## =================================================== # docker-compose.yml ## =================================================== # 1. 请在以下方案中选择任意一种，然后删除其他的方案 # 2. 修改你选择的方案中的environment环境变量，详情请见github wiki或者config.py # 3. 选择一种暴露服务端口的方法，并对相应的配置做出修改： # 【方法1: 适用于Linux，很方便，可惜windows不支持】与宿主的网络融合为一体，这个是默认配置 # network_mode: "host" # 【方法2: 适用于所有系统包括Windows和MacOS】端口映射，把容器的端口映射到宿主的端口（注意您需要先删除network_mode: "host"，再追加以下内容） # ports: # - "12345:12345" # 注意！12345必须与WEB_PORT环境变量相互对应 # 4. 最后`docker-compose up`运行 # 5. 如果希望使用显卡，请关注 LOCAL_MODEL_DEVICE 和 英伟达显卡运行时 选项 ## =================================================== # 1. Please choose one of the following options and delete the others. # 2. Modify the environment variables in the selected option, see GitHub wiki or config.py for more details. # 3. Choose a method to expose the server port and make the corresponding configuration changes: # [Method 1: Suitable for Linux, convenient, but not supported for Windows] Fusion with the host network, this is the default configuration # network_mode: "host" # [Method 2: Suitable for all systems including Windows and MacOS] Port mapping, mapping the container port to the host port (note that you need to delete network_mode: "host" first, and then add the following content) # ports: # - "12345: 12345" # Note! 12345 must correspond to the WEB_PORT environment variable. # 4. Finally, run `docker-compose up`. # 5. If you want to use a graphics card, pay attention to the LOCAL_MODEL_DEVICE and Nvidia GPU runtime options. ## =================================================== ## =================================================== ## 【方案零】 部署项目的全部能力（这个是包含cuda和latex的大型镜像。如果您网速慢、硬盘小或没有显卡，则不推荐使用这个） ## =================================================== version: '3' services: gpt_academic_full_capability: image: ghcr.io/binary-husky/gpt_academic_with_all_capacity:master environment: # 请查阅 `config.py`或者 github wiki 以查看所有的配置信息 API_KEY: ' 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 ' # USE_PROXY: ' True ' # proxies: ' { "http": "http://localhost:10881", "https": "http://localhost:10881", } ' LLM_MODEL: ' gpt-3.5-turbo ' AVAIL_LLM_MODELS: ' ["gpt-3.5-turbo", "gpt-4", "qianfan", "sparkv2", "spark", "chatglm"] ' BAIDU_CLOUD_API_KEY : ' bTUtwEAveBrQipEowUvDwYWq ' BAIDU_CLOUD_SECRET_KEY : ' jqXtLvXiVw6UNdjliATTS61rllG8Iuni ' XFYUN_APPID: ' 53a8d816 ' XFYUN_API_SECRET: ' MjMxNDQ4NDE4MzM0OSNlNjQ2NTlhMTkx ' XFYUN_API_KEY: ' 95ccdec285364869d17b33e75ee96447 ' ENABLE_AUDIO: ' False ' DEFAULT_WORKER_NUM: ' 20 ' WEB_PORT: ' 12345 ' ADD_WAIFU: ' False ' ALIYUN_APPKEY: ' RxPlZrM88DnAFkZK ' THEME: ' Chuanhu-Small-and-Beautiful ' ALIYUN_ACCESSKEY: ' LTAI5t6BrFUzxRXVGUWnekh1 ' ALIYUN_SECRET: ' eHmI20SVWIwQZxCiTD2bGQVspP9i68 ' # LOCAL_MODEL_DEVICE: ' cuda ' # 加载英伟达显卡运行时 # runtime: nvidia # deploy: # resources: # reservations: # devices: # - driver: nvidia # count: 1 # capabilities: [gpu] # 【WEB_PORT暴露方法1: 适用于Linux】与宿主的网络融合 network_mode: "host" # 【WEB_PORT暴露方法2: 适用于所有系统】端口映射 # ports: # - "12345:12345" # 12345必须与WEB_PORT相互对应 # 启动容器后，运行main.py主程序 command: > bash -c "python3 -u main.py" ## =================================================== ## 【方案一】 如果不需要运行本地模型（仅 chatgpt, azure, 星火, 千帆, claude 等在线大模型服务） ## =================================================== version: '3' services: gpt_academic_nolocalllms: image: ghcr.io/binary-husky/gpt_academic_nolocal:master # (Auto Built by Dockerfile: docs/GithubAction+NoLocal) environment: # 请查阅 `config.py` 以查看所有的配置信息 API_KEY: ' sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx ' USE_PROXY: ' True ' proxies: ' { "http": "socks5h://localhost:10880", "https": "socks5h://localhost:10880", } ' LLM_MODEL: ' gpt-3.5-turbo ' AVAIL_LLM_MODELS: ' ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "sparkv2", "qianfan"] ' WEB_PORT: ' 22303 ' ADD_WAIFU: ' True ' # THEME: ' Chuanhu-Small-and-Beautiful ' # DEFAULT_WORKER_NUM: ' 10 ' # AUTHENTICATION: ' [("username", "passwd"), ("username2", "passwd2")] ' # 与宿主的网络融合 network_mode: "host" # 不使用代理网络拉取最新代码 command: > bash -c "python3 -u main.py" ### =================================================== ### 【方案二】 如果需要运行ChatGLM + Qwen + MOSS等本地模型 ### =================================================== version: '3' services: gpt_academic_with_chatglm: image: ghcr.io/binary-husky/gpt_academic_chatglm_moss:master # (Auto Built by Dockerfile: docs/Dockerfile+ChatGLM) environment: # 请查阅 `config.py` 以查看所有的配置信息 API_KEY: ' sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx ' USE_PROXY: ' True ' proxies: ' { "http": "socks5h://localhost:10880", "https": "socks5h://localhost:10880", } ' LLM_MODEL: ' gpt-3.5-turbo ' AVAIL_LLM_MODELS: ' ["chatglm", "qwen", "moss", "gpt-3.5-turbo", "gpt-4", "newbing"] ' LOCAL_MODEL_DEVICE: ' cuda ' DEFAULT_WORKER_NUM: ' 10 ' WEB_PORT: ' 12303 ' ADD_WAIFU: ' True ' # AUTHENTICATION: ' [("username", "passwd"), ("username2", "passwd2")] ' # 显卡的使用，nvidia0指第0个GPU runtime: nvidia devices: - /dev/nvidia0:/dev/nvidia0 # 与宿主的网络融合 network_mode: "host" command: > bash -c "python3 -u main.py" # P.S. 通过对 command 进行微调，可以便捷地安装额外的依赖 # command: > # bash -c "pip install -r request_llms/requirements_qwen.txt && python3 -u main.py" ### =================================================== ### 【方案三】 如果需要运行ChatGPT + LLAMA + 盘古 + RWKV本地模型 ### =================================================== version: '3' services: gpt_academic_with_rwkv: image: ghcr.io/binary-husky/gpt_academic_jittorllms:master environment: # 请查阅 `config.py` 以查看所有的配置信息 API_KEY: ' sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx,fkxxxxxx-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx ' USE_PROXY: ' True ' proxies: ' { "http": "socks5h://localhost:10880", "https": "socks5h://localhost:10880", } ' LLM_MODEL: ' gpt-3.5-turbo ' AVAIL_LLM_MODELS: ' ["gpt-3.5-turbo", "newbing", "jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"] ' LOCAL_MODEL_DEVICE: ' cuda ' DEFAULT_WORKER_NUM: ' 10 ' WEB_PORT: ' 12305 ' ADD_WAIFU: ' True ' # AUTHENTICATION: ' [("username", "passwd"), ("username2", "passwd2")] ' # 显卡的使用，nvidia0指第0个GPU runtime: nvidia devices: - /dev/nvidia0:/dev/nvidia0 # 与宿主的网络融合 network_mode: "host" # 不使用代理网络拉取最新代码 command: > python3 -u main.py ## =================================================== ## 【方案四】 ChatGPT + Latex ## =================================================== version: '3' services: gpt_academic_with_latex: image: ghcr.io/binary-husky/gpt_academic_with_latex:master # (Auto Built by Dockerfile: docs/GithubAction+NoLocal+Latex) environment: # 请查阅 `config.py` 以查看所有的配置信息 API_KEY: ' sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx ' USE_PROXY: ' True ' proxies: ' { "http": "socks5h://localhost:10880", "https": "socks5h://localhost:10880", } ' LLM_MODEL: ' gpt-3.5-turbo ' AVAIL_LLM_MODELS: ' ["gpt-3.5-turbo", "gpt-4"] ' LOCAL_MODEL_DEVICE: ' cuda ' DEFAULT_WORKER_NUM: ' 10 ' WEB_PORT: ' 12303 ' # 与宿主的网络融合 network_mode: "host" # 不使用代理网络拉取最新代码 command: > bash -c "python3 -u main.py" ## =================================================== ## 【方案五】 ChatGPT + 语音助手 （请先阅读 docs/use_audio.md） ## =================================================== version: '3' services: gpt_academic_with_audio: image: ghcr.io/binary-husky/gpt_academic_audio_assistant:master environment: # 请查阅 `config.py` 以查看所有的配置信息 API_KEY: ' fk195831-IdP0Pb3W6DCMUIbQwVX6MsSiyxwqybyS ' USE_PROXY: ' False ' proxies: ' None ' LLM_MODEL: ' gpt-3.5-turbo ' AVAIL_LLM_MODELS: ' ["gpt-3.5-turbo", "gpt-4"] ' ENABLE_AUDIO: ' True ' LOCAL_MODEL_DEVICE: ' cuda ' DEFAULT_WORKER_NUM: ' 20 ' WEB_PORT: ' 12343 ' ADD_WAIFU: ' True ' THEME: ' Chuanhu-Small-and-Beautiful ' ALIYUN_APPKEY: ' RoP1ZrM84DnAFkZK ' ALIYUN_TOKEN: ' f37f30e0f9934c34a992f6f64f7eba4f ' # (无需填写) ALIYUN_ACCESSKEY: ' LTAI5q6BrFUzoRXVGUWnekh1 ' # (无需填写) ALIYUN_SECRET: ' eHmI20AVWIaQZ0CiTD2bGQVsaP9i68 ' # 与宿主的网络融合 network_mode: "host" # 不使用代理网络拉取最新代码 command: > bash -c "python3 -u main.py"