vs

app.py

@@ -105,13 +105,9 @@ if submit_button:
     if question:  # Check if there is a question typed
         # Process the question here (a placeholder answer is used in this example)
 
-        #Awnser = rag_chain.invoke(question)
-        #contexts = retriever.get_relevant_documents(question)
-        #output_pane.object = Awnser 
-
-
-
-        answer = "This is a placeholder answer to your question."
-        answer_placeholder.text(answer)  # Display the answer
+        Awnser = rag_chain.invoke(question)
+        contexts = retriever.get_relevant_documents(question)
+         
+        answer_placeholder.text(Awnser)  # Display the answer
     else:
         answer_placeholder.warning("Please type a question.")

start.py

@@ -8,94 +8,96 @@ try:
     import requests
     #import fitz  # PyMuPDF
     
+    if 'retriever' not in st.session_state:
+    
 
+        os.environ["OPENAI_API_KEY"]  = os.getenv('openkey')
+        org = os.getenv('openorg')
 
 
-
-    # set up key
-#        with open('key.txt', 'r') as file:
-#            openai_api_key = file.read().strip()  # strip() removes any leading/trailing whitespace
-    
+        from langchain.embeddings import OpenAIEmbeddings
+        from langchain.vectorstores import Weaviate
+        import weaviate
+        from weaviate.embedded import EmbeddedOptions
+        #from dotenv import load_dotenv,find_dotenv
+        
+        # Load OpenAI API key from .env file
+        #load_dotenv(find_dotenv())
+        
+        # Setup vector database|
+        client = weaviate.Client(
+        embedded_options = EmbeddedOptions()
+        )
+        ############################################################################
+        
+        
+        pickle_file_path = 'vectorstore.pkl'
+        import pickle
+        
+        with open(pickle_file_path, 'rb') as file:
+            docs = pickle.load(file)
 
 
-    os.environ["OPENAI_API_KEY"]  = os.getenv('openkey')
-    org = os.getenv('openorg')
+        
+        vectorstore = Weaviate.from_documents(
+            client = client,    
+            documents = docs,
+            embedding = OpenAIEmbeddings(),
+            by_text = False
+        )
+        
+        # Define vectorstore as retriever to enable semantic search
+        retriever = vectorstore.as_retriever()
 
 
-    from langchain.embeddings import OpenAIEmbeddings
-    from langchain.vectorstores import Weaviate
-    import weaviate
-    from weaviate.embedded import EmbeddedOptions
-    #from dotenv import load_dotenv,find_dotenv
-    
-    # Load OpenAI API key from .env file
-    #load_dotenv(find_dotenv())
-    
-    # Setup vector database|
-    client = weaviate.Client(
-    embedded_options = EmbeddedOptions()
-    )
     ############################################################################
-    
-    
-    pickle_file_path = 'vectorstore.pkl'
-    import pickle
-    
-    with open(pickle_file_path, 'rb') as file:
-        docs = pickle.load(file)
 
+        
+        # Create RAG chain
+        
+        #from langchain.chat_models import ChatOpenAI
+        
+        from langchain_openai import ChatOpenAI
+        
+        from langchain.prompts import ChatPromptTemplate
+        from langchain.schema.runnable import RunnablePassthrough
+        from langchain.schema.output_parser import StrOutputParser
+        
+        # Define LLM
+        llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)
+        
+        #llm = ChatOpenAI(model_name="gpt-4", temperature=0.15)
+        
+        
+        # Define prompt template / context
+        template = """You are a lawyer responding to creditors questions. 
+        Use the following pieces of retrieved context to answer the question. 
+        If you don't know the answer, just say that you don't know and add a funny joke. 
+        Keep the answer concise.
+        Question: {question} 
+        Context: {context} 
+        Answer:
+        """
+        
+        prompt = ChatPromptTemplate.from_template(template)
+        
+        # Setup RAG pipeline
+        rag_chain = (
+            {"context": retriever,  "question": RunnablePassthrough()} 
+            | prompt 
+            | llm
+            | StrOutputParser() 
+        )
 
-    
-    vectorstore = Weaviate.from_documents(
-        client = client,    
-        documents = docs,
-        embedding = OpenAIEmbeddings(),
-        by_text = False
-    )
-    
-    # Define vectorstore as retriever to enable semantic search
-    retriever = vectorstore.as_retriever()
 
+        st.session_state.retriever = retriever
+        st.session_state.retriever = rag_chain
 
-############################################################################
+    else:
+        retriever = st.session_state.retriever
+        rag_chain = st.session_state.retriever
 
-    
-    # Create RAG chain
-    
-    #from langchain.chat_models import ChatOpenAI
-    
-    from langchain_openai import ChatOpenAI
-    
-    from langchain.prompts import ChatPromptTemplate
-    from langchain.schema.runnable import RunnablePassthrough
-    from langchain.schema.output_parser import StrOutputParser
-    
-    # Define LLM
-    llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)
-    
-    #llm = ChatOpenAI(model_name="gpt-4", temperature=0.15)
-    
-    
-    # Define prompt template / context
-    template = """You are a lawyer responding to creditors questions. 
-    Use the following pieces of retrieved context to answer the question. 
-    If you don't know the answer, just say that you don't know and add a funny joke. 
-    Keep the answer concise.
-    Question: {question} 
-    Context: {context} 
-    Answer:
-    """
-    
-    prompt = ChatPromptTemplate.from_template(template)
-    
-    # Setup RAG pipeline
-    rag_chain = (
-        {"context": retriever,  "question": RunnablePassthrough()} 
-        | prompt 
-        | llm
-        | StrOutputParser() 
-    )
 
-    em = "aaa"
+        em = "aaa"
 except Exception as e:
     em = "bbb" + str(e)