content=')[1].split('response_metadata')[0]
-            
-            # Remove outer quotes if present
-            response_text = response_text.strip("'")
-            
-            # Remove escaped newlines and replace with actual newlines
-            response_text = response_text.replace('\\n', '\n')
-            
-            return response_text
-
         chain = (
             {
                 "context": get_context,
@@ -402,7 +385,6 @@ def initialize_qa_system(vector_store):
             }
             | prompt
             | llm
-            | format_response
         )
 
         return chain