Update app.py

app.py

@@ -39,9 +39,14 @@ terminators = [
     tokenizer.convert_tokens_to_ids("<|eot_id|>")
 ]
 
-SYS_PROMPT = """You are an assistant for answering questions.
-You are given the extracted parts of a long document and a question. Provide a conversational answer.
-If you don't know the answer, just say "I do not know." Don't make up an answer."""
+SYS_PROMPT = """
+ "Below is an instruction that describes a task. Write a response that appropriately completes the request."\
+    "제시하는 context에서만 대답하고 context에 없는 내용은 생성하지마"\
+    "make answer in korean. 한국어로 대답하세요"\
+    "\n\nContext:\n{context}\n;"\
+    "Question: {question}"\
+    "\n\nAnswer:"
+"""
 
 
 
@@ -112,8 +117,8 @@ A rag pipeline with a chatbot feature
 Resources used to build this project :
 * embedding model : https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1
 * dataset : https://huggingface.co/datasets/not-lain/wikipedia
-* faiss docs : https://huggingface.co/docs/datasets/v2.18.0/en/package_reference/main_classes#datasets.Dataset.add_faiss_index 
-* chatbot : https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct
+* faiss docs : 
+* chatbot : 
 * Full documentation : https://huggingface.co/blog/not-lain/rag-chatbot-using-llama3 
 """
 
@@ -129,7 +134,7 @@ demo = gr.ChatInterface(
         bubble_full_width=False,
     ),
     theme="Soft",
-    examples=[["what's anarchy ? "]],
+    examples=[["풋살 경기 규칙이 뭐야? "]],
     title=TITLE,
     description=DESCRIPTION,