KitHung/intern_LlamaIndex

1import streamlit as st2from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings3from llama_index.embeddings.huggingface import HuggingFaceEmbedding4from llama_index.legacy.callbacks import CallbackManager5from llama_index.llms.openai_like import OpenAILike6from download import prepare_data7 8# prepare datas9prepare_data()10 11# Create an instance of CallbackManager12callback_manager = CallbackManager()13 14# api_base_url =  "https://internlm-chat.intern-ai.org.cn/puyu/api/v1/"15# model = "internlm2.5-latest"16# api_key = st.secrets["API_KEY"]17 18api_base_url =  "https://api.siliconflow.cn/v1"19model = "internlm/internlm2_5-7b-chat"20api_key = st.secrets["API_KEY"]21 22llm =OpenAILike(model=model, api_base=api_base_url, api_key=api_key, is_chat_model=True,callback_manager=callback_manager)23 24 25 26st.set_page_config(page_title="llama_index_demo", page_icon="🦜🔗")27st.title("llama_index_demo")28 29# 初始化模型30@st.cache_resource31def init_models():32    embed_model = HuggingFaceEmbedding(33        model_name="/home/user/model/paraphrase-multilingual-MiniLM-L12-v2"34    )35    Settings.embed_model = embed_model36 37    #用初始化llm38    Settings.llm = llm39 40    documents = SimpleDirectoryReader("/home/user/data").load_data()41    index = VectorStoreIndex.from_documents(documents)42    query_engine = index.as_query_engine()43 44    return query_engine45 46# 检查是否需要初始化模型47if 'query_engine' not in st.session_state:48    st.session_state['query_engine'] = init_models()49 50def greet2(question):51    response = st.session_state['query_engine'].query(question)52    return response53 54      55# Store LLM generated responses56if "messages" not in st.session_state.keys():57    st.session_state.messages = [{"role": "assistant", "content": "你好，我是你的助手，有什么我可以帮助你的吗？"}]    58 59    # Display or clear chat messages60for message in st.session_state.messages:61    with st.chat_message(message["role"]):62        st.write(message["content"])63 64def clear_chat_history():65    st.session_state.messages = [{"role": "assistant", "content": "你好，我是你的助手，有什么我可以帮助你的吗？"}]66 67st.sidebar.button('Clear Chat History', on_click=clear_chat_history)68 69# Function for generating LLaMA2 response70def generate_llama_index_response(prompt_input):71    return greet2(prompt_input)72 73# User-provided prompt74if prompt := st.chat_input():75    st.session_state.messages.append({"role": "user", "content": prompt})76    with st.chat_message("user"):77        st.write(prompt)78 79# Gegenerate_llama_index_response last message is not from assistant80if st.session_state.messages[-1]["role"] != "assistant":81    with st.chat_message("assistant"):82        with st.spinner("Thinking..."):83            response = generate_llama_index_response(prompt)84            placeholder = st.empty()85            placeholder.markdown(response)86    message = {"role": "assistant", "content": response}87    st.session_state.messages.append(message)