#test that vector database is working
retriever = db.as_retriever(search_kwargs={"k": 3})

#create a prompt template
template = """<|user|>
Relevant information:
{context}

Provide a concise answer to the following question using relevant information provided above:
{question}
If the information above does not answer the question, say that you do not know. Keep answers to 3 sentences or shorter.<|end|>
<|assistant|>"""

#define prompt template
prompt = PromptTemplate(
    template=template,
    input_variables=["context", "question"])

#create RAG pipeline
rag = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, return_source_documents=True,
                                  chain_type_kwargs={"prompt": prompt}, verbose = True)

#test rag response
rag.invoke("What are the most recent advancements in computer vision?")