Updating app

app.py

@@ -224,9 +224,6 @@ workflow.add_conditional_edges(
     }
 )
 
-workflow.add_edge("use tool", "agent")
-app = workflow.compile()
-
 @cl.author_rename
 def rename(original_author: str):
     """
@@ -241,30 +238,10 @@ def rename(original_author: str):
 
 @cl.on_chat_start
 async def start_chat():
-    """
-    This function will be called at the start of every user session. 
-
-    We will build our LCEL RAG chain here, and store it in the user session. 
+    workflow.add_edge("use tool", "agent")
+    app = workflow.compile()
 
-    The user session is a dictionary that is unique to each user session, and is stored in the memory of the server.
-    """
-
-    #-----DEFINE THE AGENT INVOCATION-----#
-    rag_agent = (
-        # INVOKE CHAIN WITH: {"question" : "<<SOME USER QUESTION>>"}
-        # "question" : populated by getting the value of the "question" key
-        # "context"  : populated by getting the value of the "question" key and chaining it into the base_retriever
-        {"context": itemgetter("question") | retriever, "question": itemgetter("question")}
-        # "context"  : is assigned to a RunnablePassthrough object (will not be called or considered in the next step)
-        #              by getting the value of the "context" key from the previous step
-        | RunnablePassthrough.assign(context=itemgetter("context"))
-        # "response" : the "context" and "question" values are used to format our prompt object and then piped
-        #              into the LLM and stored in a key called "response"
-        # "context"  : populated by getting the value of the "context" key from the previous step
-        | {"response": prompt | primary_qa_llm, "context": itemgetter("context")}
-    )
-
-    cl.user_session.set("agent", agent)
+    cl.user_session.set("agent", app)
 
 @cl.on_message  
 async def main(message: cl.Message):
@@ -275,15 +252,13 @@ async def main(message: cl.Message):
 
     The LCEL RAG chain is stored in the user session, and is unique to each user session - this is why we can access it here.
     """
-    lcel_rag_chain = cl.user_session.get("lcel_rag_chain")
+    agent = cl.user_session.get("agent")
 
     msg = cl.Message(content="")
 
-    #inputs = 
-
-    messages = app.invoke({"messages" : [HumanMessage(content=message)]})
+    messages = agent.invoke({"messages" : [HumanMessage(content=message)]})
 
-    async for chunk in lcel_rag_chain.astream(
+    async for chunk in agent.astream(
         {"query": message.content},
         config=RunnableConfig(callbacks=[cl.LangchainCallbackHandler()]),
     ):