Update app.py

app.py

@@ -6,7 +6,7 @@ from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.embeddings import OpenAIEmbeddings
 from langchain.vectorstores import FAISS
 from langchain.chat_models import ChatOpenAI
-from langchain.chains import ConversationalRetrievalChain, LLMChain
+from langchain.chains import LLMChain
 from langchain.memory import ConversationBufferMemory
 from langchain.prompts import PromptTemplate
 from PyPDF2 import PdfReader
@@ -16,35 +16,25 @@ class ContextAwareResponseGenerator:
         self.llm = llm
         self.response_prompt = PromptTemplate(
             input_variables=['context', 'query', 'chat_history'],
-            template="""Based on the context, query, and chat history, generate a clear, concise, and helpful response.
+            template="""Given the context, query, and chat history, generate the best response that is clear and helpful.
 
 Context: {context}
 Query: {query}
 Chat History: {chat_history}
 
-Response Structure Selection Criteria (internal):
-1. Technical academic breakdown
-2. Concise summary with key points
-3. Markdown with hierarchical insights
-4. Narrative explanation
-5. Comparative analysis
-
-Generate the response based on the appropriate structure, but do not display structure selection to the user. Only show the final response."""  # This internal prompt generates the response.
+Choose the most appropriate response structure and generate the response directly, without explicit guidance on which format to use. Your response should be based on the query and context provided."""  
         )
         self.response_chain = LLMChain(llm=self.llm, prompt=self.response_prompt)
 
     def generate_response(self, context, query, chat_history=''):
         try:
-            # Generate structured response internally
+            # Generate the response content with structure handled by the LLM itself
             response = self.response_chain.run({
                 'context': context,
                 'query': query,
                 'chat_history': chat_history or "No previous context"
             })
-            
-            # Optionally process response internally (e.g., format it based on structure) 
-            # but only return the final formatted response.
-            return response.strip()
+            return response.strip()  # LLM decides on the structure
         except Exception as e:
             logging.error(f"Response generation error: {e}")
             return self._default_response(query)