Update app.py

app.py

@@ -46,12 +46,15 @@ def calculate_embedding_cost(text):
     # print(f'Embedding Cost in USD: {total_tokens / 1000 * 0.0004:.6f}')
     return total_tokens, total_tokens / 1000 * 0.0004
 
+
 def ask_with_memory(vector_store, query, chat_history=[]):
     from langchain.chains import ConversationalRetrievalChain
     from langchain.chat_models import ChatOpenAI
     
     llm = ChatOpenAI(model_name='gpt-3.5-turbo', temperature=1, openai_api_key=openai_api_key)
-    retriever = vector_store.as_retriever(search_type='similarity', search_kwargs={'k': 3})
+    
+    # The retriever is created with metadata filter directly in search_kwargs
+    retriever = vector_store.as_retriever(search_type='similarity', search_kwargs={'k': 3, 'filter': {'source':{'$eq': '/Users/cheynelevesseur/Desktop/Python_Code/Projects/LLM/Intensifying Literacy Instruction - Essential Practices (NATIONAL).pdf'}}})
 
     chain= ConversationalRetrievalChain.from_llm(llm, retriever)
     result = chain({'question': query, 'chat_history': st.session_state['history']})