mirror of
https://github.com/RYDE-WORK/Langchain-Chatchat.git
synced 2026-02-09 08:45:44 +08:00
删除本地fschat配置
This commit is contained in:
glide-the
liunux4odoo
parent
11bed7f0aa
commit
777b7c3499
@ -9,6 +9,10 @@ publish_server:
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host: "127.0.0.1"
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host: "127.0.0.1"
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port: 8001
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port: 8001
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subscribe_server:
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host: "127.0.0.1"
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port: 8002
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openai_plugins_folder:
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openai_plugins_folder:
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- "openai_plugins"
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- "openai_plugins"
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openai_plugins_load_folder:
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openai_plugins_load_folder:
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@ -20,323 +24,3 @@ plugins:
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name: "openai"
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name: "openai"
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- zhipuai:
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- zhipuai:
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name: "zhipuai"
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name: "zhipuai"
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- imitater:
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name: "imitater"
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logdir: "logs"
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worker_name: "qwen-worker1"
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run_openai_api:
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host: "127.0.0.1"
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port: 30000
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imitate_model_workers:
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- qwen-worker1:
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model:
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name: "Qwen-1_8B-Chat"
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chat_model_path: "Qwen-1_8B"
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chat_model_device: "0"
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chat_template_path: "openai_plugins/imitater/templates/qwen.jinja"
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generation_config_path: "openai_plugins/imitater/generation_config/qwen"
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agent_type: "react"
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embedding:
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name: "bge-large-zh"
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embed_model_path: "BAAI/bge-large-zh"
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embed_model_device: "0"
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embed_batch_size: 16
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- fastchat:
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name: "fastchat"
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logdir: "logs"
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# LLM 运行设备。设为"auto"会自动检测，也可手动设定为"cuda","mps","cpu"其中之一。
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llm_device: "auto"
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model_names:
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- "chatglm3-6b"
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run_controller:
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host: "127.0.0.1"
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port: 20001
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dispatch_method: "shortest_queue"
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run_openai_api:
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host: "127.0.0.1"
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port: 20000
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fschat_model_workers:
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- default:
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host: "127.0.0.1"
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port: 20002
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device: "auto"
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infer_turbo: False
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# model_worker多卡加载需要配置的参数
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# "gpus": None, # 使用的GPU，以str的格式指定，如"0,1"，如失效请使用CUDA_VISIBLE_DEVICES="0,1"等形式指定
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# "num_gpus": 1, # 使用GPU的数量
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# "max_gpu_memory": "20GiB", # 每个GPU占用的最大显存
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# 以下为model_worker非常用参数，可根据需要配置
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# "load_8bit": False, # 开启8bit量化
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# "cpu_offloading": None,
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# "gptq_ckpt": None,
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# "gptq_wbits": 16,
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# "gptq_groupsize": -1,
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# "gptq_act_order": False,
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# "awq_ckpt": None,
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# "awq_wbits": 16,
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# "awq_groupsize": -1,
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# "model_names": LLM_MODELS,
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# "conv_template": None,
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# "limit_worker_concurrency": 5,
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# "stream_interval": 2,
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# "no_register": False,
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# "embed_in_truncate": False,
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# 以下为vllm_worker配置参数,注意使用vllm必须有gpu，仅在Linux测试通过
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# tokenizer = model_path # 如果tokenizer与model_path不一致在此处添加
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# 'max_model_len': 1024
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# 'max_parallel_loading_workers': 1
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# 'max_context_len_to_capture': 1024
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# 'enforce_eager': False
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# 'tokenizer_mode': 'auto'
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# 'trust_remote_code': True
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# 'download_dir': None
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# 'load_format': 'auto'
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# 'dtype': 'auto'
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# 'seed': 0
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# 'worker_use_ray': False
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# 'pipeline_parallel_size': 1
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# 'tensor_parallel_size': 1
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# 'block_size': 16
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# 'swap_space': 4 # GiB
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# 'gpu_memory_utilization': 0.90
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# 'max_num_batched_tokens': 2560
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# 'max_num_seqs': 256
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# 'disable_log_stats': False
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# 'conv_template': 'qwen-7b-chat'
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# 'limit_worker_concurrency': 5
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# 'no_register': False
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# 'num_gpus': 1
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# 'engine_use_ray': False
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# 'disable_log_requests': False
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- chatglm3-6b:
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host: "127.0.0.1"
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device: "cuda"
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port: 20009
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- internlm2-chat-7b:
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host: "127.0.0.1"
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device: "cuda"
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port: 20009
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- Qwen-1_8B:
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host: "127.0.0.1"
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port: 20008
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# 以下配置可以不用修改，在model_config中设置启动的模型
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- zhipu-api:
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port: 21001
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- minimax-api:
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port: 21002
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- xinghuo-api:
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port: 21003
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- qianfan-api:
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port: 21004
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- fangzhou-api:
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port: 21005
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- qwen-api:
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port: 21006
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- baichuan-api:
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port: 21007
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- azure-api:
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port: 21008
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- tiangong-api:
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port: 21009
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online_llm_model:
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# 线上模型。请在server_config中为每个在线API设置不同的端口
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- "openai-api":
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"model_name": "gpt-3.5-turbo"
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"api_base_url": "https://api.openai.com/v1"
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"api_key": ""
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"openai_proxy": ""
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# 具体注册及api key获取请前往 http://open.bigmodel.cn
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- "zhipu-api":
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"api_key": ""
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"version": "chatglm_turbo" # 可选包括 "chatglm_turbo"
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"provider": "ChatGLMWorker"
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# 具体注册及api key获取请前往 https://api.minimax.chat/
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- "minimax-api":
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"group_id": ""
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"api_key": ""
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"is_pro": False
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"provider": "MiniMaxWorker"
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# 具体注册及api key获取请前往 https://xinghuo.xfyun.cn/
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- "xinghuo-api":
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"APPID": ""
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"APISecret": ""
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"api_key": ""
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"version": "v1.5" # 你使用的讯飞星火大模型版本，可选包括 "v3.0", "v1.5", "v2.0"
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"provider": "XingHuoWorker"
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# 百度千帆 API，申请方式请参考 https://cloud.baidu.com/doc/WENXINWORKSHOP/s/4lilb2lpf
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- "qianfan-api":
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"version": "ERNIE-Bot" # 注意大小写。当前支持 "ERNIE-Bot" 或 "ERNIE-Bot-turbo"， 更多的见官方文档。
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"version_url": "" # 也可以不填写version，直接填写在千帆申请模型发布的API地址
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"api_key": ""
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"secret_key": ""
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"provider": "QianFanWorker"
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# 火山方舟 API，文档参考 https://www.volcengine.com/docs/82379
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- "fangzhou-api":
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"version": "chatglm-6b-model" # 当前支持 "chatglm-6b-model"， 更多的见文档模型支持列表中方舟部分。
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"version_url": "" # 可以不填写version，直接填写在方舟申请模型发布的API地址
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"api_key": ""
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"secret_key": ""
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"provider": "FangZhouWorker"
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# 阿里云通义千问 API，文档参考 https://help.aliyun.com/zh/dashscope/developer-reference/api-details
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- "qwen-api":
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"version": "qwen-turbo" # 可选包括 "qwen-turbo", "qwen-plus"
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"api_key": "" # 请在阿里云控制台模型服务灵积API-KEY管理页面创建
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"provider": "QwenWorker"
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# 百川 API，申请方式请参考 https://www.baichuan-ai.com/home#api-enter
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- "baichuan-api":
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"version": "Baichuan2-53B" # 当前支持 "Baichuan2-53B"， 见官方文档。
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"api_key": ""
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"secret_key": ""
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"provider": "BaiChuanWorker"
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# Azure API
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- "azure-api":
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"deployment_name": "" # 部署容器的名字
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"resource_name": "" # https://{resource_name}.openai.azure.com/openai/ 填写resource_name的部分，其他部分不要填写
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"api_version": "" # API的版本，不是模型版本
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"api_key": ""
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"provider": "AzureWorker"
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# 昆仑万维天工 API https://model-platform.tiangong.cn/
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- "tiangong-api":
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"version": "SkyChat-MegaVerse"
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"api_key": ""
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"secret_key": ""
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"provider": "TianGongWorker"
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"llm_model":
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"chatglm2-6b": "THUDM/chatglm2-6b"
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"chatglm2-6b-32k": "THUDM/chatglm2-6b-32k"
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"chatglm3-6b": "THUDM/chatglm3-6b"
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"chatglm3-6b-32k": "THUDM/chatglm3-6b-32k"
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"Llama-2-7b-chat-hf": "meta-llama/Llama-2-7b-chat-hf"
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"Llama-2-13b-chat-hf": "meta-llama/Llama-2-13b-chat-hf"
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"Llama-2-70b-chat-hf": "meta-llama/Llama-2-70b-chat-hf"
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"Qwen-1_8B-Chat": "/media/checkpoint/Qwen-1_8B-Chat"
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"Qwen-7B-Chat": "Qwen/Qwen-7B-Chat"
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"Qwen-14B-Chat": "Qwen/Qwen-14B-Chat"
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"Qwen-72B-Chat": "Qwen/Qwen-72B-Chat"
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"baichuan-7b-chat": "baichuan-inc/Baichuan-7B-Chat"
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"baichuan-13b-chat": "baichuan-inc/Baichuan-13B-Chat"
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"baichuan2-7b-chat": "baichuan-inc/Baichuan2-7B-Chat"
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"baichuan2-13b-chat": "baichuan-inc/Baichuan2-13B-Chat"
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"internlm-7b": "internlm/internlm-7b"
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"internlm-chat-7b": "internlm/internlm-chat-7b"
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"internlm2-chat-7b": "internlm/internlm2-chat-7b"
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"internlm2-chat-20b": "internlm/internlm2-chat-20b"
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"BlueLM-7B-Chat": "vivo-ai/BlueLM-7B-Chat"
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"BlueLM-7B-Chat-32k": "vivo-ai/BlueLM-7B-Chat-32k"
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"Yi-34B-Chat": "https://huggingface.co/01-ai/Yi-34B-Chat"
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"agentlm-7b": "THUDM/agentlm-7b"
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"agentlm-13b": "THUDM/agentlm-13b"
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"agentlm-70b": "THUDM/agentlm-70b"
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"falcon-7b": "tiiuae/falcon-7b"
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"falcon-40b": "tiiuae/falcon-40b"
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"falcon-rw-7b": "tiiuae/falcon-rw-7b"
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"aquila-7b": "BAAI/Aquila-7B"
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"aquilachat-7b": "BAAI/AquilaChat-7B"
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"open_llama_13b": "openlm-research/open_llama_13b"
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"vicuna-13b-v1.5": "lmsys/vicuna-13b-v1.5"
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"koala": "young-geng/koala"
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"mpt-7b": "mosaicml/mpt-7b"
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"mpt-7b-storywriter": "mosaicml/mpt-7b-storywriter"
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"mpt-30b": "mosaicml/mpt-30b"
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"opt-66b": "facebook/opt-66b"
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"opt-iml-max-30b": "facebook/opt-iml-max-30b"
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"gpt2": "gpt2"
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"gpt2-xl": "gpt2-xl"
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"gpt-j-6b": "EleutherAI/gpt-j-6b"
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"gpt4all-j": "nomic-ai/gpt4all-j"
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"gpt-neox-20b": "EleutherAI/gpt-neox-20b"
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"pythia-12b": "EleutherAI/pythia-12b"
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"oasst-sft-4-pythia-12b-epoch-3.5": "OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5"
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"dolly-v2-12b": "databricks/dolly-v2-12b"
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"stablelm-tuned-alpha-7b": "stabilityai/stablelm-tuned-alpha-7b"
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vllm_model_dict:
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"chatglm2-6b": "THUDM/chatglm2-6b"
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"chatglm2-6b-32k": "THUDM/chatglm2-6b-32k"
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"chatglm3-6b": "THUDM/chatglm3-6b"
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"chatglm3-6b-32k": "THUDM/chatglm3-6b-32k"
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"Llama-2-7b-chat-hf": "meta-llama/Llama-2-7b-chat-hf"
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"Llama-2-13b-chat-hf": "meta-llama/Llama-2-13b-chat-hf"
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"Llama-2-70b-chat-hf": "meta-llama/Llama-2-70b-chat-hf"
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"Qwen-1_8B-Chat": "Qwen/Qwen-1_8B-Chat"
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"Qwen-7B-Chat": "Qwen/Qwen-7B-Chat"
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"Qwen-14B-Chat": "Qwen/Qwen-14B-Chat"
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"Qwen-72B-Chat": "Qwen/Qwen-72B-Chat"
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"baichuan-7b-chat": "baichuan-inc/Baichuan-7B-Chat"
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"baichuan-13b-chat": "baichuan-inc/Baichuan-13B-Chat"
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"baichuan2-7b-chat": "baichuan-inc/Baichuan-7B-Chat"
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"baichuan2-13b-chat": "baichuan-inc/Baichuan-13B-Chat"
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"BlueLM-7B-Chat": "vivo-ai/BlueLM-7B-Chat"
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"BlueLM-7B-Chat-32k": "vivo-ai/BlueLM-7B-Chat-32k"
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"internlm-7b": "internlm/internlm-7b"
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"internlm-chat-7b": "internlm/internlm-chat-7b"
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"internlm2-chat-7b": "internlm/Models/internlm2-chat-7b"
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"internlm2-chat-20b": "internlm/Models/internlm2-chat-20b"
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"aquila-7b": "BAAI/Aquila-7B"
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"aquilachat-7b": "BAAI/AquilaChat-7B"
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|
||||||
"falcon-7b": "tiiuae/falcon-7b"
|
|
||||||
"falcon-40b": "tiiuae/falcon-40b"
|
|
||||||
"falcon-rw-7b": "tiiuae/falcon-rw-7b"
|
|
||||||
"gpt2": "gpt2"
|
|
||||||
"gpt2-xl": "gpt2-xl"
|
|
||||||
"gpt-j-6b": "EleutherAI/gpt-j-6b"
|
|
||||||
"gpt4all-j": "nomic-ai/gpt4all-j"
|
|
||||||
"gpt-neox-20b": "EleutherAI/gpt-neox-20b"
|
|
||||||
"pythia-12b": "EleutherAI/pythia-12b"
|
|
||||||
"oasst-sft-4-pythia-12b-epoch-3.5": "OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5"
|
|
||||||
"dolly-v2-12b": "databricks/dolly-v2-12b"
|
|
||||||
"stablelm-tuned-alpha-7b": "stabilityai/stablelm-tuned-alpha-7b"
|
|
||||||
"open_llama_13b": "openlm-research/open_llama_13b"
|
|
||||||
"vicuna-13b-v1.3": "lmsys/vicuna-13b-v1.3"
|
|
||||||
"koala": "young-geng/koala"
|
|
||||||
"mpt-7b": "mosaicml/mpt-7b"
|
|
||||||
"mpt-7b-storywriter": "mosaicml/mpt-7b-storywriter"
|
|
||||||
"mpt-30b": "mosaicml/mpt-30b"
|
|
||||||
"opt-66b": "facebook/opt-66b"
|
|
||||||
"opt-iml-max-30b": "facebook/opt-iml-max-30b"
|
|
||||||
|
|
||||||
|
|||||||
@ -1,6 +1,6 @@
|
|||||||
{
|
{
|
||||||
"openai_plugins": [
|
"openai_plugins": [
|
||||||
"fastchat", "openai", "imitater", "zhipuai"
|
"imitater"
|
||||||
]
|
]
|
||||||
|
|
||||||
}
|
}
|
||||||
|
|||||||
@ -1,48 +0,0 @@
|
|||||||
from loom_core.openai_plugins.core.adapter import ProcessesInfo
|
|
||||||
from loom_core.openai_plugins.core.application import ApplicationAdapter
|
|
||||||
|
|
||||||
import os
|
|
||||||
import sys
|
|
||||||
import logging
|
|
||||||
|
|
||||||
logger = logging.getLogger(__name__)
|
|
||||||
root_dir = os.path.dirname(os.path.abspath(__file__))
|
|
||||||
sys.path.append(root_dir)
|
|
||||||
|
|
||||||
|
|
||||||
class OpenAIApplicationAdapter(ApplicationAdapter):
|
|
||||||
|
|
||||||
def __init__(self, cfg=None, state_dict: dict = None):
|
|
||||||
self.processesInfo = None
|
|
||||||
self._cfg = cfg
|
|
||||||
super().__init__(state_dict=state_dict)
|
|
||||||
|
|
||||||
def class_name(self) -> str:
|
|
||||||
"""Get class name."""
|
|
||||||
return self.__name__
|
|
||||||
|
|
||||||
@classmethod
|
|
||||||
def from_config(cls, cfg=None):
|
|
||||||
_state_dict = {
|
|
||||||
"application_name": "openai",
|
|
||||||
"application_version": "0.0.1",
|
|
||||||
"application_description": "openai application",
|
|
||||||
"application_author": "openai"
|
|
||||||
}
|
|
||||||
state_dict = cfg.get("state_dict", {})
|
|
||||||
if state_dict is not None and _state_dict is not None:
|
|
||||||
_state_dict = {**state_dict, **_state_dict}
|
|
||||||
else:
|
|
||||||
# 处理其中一个或两者都为 None 的情况
|
|
||||||
_state_dict = state_dict or _state_dict or {}
|
|
||||||
return cls(cfg=cfg, state_dict=_state_dict)
|
|
||||||
|
|
||||||
def init_processes(self, processesInfo: ProcessesInfo):
|
|
||||||
|
|
||||||
self.processesInfo = processesInfo
|
|
||||||
|
|
||||||
def start(self):
|
|
||||||
pass
|
|
||||||
|
|
||||||
def stop(self):
|
|
||||||
pass
|
|
||||||
@ -1,47 +0,0 @@
|
|||||||
from loom_core.openai_plugins.core.control import ControlAdapter
|
|
||||||
|
|
||||||
import os
|
|
||||||
import sys
|
|
||||||
import logging
|
|
||||||
|
|
||||||
logger = logging.getLogger(__name__)
|
|
||||||
# 为了能使用插件中的函数，需要将当前目录加入到sys.path中
|
|
||||||
root_dir = os.path.dirname(os.path.abspath(__file__))
|
|
||||||
sys.path.append(root_dir)
|
|
||||||
|
|
||||||
|
|
||||||
class OpenAIControlAdapter(ControlAdapter):
|
|
||||||
|
|
||||||
def __init__(self, cfg=None, state_dict: dict = None):
|
|
||||||
self._cfg = cfg
|
|
||||||
super().__init__(state_dict=state_dict)
|
|
||||||
|
|
||||||
def class_name(self) -> str:
|
|
||||||
"""Get class name."""
|
|
||||||
return self.__name__
|
|
||||||
|
|
||||||
def start_model(self, new_model_name):
|
|
||||||
pass
|
|
||||||
|
|
||||||
def stop_model(self, model_name: str):
|
|
||||||
pass
|
|
||||||
|
|
||||||
def replace_model(self, model_name: str, new_model_name: str):
|
|
||||||
pass
|
|
||||||
|
|
||||||
@classmethod
|
|
||||||
def from_config(cls, cfg=None):
|
|
||||||
_state_dict = {
|
|
||||||
"controller_name": "openai",
|
|
||||||
"controller_version": "0.0.1",
|
|
||||||
"controller_description": "openai controller",
|
|
||||||
"controller_author": "openai"
|
|
||||||
}
|
|
||||||
state_dict = cfg.get("state_dict", {})
|
|
||||||
if state_dict is not None and _state_dict is not None:
|
|
||||||
_state_dict = {**state_dict, **_state_dict}
|
|
||||||
else:
|
|
||||||
# 处理其中一个或两者都为 None 的情况
|
|
||||||
_state_dict = state_dict or _state_dict or {}
|
|
||||||
|
|
||||||
return cls(cfg=cfg, state_dict=_state_dict)
|
|
||||||
@ -1,104 +0,0 @@
|
|||||||
import logging
|
|
||||||
import sys
|
|
||||||
import os
|
|
||||||
import subprocess
|
|
||||||
import threading
|
|
||||||
import re
|
|
||||||
import locale
|
|
||||||
|
|
||||||
logger = logging.getLogger(__name__)
|
|
||||||
# 为了能使用插件中的函数，需要将当前目录加入到sys.path中
|
|
||||||
root_dir = os.path.dirname(os.path.abspath(__file__))
|
|
||||||
sys.path.append(root_dir)
|
|
||||||
|
|
||||||
# Perform install
|
|
||||||
processed_install = set()
|
|
||||||
pip_list = None
|
|
||||||
|
|
||||||
|
|
||||||
def handle_stream(stream, prefix):
|
|
||||||
stream.reconfigure(encoding=locale.getpreferredencoding(), errors='replace')
|
|
||||||
for msg in stream:
|
|
||||||
if prefix == '[!]' and ('it/s]' in msg or 's/it]' in msg) and ('%|' in msg or 'it [' in msg):
|
|
||||||
if msg.startswith('100%'):
|
|
||||||
print('\r' + msg, end="", file=sys.stderr),
|
|
||||||
else:
|
|
||||||
print('\r' + msg[:-1], end="", file=sys.stderr),
|
|
||||||
else:
|
|
||||||
if prefix == '[!]':
|
|
||||||
print(prefix, msg, end="", file=sys.stderr)
|
|
||||||
else:
|
|
||||||
print(prefix, msg, end="")
|
|
||||||
|
|
||||||
|
|
||||||
def get_installed_packages():
|
|
||||||
global pip_list
|
|
||||||
|
|
||||||
if pip_list is None:
|
|
||||||
try:
|
|
||||||
result = subprocess.check_output([sys.executable, '-m', 'pip', 'list'], universal_newlines=True)
|
|
||||||
pip_list = set([line.split()[0].lower() for line in result.split('\n') if line.strip()])
|
|
||||||
except subprocess.CalledProcessError as e:
|
|
||||||
print(f"[ComfyUI-Manager] Failed to retrieve the information of installed pip packages.")
|
|
||||||
return set()
|
|
||||||
|
|
||||||
return pip_list
|
|
||||||
|
|
||||||
|
|
||||||
def is_installed(name):
|
|
||||||
name = name.strip()
|
|
||||||
|
|
||||||
if name.startswith('#'):
|
|
||||||
return True
|
|
||||||
|
|
||||||
pattern = r'([^<>!=]+)([<>!=]=?)'
|
|
||||||
match = re.search(pattern, name)
|
|
||||||
|
|
||||||
if match:
|
|
||||||
name = match.group(1)
|
|
||||||
|
|
||||||
return name.lower() in get_installed_packages()
|
|
||||||
|
|
||||||
|
|
||||||
def process_wrap(cmd_str, cwd_path, handler=None):
|
|
||||||
process = subprocess.Popen(cmd_str, cwd=cwd_path, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True,
|
|
||||||
bufsize=1)
|
|
||||||
|
|
||||||
if handler is None:
|
|
||||||
handler = handle_stream
|
|
||||||
|
|
||||||
stdout_thread = threading.Thread(target=handler, args=(process.stdout, ""))
|
|
||||||
stderr_thread = threading.Thread(target=handler, args=(process.stderr, "[!]"))
|
|
||||||
|
|
||||||
stdout_thread.start()
|
|
||||||
stderr_thread.start()
|
|
||||||
|
|
||||||
stdout_thread.join()
|
|
||||||
stderr_thread.join()
|
|
||||||
|
|
||||||
return process.wait()
|
|
||||||
|
|
||||||
|
|
||||||
def install():
|
|
||||||
try:
|
|
||||||
requirements_path = os.path.join(root_dir, 'requirements.txt')
|
|
||||||
|
|
||||||
this_exit_code = 0
|
|
||||||
|
|
||||||
if os.path.exists(requirements_path):
|
|
||||||
with open(requirements_path, 'r', encoding="UTF-8") as file:
|
|
||||||
for line in file:
|
|
||||||
package_name = line.strip()
|
|
||||||
if package_name and not is_installed(package_name):
|
|
||||||
install_cmd = [sys.executable, "-m", "pip", "install", package_name]
|
|
||||||
this_exit_code += process_wrap(install_cmd, root_dir)
|
|
||||||
|
|
||||||
if this_exit_code != 0:
|
|
||||||
logger.info(f"[openai_plugins] Restoring fastchat is failed.")
|
|
||||||
|
|
||||||
except Exception as e:
|
|
||||||
logger.error(f"[openai_plugins] Restoring fastchat is failed.", exc_info=True)
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
install()
|
|
||||||
@ -1,11 +0,0 @@
|
|||||||
{
|
|
||||||
"plugins_name": "openai",
|
|
||||||
"endpoint_host": "https://api.openai.com/v1",
|
|
||||||
"install_file": "install.py",
|
|
||||||
"application_file": "app.py",
|
|
||||||
"application_class": "OpenAIApplicationAdapter",
|
|
||||||
"endpoint_controller_file": "controller.py",
|
|
||||||
"endpoint_controller_class": "OpenAIControlAdapter",
|
|
||||||
"profile_endpoint_file": "profile_endpoint.py",
|
|
||||||
"profile_endpoint_class": "OpenAIProfileEndpointAdapter"
|
|
||||||
}
|
|
||||||
@ -1,107 +0,0 @@
|
|||||||
import json
|
|
||||||
from typing import List
|
|
||||||
|
|
||||||
from loom_core.openai_plugins.core.adapter import LLMWorkerInfo
|
|
||||||
from loom_core.openai_plugins.core.profile_endpoint.core import ProfileEndpointAdapter
|
|
||||||
|
|
||||||
import os
|
|
||||||
import sys
|
|
||||||
import logging
|
|
||||||
|
|
||||||
logger = logging.getLogger(__name__)
|
|
||||||
# 为了能使用插件中的函数，需要将当前目录加入到sys.path中
|
|
||||||
root_dir = os.path.dirname(os.path.abspath(__file__))
|
|
||||||
sys.path.append(root_dir)
|
|
||||||
|
|
||||||
|
|
||||||
class OpenAIProfileEndpointAdapter(ProfileEndpointAdapter):
|
|
||||||
"""Adapter for the profile endpoint."""
|
|
||||||
|
|
||||||
def __init__(self, cfg=None, state_dict: dict = None):
|
|
||||||
self._cfg = cfg
|
|
||||||
super().__init__(state_dict=state_dict)
|
|
||||||
|
|
||||||
def class_name(self) -> str:
|
|
||||||
"""Get class name."""
|
|
||||||
return self.__name__
|
|
||||||
|
|
||||||
def list_running_models(self) -> List[LLMWorkerInfo]:
|
|
||||||
"""模型列表及其配置项"""
|
|
||||||
list_worker = []
|
|
||||||
list_worker.append(LLMWorkerInfo(worker_id="gpt-3.5-turbo",
|
|
||||||
model_name="gpt-3.5-turbo",
|
|
||||||
model_description="gpt-3.5-turbo",
|
|
||||||
providers=["model", "embedding"],
|
|
||||||
model_extra_info="{}"))
|
|
||||||
list_worker.append(LLMWorkerInfo(worker_id="gpt-4",
|
|
||||||
model_name="gpt-4",
|
|
||||||
model_description="gpt-4",
|
|
||||||
providers=["model", "embedding"],
|
|
||||||
model_extra_info="{}"))
|
|
||||||
list_worker.append(LLMWorkerInfo(worker_id="gpt-4-1106-preview",
|
|
||||||
model_name="gpt-4-1106-preview",
|
|
||||||
model_description="gpt-4-1106-preview",
|
|
||||||
providers=["model", "embedding"],
|
|
||||||
model_extra_info="{}"))
|
|
||||||
# list_worker.append(LLMWorkerInfo(worker_id="text-embedding-ada-002",
|
|
||||||
# model_name="text-embedding-ada-002",
|
|
||||||
# model_description="text-embedding-ada-002",
|
|
||||||
# providers=["embedding"],
|
|
||||||
# model_extra_info="{}"))
|
|
||||||
return list_worker
|
|
||||||
|
|
||||||
def list_llm_models(self) -> List[LLMWorkerInfo]:
|
|
||||||
"""获取已配置模型列表"""
|
|
||||||
list_worker = []
|
|
||||||
list_worker.append(LLMWorkerInfo(worker_id="gpt-3.5-turbo",
|
|
||||||
model_name="gpt-3.5-turbo",
|
|
||||||
model_description="gpt-3.5-turbo",
|
|
||||||
providers=["model", "embedding"],
|
|
||||||
model_extra_info="{}"))
|
|
||||||
list_worker.append(LLMWorkerInfo(worker_id="gpt-4",
|
|
||||||
model_name="gpt-4",
|
|
||||||
model_description="gpt-4",
|
|
||||||
providers=["model", "embedding"],
|
|
||||||
model_extra_info="{}"))
|
|
||||||
list_worker.append(LLMWorkerInfo(worker_id="gpt-4-1106-preview",
|
|
||||||
model_name="gpt-4-1106-preview",
|
|
||||||
model_description="gpt-4-1106-preview",
|
|
||||||
providers=["model", "embedding"],
|
|
||||||
model_extra_info="{}"))
|
|
||||||
# list_worker.append(LLMWorkerInfo(worker_id="text-embedding-ada-002",
|
|
||||||
# model_name="text-embedding-ada-002",
|
|
||||||
# model_description="text-embedding-ada-002",
|
|
||||||
# providers=["embedding"],
|
|
||||||
# model_extra_info="{}"))
|
|
||||||
return list_worker
|
|
||||||
|
|
||||||
def get_model_config(self, model_name) -> LLMWorkerInfo:
|
|
||||||
|
|
||||||
'''
|
|
||||||
获取LLM模型配置项（合并后的）
|
|
||||||
'''
|
|
||||||
|
|
||||||
info_obj = LLMWorkerInfo(worker_id=model_name,
|
|
||||||
model_name=model_name,
|
|
||||||
model_description="",
|
|
||||||
providers=["model", "embedding"],
|
|
||||||
model_extra_info="{}")
|
|
||||||
|
|
||||||
return info_obj
|
|
||||||
|
|
||||||
@classmethod
|
|
||||||
def from_config(cls, cfg=None):
|
|
||||||
_state_dict = {
|
|
||||||
"profile_name": "openai",
|
|
||||||
"profile_version": "0.0.1",
|
|
||||||
"profile_description": "openai profile endpoint",
|
|
||||||
"profile_author": "openai"
|
|
||||||
}
|
|
||||||
state_dict = cfg.get("state_dict", {})
|
|
||||||
if state_dict is not None and _state_dict is not None:
|
|
||||||
_state_dict = {**state_dict, **_state_dict}
|
|
||||||
else:
|
|
||||||
# 处理其中一个或两者都为 None 的情况
|
|
||||||
_state_dict = state_dict or _state_dict or {}
|
|
||||||
|
|
||||||
return cls(cfg=cfg, state_dict=_state_dict)
|
|
||||||
@ -1 +0,0 @@
|
|||||||
openai>=1.7.1
|
|
||||||
Loading…
x
Reference in New Issue
Block a user