Update app.py

app.py

@@ -24,7 +24,7 @@ TOKEN = os.getenv("HF_TOKEN")
 def load_embedding_mode():
     # embedding_model_dict = {"m3e-base": "/home/xiongwen/m3e-base"}
     encode_kwargs = {"normalize_embeddings": False}
-    model_kwargs = {"device": 'cuda'}
+    model_kwargs = {"device": 'cpu'}
     return HuggingFaceEmbeddings(model_name="BAAI/bge-m3",
                                  model_kwargs=model_kwargs,
                                  encode_kwargs=encode_kwargs)
@@ -34,6 +34,15 @@ client = OpenAI(
 )
 embedding = load_embedding_mode()
 db = Chroma(persist_directory='/VecterStore2_512_txt/VecterStore2_512_txt', embedding_function=embedding)
+prompt_template = """
+    {context}
+    The above content is a form of biological background knowledge. Please answer the questions according to the above content. Please be sure to answer the questions according to the background knowledge and attach the doi number of the information source when answering.
+    Question: {question}
+    Answer in English:"""
+    PROMPT = PromptTemplate(
+        template=prompt_template, input_variables=["context", "question"]
+    )
+    chain_type_kwargs = {"prompt": PROMPT}
 
 def respond(
     message,