TaskingAI实践（一）快速上手

TaskingAI实践-20240912:

20240912 写在前面我们一直走一直看，路过的风景，一闪而过的瞬间，终究还是会留下瞬间印记。不学习不代表它不存在，学习了不代表它就直接可以用，不用不代表它没有用。说到最深处，人生就是一场体验。

TaskingAI：

TaskingAI 是一个基于大语言模型 (LLM) 的开发与部署平台，提供统一的 API 接入数百个 AI 模型，并通过直观的用户界面管理功能模块，如工具、RAG 系统、助手等。其主要特点包括一键部署、异步高效处理、集成各种 LLM 模型和插件。支持状态和无状态的使用方式，帮助开发者轻松构建多租户 AI 应用。通过 Docker 快速启动，也提供 SDK 与 API 进行编程交互。

更多信息可以查看TaskingAI

快速上手Quickstart with Docker

A simple way to initiate self-hosted TaskingAI community edition is through Docker.

前置环境准备Prerequisites

Docker环境，Docker and Docker Compose installed on your machine.
GIT环境，Git installed for cloning the repository.
Python环境>3.8，Python environment (above Python 3.8) for running the client SDK.

安装 Installation

从GitHub下载项目源代码

First, clone the TaskingAI (community edition) repository from GitHub.

git clone https://github.com/taskingai/taskingai.git
cd taskingai

进入到项目仓库，进入到docker目录，

Inside the cloned repository, go to the docker directory.

cd docker

Copy .env.example to .env:
```
cp .env.example .env
```
Edit the .env file : Open the .env file in your favorite text editor and update the necessary configurations. Ensure all required environment variables are set correctly.
Start Docker Compose: Run the following command to start all services:
```
docker-compose -p taskingai --env-file .env up -d
```

项目启动后直接访问， http://localhost:8080。默认用户名和密码是 admin and TaskingAI321.

Once the service is up, access the TaskingAI console through your browser with the URL http://localhost:8080. The default username and password are admin and TaskingAI321.

升级操作 Upgrade

If you have already installed TaskingAI with a previous version and want to upgrade to the latest version, first update the repository.

git pull origin master

Then stop the current docker service, upgrade to the latest version by pulling the latest image, and finally restart the service.

cd docker
docker-compose -p taskingai down
docker-compose -p taskingai pull
docker-compose -p taskingai --env-file .env up -d

Don't worry about data loss; your data will be automatically migrated to the latest version schema if needed.

问题：

8080端口占用

实践遇到的docker端口占用问题，当然和taskingAI本身无关，是环境问题，解决端口冲突即可。

bash 复制代码

➜  docker git:(master) pwd
/Users/zhizhou/Documents/docker_home/taskingai/docker

➜  docker git:(master) docker-compose -p taskingai --env-file .env up -d
[+] Running 7/8
 ⠿ Container taskingai-cache-1              Running                                            0.0s
 ⠿ Container taskingai-db-1                 Running                                            0.0s
 ⠿ Container taskingai-backend-inference-1  Start...                                          21.0s
 ⠿ Container taskingai-backend-plugin-1     Started                                           21.0s
 ⠿ Container taskingai-backend-web-1        Started                                           11.2s
 ⠿ Container taskingai-backend-api-1        Started                                           11.2s
 ⠿ Container taskingai-frontend-1           Started                                            1.3s
 ⠿ Container taskingai-nginx-1              Starting                                           1.2s
Error response from daemon: Ports are not available: exposing port TCP 0.0.0.0:8080 -> 0.0.0.0:0: listen tcp 0.0.0.0:8080: bind: address already in use

上述终端表示端口被占用，检查一下是否有正在启动的Java程序或者直接查看端口8080的使用情况。