148 lines
3.7 KiB
Plaintext
148 lines
3.7 KiB
Plaintext
from .model_config import LLM_MODEL, LLM_DEVICE
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import os
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# API 是否开启跨域，默认为False，如果需要开启，请设置为True
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# is open cross domain
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OPEN_CROSS_DOMAIN = False
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# 是否用容器来启动服务
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DOCKER_SERVICE = True
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# 是否采用容器沙箱
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SANDBOX_DO_REMOTE = True
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# 是否采用api服务来进行
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NO_REMOTE_API = True
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# 各服务器默认绑定host
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DEFAULT_BIND_HOST = "127.0.0.1"
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#
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CONTRAINER_NAME = "devopsgpt_webui"
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IMAGE_NAME = "devopsgpt:py39"
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# webui.py server
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WEBUI_SERVER = {
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"host": DEFAULT_BIND_HOST,
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"port": 8501,
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"docker_port": 8501
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}
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# api.py server
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API_SERVER = {
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"host": DEFAULT_BIND_HOST,
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"port": 7861,
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"docker_port": 7861
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}
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# sdfile_api.py server
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SDFILE_API_SERVER = {
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"host": DEFAULT_BIND_HOST,
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"port": 7862,
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"docker_port": 7862
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}
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# fastchat openai_api server
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FSCHAT_OPENAI_API = {
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"host": DEFAULT_BIND_HOST,
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"port": 8888, # model_config.llm_model_dict中模型配置的api_base_url需要与这里一致。
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"docker_port": 8888, # model_config.llm_model_dict中模型配置的api_base_url需要与这里一致。
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}
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# nebula conf
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NEBULA_HOST = DEFAULT_BIND_HOST
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NEBULA_PORT = 9669
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NEBULA_STORAGED_PORT = 9779
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NEBULA_USER = 'root'
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NEBULA_PASSWORD = ''
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NEBULA_GRAPH_SERVER = {
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"host": DEFAULT_BIND_HOST,
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"port": NEBULA_PORT,
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"docker_port": NEBULA_PORT
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}
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# chroma conf
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CHROMA_PERSISTENT_PATH = '/home/user/chatbot/data/chroma_data'
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# sandbox api server
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SANDBOX_CONTRAINER_NAME = "devopsgpt_sandbox"
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SANDBOX_IMAGE_NAME = "devopsgpt:py39"
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SANDBOX_HOST = os.environ.get("SANDBOX_HOST") or DEFAULT_BIND_HOST # "172.25.0.3"
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SANDBOX_SERVER = {
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"host": f"http://{SANDBOX_HOST}",
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"port": 5050,
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"docker_port": 5050,
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"url": f"http://{SANDBOX_HOST}:5050",
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"do_remote": SANDBOX_DO_REMOTE,
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}
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# fastchat model_worker server
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# 这些模型必须是在model_config.llm_model_dict中正确配置的。
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# 在启动startup.py时，可用通过`--model-worker --model-name xxxx`指定模型，不指定则为LLM_MODEL
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FSCHAT_MODEL_WORKERS = {
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LLM_MODEL: {
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"host": DEFAULT_BIND_HOST,
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"port": 20002,
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"device": LLM_DEVICE,
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# todo: 多卡加载需要配置的参数
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"gpus": None,
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"numgpus": 1,
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# 以下为非常用参数，可根据需要配置
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# "max_gpu_memory": "20GiB",
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# "load_8bit": False,
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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_MODEL],
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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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},
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}
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# fastchat multi model worker server
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FSCHAT_MULTI_MODEL_WORKERS = {
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# todo
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}
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# fastchat controller server
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FSCHAT_CONTROLLER = {
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"host": DEFAULT_BIND_HOST,
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"port": 20001,
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"dispatch_method": "shortest_queue",
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}
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# 以下不要更改
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def fschat_controller_address() -> str:
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host = FSCHAT_CONTROLLER["host"]
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port = FSCHAT_CONTROLLER["port"]
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return f"http://{host}:{port}"
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def fschat_model_worker_address(model_name: str = LLM_MODEL) -> str:
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if model := FSCHAT_MODEL_WORKERS.get(model_name):
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host = model["host"]
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port = model["port"]
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return f"http://{host}:{port}"
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def fschat_openai_api_address() -> str:
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host = FSCHAT_OPENAI_API["host"]
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port = FSCHAT_OPENAI_API["port"]
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return f"http://{host}:{port}"
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def api_address() -> str:
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host = API_SERVER["host"]
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port = API_SERVER["port"]
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return f"http://{host}:{port}"
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def webui_address() -> str:
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host = WEBUI_SERVER["host"]
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port = WEBUI_SERVER["port"]
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return f"http://{host}:{port}"
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