Updated incorrect variable name

app.py

@@ -64,45 +64,38 @@ retriever = qdrant_vector_store.as_retriever()
 """
 ### 1. DEFINE STRING TEMPLATE
 RAG_PROMPT_TEMPLATE = """\
-<|start_header_id|>system<|end_header_id|>
+system
 You are a helpful assistant. You answer user questions based on provided context. If you can't answer the question with the provided context,\
-    say you don't know.<|eot_id|>
-<|start_header_id|>user<|end_header_id|>
+    say you don't know.
+user
 User Query:
 {query}
 Context:
-{context}<|eot_id|>
-<|start_header_id|>assistant<|end_header_id|>
+{context}
+assistant
 """
-#Note that we do not have the response here. We have assistent, we ONLY start, but not followed by <|eot_id> as we do not have a response YET.
+# Note that we do not have the response here. We have assistant, we ONLY start, but not followed by <|eot_id> as we do not have a response YET.
 
 rag_prompt = ChatPromptTemplate.from_template(RAG_PROMPT_TEMPLATE)
 
-
 # Define the LLM
 llm = ChatOpenAI(model_name="gpt-4o")
 
-#-----Creating a Retrieval Augmented Generation (RAG) Chain-----#
-# The RAG chain:
-# (1) Takes the user question and retrieves relevant context, 
-# (2) Passes the context through unchanged, 
-# (3) Formats the prompt with context and question, then send it to the LLM to generate a response
-
 retrieval_augmented_qa_chain = (
-    # INVOKE CHAIN WITH: {"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")}
+    # INVOKE CHAIN WITH: {"query" : "<>"}
+    # "query" : populated by getting the value of the "question" key
+    # "context"  : populated by getting the value of the "query" key and chaining it into the base_retriever
+    {"context": itemgetter("query") | retriever, "query": itemgetter("query")}
     # "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
+    # "response" : the "context" and "query" 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": rag_prompt | llm, "context": itemgetter("context")}
 )
 
-# Sets initial chat settings at the start of a user session
+# Sets initial chat settings 
 @cl.on_chat_start  
 async def start_chat():
     """
@@ -120,20 +113,16 @@ async def start_chat():
     }
     cl.user_session.set("settings", settings)
 
-# Processes incoming messages from the user and sends a response through a series of steps:
-# (1) Retrieves the user's settings
-# (2) Invokes the RAG chain with the user's message
-# (3) Extracts the content from the response and sends it back to the user
+# Initializes user session w/ settings and retrieves context based on query
 
 @cl.on_message 
 async def handle_message(message: cl.Message):
     settings = cl.user_session.get("settings")
 
-    response = retrieval_augmented_qa_chain.invoke({"question": message.content})
-
+    response = retrieval_augmented_qa_chain.invoke({"query": message.content})
 
     # Extracting and sending just the content
     content = response["response"].content
     pretty_content = content.strip()  
 
-    await cl.Message(content=pretty_content).send()
+    await cl.Message(content=pretty_content).send()