【chainlit】使用chainlit部署chatgpt

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一.chainlit 简介

1.官方文档

官方文档

github

langchain 方式

python

docker 启动 chainlit

2.python 安装

在 centos 服务器上安装 python3.10

安装依赖

#安装依赖库
sudo yum install gcc openssl-devel bzip2-devel libffi-devel zlib-devel wget sqlite-devel#下载python
wget https://www.python.org/ftp/python/3.10.0/Python-3.10.0.tgz#解压
tar -zxvf Python-3.10.0.tgz

安装python3.10

#进入目录
cd Python-3.10.0#校验
./configure --enable-optimizations#编译
make -j 8#安装
sudo make altinstall

验证

#验证
python3.10 --version

3.安装 chainlit

创建虚拟环境:

#创建虚拟环境
python3.10 -m venv myenv#激活虚拟环境
source myenv/bin/activate#退出虚拟环境
deactivate

安装依赖

#安装chainlit
pip install chainlit#安装langchain
pip install langchain

创建azure_demo.py文件,内容如下

import os
import chainlit as clfrom langchain.chat_models import ChatOpenAI
from langchain.schema import (HumanMessage,SystemMessage
)# 公司的key
os.environ["OPENAI_API_KEY"] = 'xxxxx'
os.environ["OPENAI_API_BASE"] = 'https://opencatgpt.openai.azure.com/'
os.environ["OPENAI_API_TYPE"] = 'azure'
os.environ["OPENAI_API_VERSION"] = '2023-05-15'chat = ChatOpenAI(model_name="gpt-35-turbo", engine="gpt-35-turbo")
history = [SystemMessage(content="你是一个聊天机器人，请回答下列问题。\n")]@cl.on_message  # this function will be called every time a user inputs a message in the UI
async def main(message: str):# history = [SystemMessage(content="你是一个聊天机器人，请回答下列问题。\n")]history.append(HumanMessage(content=message))res = await cl.make_async(sync_func)()# res = chat(history)# print(res.content)# this is an intermediate step# await cl.Message(author="Tool 1", content=f"Response from tool1", indent=1).send()# send back the final answerhistory.append(res)await cl.Message(content=f"{res.content}").send()def sync_func():return chat(history)

方式二:

import openai
import chainlit as clopenai.proxy = 'http://127.0.0.1:7890'
openai.api_key = "xxxx"
# model_name = "text-davinci-003"
model_name = "gpt-3.5-turbo"
settings = {"temperature": 0.7,"max_tokens": 500,"top_p": 1,"frequency_penalty": 0,"presence_penalty": 0,
}@cl.on_chat_start
def start_chat():cl.user_session.set("message_history",[{"role": "system", "content": "You are a helpful assistant."}],)@cl.on_message
async def main(message: str):message_history = cl.user_session.get("message_history")message_history.append({"role": "user", "content": message})msg = cl.Message(content="")async for stream_resp in await openai.ChatCompletion.acreate(model=model_name, messages=message_history, stream=True, **settings):token = stream_resp.choices[0]["delta"].get("content", "")await msg.stream_token(token)message_history.append({"role": "assistant", "content": msg.content})await msg.send()

4.启动脚本

nohup chainlit run azure_demo.py &

5.一键启动

echo `ps -ef | grep azure_demo | grep -v grep | awk '{print $2}'`
kill -9  `ps -ef | grep azure_demo | grep -v grep | awk '{print $2}'`
cd /kwan/chainlit
python3.10 -m venv myenv
source myenv/bin/activate
nohup  chainlit run azure_demo.py >/dev/null 2>&1 & exit

二.docker 部署

1.github 地址

Github

gitcode

2.Dockerfile

FROM python:3.11-slim-buster as builder#RUN apt-get update && apt-get install -y gitRUN pip install poetry==1.4.2 -i https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple/ \
&& pip install DBUtils==3.0.3 -i https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple/ \
&& pip install PyMySQL==1.1.0 -i https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple/ENV POETRY_NO_INTERACTION=1 \POETRY_VIRTUALENVS_IN_PROJECT=1 \POETRY_VIRTUALENVS_CREATE=1 \POETRY_CACHE_DIR=/tmp/poetry_cacheENV HOST=0.0.0.0
ENV LISTEN_PORT 8000
EXPOSE 8000WORKDIR /appCOPY pyproject.toml poetry.lock ./RUN poetry config repositories.clearlydefined https://pypi.tuna.tsinghua.edu.cn/simple/RUN poetry config cache-dir /kwan/chainlit/demoRUN poetry config virtualenvs.create falseRUN poetry install  --without dev --no-root && rm -rf $POETRY_CACHE_DIR# The runtime image, used to just run the code provided its virtual environment
FROM python:3.11-slim-buster as runtimeENV VIRTUAL_ENV=/app/.venv \PATH="/app/.venv/bin:$PATH"COPY --from=builder ${VIRTUAL_ENV} ${VIRTUAL_ENV}COPY ./demo_app ./demo_app
COPY ./.chainlit ./.chainlit
COPY chainlit.md ./CMD ["chainlit", "run", "demo_app/main.py"]

3.新增依赖

#在pyproject.toml中新增依赖
[tool.poetry.dependencies]
python = "^3.10"
langchain = "0.0.199"
openai = "0.27.8"
chainlit = "0.5.2"
DBUtils = "3.0.3"
PyMySQL = "1.1.0"#执行poetry update会更新poetry.lock文件
poetry update

4.部署步骤

#创建缓存目录
mkdir -p /kwan/chainlit#进入目录
cd  /kwan/chainlit#下载源码
git clone https://github.com/amjadraza/langchain-chainlit-docker-deployment-template#进入目录
cd /kwan/chainlit/langchain-chainlit-docker-deployment-template#修改代码
/kwan/chainlit/langchain-chainlit-docker-deployment-template/demo_app#构建镜像
DOCKER_BUILDKIT=1 docker build --target=runtime . -t langchain-chainlit-chat-app:latest#启动容器
docker run -d --name langchain-chainlit-chat-app -p 8000:8000 langchain-chainlit-chat-app#删除容器
docker rm -f langchain-chainlit-chat-app#容器日志
docker logs -f langchain-chainlit-chat-app#所有容器
docker ps -a

5.修改配置

#修改chainlit的配置
cd /kwan/chainlit/.chainlit#修改markdown文件
cd  /kwan/chainlit

6.访问验证

#页面验证
http://120.79.36.53:8000/#公司内网地址
http://10.201.0.6:8000/

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