Upload app.py

app.py

@@ -66,7 +66,7 @@ llm = HuggingFacePipeline(pipeline=pipe)
 # --- Step 7: Setup memory and QA chain ---
 memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
 
-prompt = PromptTemplate.from_template("""
+custom_prompt = PromptTemplate.from_template("""
 You are a helpful assistant at the University of Hertfordshire. Use the context below to answer the question clearly and factually.
 If the answer is not in the context, say you don't know.
 
@@ -76,15 +76,15 @@ Context:
 Question:
 {question}
 
-Helpful Answer:
+Answer:
 """)
 
 qa_chain = ConversationalRetrievalChain.from_llm(
     llm=llm,
     retriever=vectordb.as_retriever(search_kwargs={"k": 3}),
     memory=memory,
-    chain_type="map_reduce",
-    combine_docs_chain_kwargs={"combine_prompt": prompt}
+    chain_type="stuff",
+    combine_docs_chain_kwargs={"prompt": custom_prompt}
 )
 
 # --- Step 8: Define chatbot logic ---