cl.make_sync

app.py

@@ -11,6 +11,7 @@ from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.prompts import ChatPromptTemplate
 from langchain.agents import Tool
 from langchain.agents import ZeroShotAgent, AgentExecutor
+from langchain.chat_models import ChatOpenAI
 from langchain import LLMChain
 
 # text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
@@ -38,6 +39,9 @@ from langchain import LLMChain
 # prompt = ChatPromptTemplate(messages=messages)
 # chain_type_kwargs = {"prompt": prompt}
 
+import os
+os.environ["OPENAI_API_KEY"] = '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'
+
 @cl.author_rename
 def rename(orig_author: str):
     rename_dict = {"RetrievalQA": "Consulting The Barbenheimer"}
@@ -45,9 +49,11 @@ def rename(orig_author: str):
 
 @cl.on_chat_start
 async def init():
+    
     msg = cl.Message(content=f"Building Index...")
     await msg.send()
     
+    llm = ChatOpenAI(model="gpt-3.5-turbo", temperature = 0)
     
     # set up text splitters
     wikipedia_text_splitter = RecursiveCharacterTextSplitter(
@@ -172,7 +178,7 @@ async def init():
         "source2": (lambda x: x["question"]) | opp_csv_faiss_retriever,
         "question": lambda x: x["question"],
     } | prompt | llm
-    
+
     
     # Agent creation
     # set up tools
@@ -188,7 +194,7 @@ async def init():
         ),
         Tool(
             name = "OppenheimerInfo",
-            func= query_oppenheimer,
+            func=query_oppenheimer,
             description='Useful when you need to answer questions about the Oppenheimer movie' 
         ),
     ]
@@ -216,9 +222,18 @@ async def init():
         agent=barbenheimer_agent,
         tools=tools, 
         verbose=True )
+    
+    cl.user_session.set("chain", barbenheimer_agent_chain)
+
+    msg.content = f"Agent ready!"
+    await msg.send()
 
 @cl.on_message
 async def main(message):
+       
+    msg = cl.Message(content=f"Thinking...")
+    await msg.send()
+    
     chain = cl.user_session.get("chain")
     cb = cl.AsyncLangchainCallbackHandler(
         stream_final_answer=False, answer_prefix_tokens=["FINAL", "ANSWER"]