llamaindex+Internlm2 RAG实践

news2025/1/3 0:58:09

llamaindex+Internlm2 RAG实践

环境、模型准备

进入开发机后，从官方环境复制运行 InternLM 的基础环境，命名为 llamaindex，在命令行模式下运行：

conda create -n llamaindex python=3.10

运行 conda 命令，激活 llamaindex 然后安装相关基础依赖 python 虚拟环境:

conda activate llamaindex
conda install pytorch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 pytorch-cuda=11.7 -c pytorch -c nvidia

安装 Llamaindex

安装 Llamaindex和相关的包

conda activate llamaindex
pip install llama-index==0.10.38 llama-index-llms-huggingface==0.2.0 "transformers[torch]==4.41.1" "huggingface_hub[inference]==0.23.1" huggingface_hub==0.23.1 sentence-transformers==2.7.0 sentencepiece==0.2.0  protobuf     einops

下载 Sentence Transformer 模型
源词向量模型 Sentence Transformer:（我们也可以选用别的开源词向量模型来进行 Embedding，目前选用这个模型是相对轻量、支持中文且效果较好的，同学们可以自由尝试别的开源词向量模型）运行以下指令，新建一个python文件,贴入一下代码。

import os
from modelscope.hub.snapshot_download import snapshot_download
# 创建保存模型目录
os.system("mkdir /root/models")
# save_dir是模型保存到本地的目录
save_dir="/root/model"
path="Ceceliachenen/paraphrase-multilingual-MiniLM-L12-v2"
snapshot_download(path, 
                  cache_dir=save_dir)

运行代码即可下载到root下边的models文件夹下边。

下载 NLTK 相关资源

使用以下命令：

cd /root
git clone https://gitee.com/yzy0612/nltk_data.git  --branch gh-pages
cd nltk_data
mv packages/*  ./
cd tokenizers
unzip punkt.zip
cd ../taggers
unzip averaged_perceptron_tagger.zip

LlamaIndex HuggingFaceLLM

运行以下指令，把 InternLM2 1.8B 软连接出来

cd ~/model
ln -s /root/share/new_models/Shanghai_AI_Laboratory/internlm2-chat-1_8b/ ./

运行以下指令，新建一个python文件

cd ~/llamaindex_demo
touch llamaindex_internlm.py

打开llamaindex_internlm.py 贴入以下代码

from llama_index.llms.huggingface import HuggingFaceLLM
from llama_index.core.llms import ChatMessage
llm = HuggingFaceLLM(
    model_name="/root/model/internlm2-chat-1_8b",
    tokenizer_name="/root/model/internlm2-chat-1_8b",
    model_kwargs={"trust_remote_code":True},
    tokenizer_kwargs={"trust_remote_code":True}
)

rsp = llm.chat(messages=[ChatMessage(content="xtuner是什么？")])
print(rsp)

之后运行

conda activate llamaindex
cd ~/llamaindex_demo/
python llamaindex_internlm.py

在这里插入图片描述

LlamaIndex RAG

安装 LlamaIndex 词嵌入向量依赖

conda activate llamaindex
pip install llama-index-embeddings-huggingface llama-index-embeddings-instructor

运行以下命令，获取知识库

cd ~/llamaindex_demo
mkdir data
cd data
git clone https://github.com/InternLM/xtuner.git
mv xtuner/README_zh-CN.md ./

运行以下指令，新建一个python文件

cd ~/llamaindex_demo
touch llamaindex_RAG.py

打开llamaindex_RAG.py贴入以下代码

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings

from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.llms.huggingface import HuggingFaceLLM

embed_model = HuggingFaceEmbedding(
    model_name="/root/model/Ceceliachenen/paraphrase-multilingual-MiniLM-L12-v2"
)

Settings.embed_model = embed_model

llm = HuggingFaceLLM(
    model_name="/root/model/internlm2-chat-1_8b",
    tokenizer_name="/root/model/internlm2-chat-1_8b",
    model_kwargs={"trust_remote_code":True},
    tokenizer_kwargs={"trust_remote_code":True}
)
Settings.llm = llm

documents = SimpleDirectoryReader("/root/llamaindex_demo/data").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
response = query_engine.query("xtuner是什么?")

print(response)

之后运行

conda activate llamaindex
cd ~/llamaindex_demo/
python llamaindex_RAG.py

结果为：
在这里插入图片描述

LlamaIndex web

运行之前首先安装依赖

pip install streamlit==1.36.0

运行以下指令，新建一个python文件

cd ~/llamaindex_demo
touch app.py

打开app.py贴入以下代码

import streamlit as st
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.llms.huggingface import HuggingFaceLLM

st.set_page_config(page_title="llama_index_demo", page_icon="🦜🔗")
st.title("llama_index_demo")

# 初始化模型
@st.cache_resource
def init_models():
    embed_model = HuggingFaceEmbedding(
        model_name="/root/model/Ceceliachenen/paraphrase-multilingual-MiniLM-L12-v2"
    )
    Settings.embed_model = embed_model

    llm = HuggingFaceLLM(
        model_name="/root/model/internlm2-chat-1_8b",
        tokenizer_name="/root/model/internlm2-chat-1_8b",
        model_kwargs={"trust_remote_code": True},
        tokenizer_kwargs={"trust_remote_code": True}
    )
    Settings.llm = llm

    documents = SimpleDirectoryReader("/root/llamaindex_demo/data").load_data()
    index = VectorStoreIndex.from_documents(documents)
    query_engine = index.as_query_engine()

    return query_engine

# 检查是否需要初始化模型
if 'query_engine' not in st.session_state:
    st.session_state['query_engine'] = init_models()

def greet2(question):
    response = st.session_state['query_engine'].query(question)
    return response

      
# Store LLM generated responses
if "messages" not in st.session_state.keys():
    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？"}]    

    # Display or clear chat messages
for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.write(message["content"])

def clear_chat_history():
    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？"}]

st.sidebar.button('Clear Chat History', on_click=clear_chat_history)

# Function for generating LLaMA2 response
def generate_llama_index_response(prompt_input):
    return greet2(prompt_input)

# User-provided prompt
if prompt := st.chat_input():
    st.session_state.messages.append({"role": "user", "content": prompt})
    with st.chat_message("user"):
        st.write(prompt)

# Gegenerate_llama_index_response last message is not from assistant
if st.session_state.messages[-1]["role"] != "assistant":
    with st.chat_message("assistant"):
        with st.spinner("Thinking..."):
            response = generate_llama_index_response(prompt)
            placeholder = st.empty()
            placeholder.markdown(response)
    message = {"role": "assistant", "content": response}
    st.session_state.messages.append(message)