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README
Apache-2.0

CodeShell IntelliJ IDEA Extension

codeshell-intellij项目是基于CodeShell大模型开发的支持IntelliJ IDEA、Pycharm、GoLang等多种IDE的智能编码助手插件,支持python、java、c++/c、javascript、go等多种编程语言,为开发者提供代码补全、代码解释、代码优化、注释生成、对话问答等功能,旨在通过智能化的方式帮助开发者提高编程效率。

环境要求

插件编译

如果要从源码进行打包,请先获取代码:

git clone https://github.com/WisdomShell/codeshell-intellij.git
  • 项目使用Gradle管理依赖,点击刷新按钮自动重新加载依赖
  • 本地运行插件:Gradle-CodeShell-Task-intellij-runIde
  • runIde右键,可选择使用Debug模式启动

插件DEBUG截图

打包插件

  • 本地生成插件安装包:Gradle-CodeShell-Task-intellij-buildPlugin
  • 打包任务执行完成后,插件安装包在项目根目录下的build/distributions目录中

安装插件

  • 安装入口:Settings-Plugins-Install Plugin from Disk...,在打开的资源选择窗口中选择插件安装包即可

插件安装截图

模型服务

llama_cpp_for_codeshell项目提供CodeShell大模型 4bits量化后的模型,模型名称为codeshell-chat-q4_0.gguf。以下为部署模型服务步骤:

编译代码

  • Linux / Mac(Apple Silicon设备)

    git clone https://github.com/WisdomShell/llama_cpp_for_codeshell.git
    cd llama_cpp_for_codeshell
    make

    在 macOS 上,默认情况下启用了Metal,启用Metal可以将模型加载到 GPU 上运行,从而显著提升性能。

  • Mac(非Apple Silicon设备)

    git clone https://github.com/WisdomShell/llama_cpp_for_codeshell.git
    cd llama_cpp_for_codeshell
    LLAMA_NO_METAL=1 make

    对于非 Apple Silicon 芯片的 Mac 用户,在编译时可以使用 LLAMA_NO_METAL=1LLAMA_METAL=OFF 的 CMake 选项来禁用Metal构建,从而使模型正常运行。

  • Windows

    您可以选择在Windows Subsystem for Linux中按照Linux的方法编译代码,也可以选择参考llama.cpp仓库中的方法,配置好w64devkit后再按照Linux的方法编译。

下载模型

Hugging Face Hub上,我们提供了三种不同的模型,分别是CodeShell-7BCodeShell-7B-ChatCodeShell-7B-Chat-int4。以下是下载模型的步骤。

  • 使用CodeShell-7B-Chat-int4模型推理,将模型下载到本地后并放置在以上代码中的 llama_cpp_for_codeshell/models 文件夹的路径
git clone https://huggingface.co/WisdomShell/CodeShell-7B-Chat-int4/blob/main/codeshell-chat-q4_0.gguf

加载模型

  • CodeShell-7B-Chat-int4模型使用llama_cpp_for_codeshell项目中的server命令即可提供API服务
./server -m ./models/codeshell-chat-q4_0.gguf --host 127.0.0.1 --port 8080

注意:对于编译时启用了 Metal 的情况下,若运行时出现异常,您也可以在命令行添加参数 -ngl 0 显式地禁用Metal GPU推理,从而使模型正常运行。

模型服务[NVIDIA GPU]

对于希望使用NVIDIA GPU进行推理的用户,可以使用text-generation-inference项目部署CodeShell大模型。以下为部署模型服务步骤:

下载模型

Hugging Face Hub将模型下载到本地后,将模型放置在 $HOME/models 文件夹的路径下,即可从本地加载模型。

git clone https://huggingface.co/WisdomShell/CodeShell-7B-Chat

部署模型

使用以下命令即可用text-generation-inference进行GPU加速推理部署:

docker run --gpus 'all' --shm-size 1g -p 9090:80 -v $HOME/models:/data \
        --env LOG_LEVEL="info,text_generation_router=debug" \
        ghcr.nju.edu.cn/huggingface/text-generation-inference:1.0.3 \
        --model-id /data/CodeShell-7B-Chat --num-shard 1 \
        --max-total-tokens 5000 --max-input-length 4096 \
        --max-stop-sequences 12 --trust-remote-code

更详细的参数说明请参考text-generation-inference项目文档

配置插件

  • 设置CodeShell大模型服务地址
  • 配置是否自动触发代码补全建议
  • 配置补全的最大tokens数量
  • 配置问答的最大tokens数量
  • 配置模型运行环境

注意:不同的模型运行环境可以在插件中进行配置。对于CodeShell-7B-Chat-int4模型,您可以在Model Runtime Environment选项中选择Use CPU Mode(with llama.cpp)选项。而对于CodeShell-7BCodeShell-7B-Chat模型,应选择Use GPU Model(with TGI framework)选项。

插件配置截图

功能特性

1. 代码补全

  • 自动触发代码建议

在编码时,当您停止输入时,代码建议将自动触发(可在插件设置中配置延迟时间为1到3秒)。

当插件提供代码建议时,建议内容以灰色显示在编辑器光标位置,您可以按下Tab键来接受该建议,或者继续输入以忽略该建议。

代码建议截图

2. 代码辅助

  • 对一段代码进行解释/优化/清理
  • 为一段代码生成注释/单元测试
  • 检查一段代码是否存在性能/安全性问题

在IDE侧边栏中打开插件问答界面,在编辑器中选中一段代码,在鼠标右键CodeShell菜单中选择对应的功能项,插件将在问答界面中给出相应的答复。

在问答界面的代码块中,可以点击复制按钮复制该代码块。

代码辅助截图

3. 智能问答

  • 支持多轮对话
  • 可编辑问题,重新提问
  • 对任一问题,可重新获取回答
  • 在回答过程中,可以打断

智能问答截图

开源协议

Apache 2.0

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