update 3

app.py

@@ -57,16 +57,7 @@ def _combine_documents(docs, document_prompt=PromptTemplate.from_template("{page
     doc_strings = [format_document(doc, document_prompt) for doc in docs]
     return document_separator.join(doc_strings)
 
-# Define the chain using LCEL
-condense_question_chain = RunnableLambda(lambda x: {"chat_history": chistory, "question": x}) | CONDENSE_QUESTION_PROMPT | llmc
-retriever_chain = RunnableLambda(lambda x: {"standalone_question": x}) | retriever | _combine_documents
-answer_chain = ANSWER_PROMPT | llm
 
-conversational_qa_chain = RunnableParallel(
-    condense_question=condense_question_chain,
-    retrieve=retriever_chain,
-    generate_answer=answer_chain
-)
 
 # Define the Streamlit app
 def app():
@@ -76,6 +67,17 @@ def app():
             'Which retriever would you like to use?',
             ('General Medicine', 'RespiratoryFishman', 'RespiratoryMurray', 'MedMRCP2', 'OldMedicine')
         )
+
+        # Define the chain using LCEL
+        condense_question_chain = RunnableLambda(lambda x: {"chat_history": chistory, "question": x}) | CONDENSE_QUESTION_PROMPT | llmc
+        retriever_chain = RunnableLambda(lambda x: {"standalone_question": x}) | retriever | _combine_documents
+        answer_chain = ANSWER_PROMPT | llm
+        
+        conversational_qa_chain = RunnableParallel(
+            condense_question=condense_question_chain,
+            retrieve=retriever_chain,
+            generate_answer=answer_chain
+        )
         
         # Define retrievers based on option
         persist_directory = {