Update app.py

app.py

@@ -30,11 +30,10 @@ import asyncio
 
 from langchain.globals import set_llm_cache
 from langchain_openai import OpenAI
-from langchain.cache import SQLiteCache
 
 
-# Set up SQLite-based caching
-set_llm_cache(SQLiteCache())
+
+
 
 
 embeddings = OpenAIEmbeddings(api_key=os.environ['OPENAI_API_KEY'])
@@ -61,16 +60,7 @@ index_name="radarclintcountrymusic11152024"
 vectorstore = PineconeVectorStore(index_name=index_name, embedding=embeddings)
 retriever = vectorstore.as_retriever(search_kwargs={'k': 2})
 
-#chat_model = ChatOpenAI(api_key=os.environ['OPENAI_API_KEY'], temperature=0, model='gpt-4o')
-
-# Initialize a slower LLM model with caching
-chat_model = OpenAI(
-    api_key=os.environ['OPENAI_API_KEY'],
-    model_name="gpt-4o",
-    n=2,
-    best_of=2,
-    temperature=0
-)
+chat_model = ChatOpenAI(api_key=os.environ['OPENAI_API_KEY'], temperature=0, model='gpt-4o')
 
 #code for history
 conversational_memory = ConversationBufferWindowMemory(
@@ -121,30 +111,20 @@ Helpful Answer:"""
 
 QA_CHAIN_PROMPT= PromptTemplate(input_variables=["context", "question"], template=template)
 
-#def build_qa_chain(prompt_template):
-    #qa_chain = RetrievalQA.from_chain_type(
-        #llm=chat_model,
-        #chain_type="stuff",
-        #retriever=retriever,
-        #chain_type_kwargs={"prompt": prompt_template}
-    #)
-    #return qa_chain  # Return the qa_chain object
-
-# Function to initialize QA Chain with caching enabled
-def build_qa_chain_with_cache(prompt_template):
+def build_qa_chain(prompt_template):
     qa_chain = RetrievalQA.from_chain_type(
         llm=chat_model,
         chain_type="stuff",
         retriever=retriever,
         chain_type_kwargs={"prompt": prompt_template}
     )
-    return qa_chain
+    return qa_chain  # Return the qa_chain object
+
 
 # Instantiate the QA Chain using the defined prompt template
-#qa_chain = build_qa_chain(QA_CHAIN_PROMPT)
+qa_chain = build_qa_chain(QA_CHAIN_PROMPT)
+
 
-# Instantiate the QA Chain using the cached LLM
-qa_chain = build_qa_chain_with_cache(QA_CHAIN_PROMPT)
 
 # Define the function to clear input and output
 def clear_fields():