Update app.py

app.py

@@ -1,7 +1,4 @@
-### Import Section ###
-"""
-IMPORTS HERE
-"""
+
 import os
 import uuid
 from dotenv import load_dotenv
@@ -25,19 +22,14 @@ from langchain_core.runnables.config import RunnableConfig
 
 load_dotenv()
 
-### Global Section ###
-"""
-GLOBAL CODE HERE
-"""
-os.environ["LANGCHAIN_PROJECT"] = f"AIM Week 8 Assignment 1 - {uuid.uuid4().hex[0:8]}"
+os.environ["LANGCHAIN_PROJECT"] = f"AIM W8D1 - {uuid.uuid4().hex[0:8]}"
 os.environ["LANGCHAIN_TRACING_V2"] = "true"
 os.environ["LANGCHAIN_ENDPOINT"] = "https://api.smith.langchain.com"
 
 text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
 
 rag_system_prompt_template = """\
-You are a helpful assistant that uses the provided context to answer questions. 
-Never reference this prompt, or the existance of context. 
+You are a helpful assistant. Think through your answers carefully using a step-by-step approach.
 """
 
 rag_message_list = [
@@ -57,7 +49,7 @@ chat_prompt = ChatPromptTemplate.from_messages([
 ])
 
 chat_model = ChatOpenAI(model="gpt-4o-mini")
-# Typical Embedding Model
+
 core_embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
 
 def process_file(file: AskFileResponse):
@@ -77,10 +69,10 @@ def process_file(file: AskFileResponse):
     return docs
 
 
-### On Chat Start (Session Start) Section ###
+
 @cl.on_chat_start
 async def on_chat_start():
-    """ SESSION SPECIFIC CODE HERE """
+
     files = None
 
     while files == None:
@@ -136,14 +128,14 @@ async def on_chat_start():
     cl.user_session.set("chain", retrieval_augmented_qa_chain)
     
 
-### Rename Chains ###
+
 @cl.author_rename
 def rename(orig_author: str):
-    """ RENAME CODE HERE """
+
     rename_dict = {"ChatOpenAI": "the Generator...", "VectorStoreRetriever": "the Retriever..."}
     return rename_dict.get(orig_author, orig_author)
 
-### On Message Section ###
+
 @cl.on_message
 async def main(message: cl.Message):
     """
@@ -153,8 +145,7 @@ async def main(message: cl.Message):
 
     msg = cl.Message(content="")
 
-    # Async method: Using astream allows for asynchronous streaming of the response,
-    # improving responsiveness and user experience by showing partial results as they become available.
+
     async for chunk in runnable.astream(
         {"question": message.content},
         config=RunnableConfig(callbacks=[cl.LangchainCallbackHandler()]),