Update chatbot_rag.py

chatbot_rag.py

@@ -3,6 +3,7 @@ from langchain_community.embeddings import HuggingFaceEmbeddings
 from langchain_community.llms import HuggingFacePipeline
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
 from langchain.chains import RetrievalQA
+from langchain.prompts import PromptTemplate
 import traceback
 
 def build_qa():
@@ -39,13 +40,29 @@ def build_qa():
     # 4. QA Chain with retrieval
     print("🔹 Building RetrievalQA...")
     retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
+    
+
+    template = """
+    Use the following context to answer the question at the end. 
+    If you don't know the answer, just say "I don't know" — do not make up an answer.
+    
+    Context:
+    {context}
+    
+    Question: {question}
+    Answer (one short sentence):
+    """
+    qa_prompt = PromptTemplate(template=template, input_variables=["context", "question"])
+    
     qa = RetrievalQA.from_chain_type(
         llm=llm,
         retriever=retriever,
+        chain_type="stuff",
+        chain_type_kwargs={"prompt": qa_prompt},
         return_source_documents=False,
-        chain_type="stuff"  # simplest chain, passes context + question
     )
 
+
     print("✅ QA pipeline ready.")
     return qa