阿里云部署langchain+ChatGLM2-6B本地知识库实战加踩坑

1、白嫖阿里云的算力








创建实例，等待机器启动即可。

创建机器完成。

2、点击打开，进入到阿里云的idea界面，通过Terminal进入控制台。
3、重点部署内容
（1）更新git及相关内容

apt-get update
apt-get install git-lfs 
git init
git lfs install 

（2）下载相关包

git clone https://github.com/THUDM/ChatGLM2-6B.git
git clone https://www.modelscope.cn/ZhipuAI/chatglm2-6b.git
#git clone https://github.com/chatchat-space/Langchain-Chatchat.git 这两个是一样的不过下载的文件夹名字不一样
git clone https://github.com/imClumsyPanda/langchain-ChatGLM.git
cd langchain-ChatGLM
git clone https://www.modelscope.cn/xrunda/m3e-base.git
git clone https://www.modelscope.cn/thomas/text2vec-base-chinese.git

分别在/ChatGLM2-6B 和/langchain-ChatGLM目录下下载依赖

pip install –r requirements.txt

注意：
这里在按照依赖的时候会报错，不影响使用，再重新pip install –r requirements.txt即可
（3）重头戏相关
修改模型的相关内容（通过WebIDE进行修改） <1> chatglm2-6b chatglm2-6b模型在目录的config.json文件中修改"_name_or_path"

 "_name_or_path": "/mnt/workspace/chatglm2-6b",

<2> ChatGLM2-6B 在web_demo.py和web_demo2.py中都把tokenizer和model的路径修改为本地chatlm2-6b的路径

tokenizer = AutoTokenizer.from_pretrained("/mnt/workspace/chatglm2-6b", trust_remote_code=True)
model = AutoModel.from_pretrained("/mnt/workspace/chatglm2-6b", trust_remote_code=True).cuda()

<3> langchain-ChatGLM 修改configs目录下的文件后面的.example都去掉

• 修改model_config.py（完全替换内容即可）

import os

# 可以指定一个绝对路径，统一存放所有的Embedding和LLM模型。
# 每个模型可以是一个单独的目录，也可以是某个目录下的二级子目录。
# 如果模型目录名称和 MODEL_PATH 中的 key 或 value 相同，程序会自动检测加载，无需修改 MODEL_PATH 中的路径。
MODEL_ROOT_PATH = ""

# 选用的 Embedding 名称
# EMBEDDING_MODEL = "bge-large-zh-v1.5"

EMBEDDING_MODEL = "m3e-base"

# Embedding 模型运行设备。设为 "auto" 会自动检测(会有警告)，也可手动设定为 "cuda","mps","cpu","xpu" 其中之一。
EMBEDDING_DEVICE = "auto"

# 选用的reranker模型
RERANKER_MODEL = "bge-reranker-large"
# 是否启用reranker模型
USE_RERANKER = False
RERANKER_MAX_LENGTH = 1024

# 如果需要在 EMBEDDING_MODEL 中增加自定义的关键字时配置
EMBEDDING_KEYWORD_FILE = "keywords.txt"
EMBEDDING_MODEL_OUTPUT_PATH = "output"

# 要运行的 LLM 名称，可以包括本地模型和在线模型。列表中本地模型将在启动项目时全部加载。
# 列表中第一个模型将作为 API 和 WEBUI 的默认模型。
# 在这里，我们使用目前主流的两个离线模型，其中，chatglm3-6b 为默认加载模型。
# 如果你的显存不足，可使用 Qwen-1_8B-Chat, 该模型 FP16 仅需 3.8G显存。

# LLM_MODELS = ["chatglm3-6b", "zhipu-api", "openai-api"]
LLM_MODELS = ["chatglm2-6b"]

Agent_MODEL = None

# LLM 模型运行设备。设为"auto"会自动检测(会有警告)，也可手动设定为 "cuda","mps","cpu","xpu" 其中之一。
LLM_DEVICE = "auto"

HISTORY_LEN = 3

MAX_TOKENS = 2048

TEMPERATURE = 0.7

ONLINE_LLM_MODEL = {
#     "openai-api": {
#         "model_name": "gpt-4",
#         "api_base_url": "https://api.openai.com/v1",
#         "api_key": "",
#         "openai_proxy": "",
#     },

#     # 智谱AI API,具体注册及api key获取请前往 http://open.bigmodel.cn
#     "zhipu-api": {
#         "api_key": "",
#         "version": "glm-4",
#         "provider": "ChatGLMWorker",
#     },

#     # 具体注册及api key获取请前往 https://api.minimax.chat/
#     "minimax-api": {
#         "group_id": "",
#         "api_key": "",
#         "is_pro": False,
#         "provider": "MiniMaxWorker",
#     },

#     # 具体注册及api key获取请前往 https://xinghuo.xfyun.cn/
#     "xinghuo-api": {
#         "APPID": "",
#         "APISecret": "",
#         "api_key": "",
#         "version": "v3.5", # 你使用的讯飞星火大模型版本，可选包括 "v3.5","v3.0", "v2.0", "v1.5"
#         "provider": "XingHuoWorker",
#     },

#     # 百度千帆 API，申请方式请参考 https://cloud.baidu.com/doc/WENXINWORKSHOP/s/4lilb2lpf
#     "qianfan-api": {
#         "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。
#         "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
#         "api_key": "",
#         "secret_key": "",
#         "provider": "QianFanWorker",
#     },

#     # 火山方舟 API，文档参考 https://www.volcengine.com/docs/82379
#     "fangzhou-api": {
#         "version": "chatglm-6b-model",
#         "version_url": "",
#         "api_key": "",
#         "secret_key": "",
#         "provider": "FangZhouWorker",
#     },

#     # 阿里云通义千问 API，文档参考 https://help.aliyun.com/zh/dashscope/developer-reference/api-details
#     "qwen-api": {
#         "version": "qwen-max",
#         "api_key": "",
#         "provider": "QwenWorker",
#         "embed_model": "text-embedding-v1"  # embedding 模型名称
#     },

#     # 百川 API，申请方式请参考 https://www.baichuan-ai.com/home#api-enter
#     "baichuan-api": {
#         "version": "Baichuan2-53B",
#         "api_key": "",
#         "secret_key": "",
#         "provider": "BaiChuanWorker",
#     },

#     # Azure API
#     "azure-api": {
#         "deployment_name": "",  # 部署容器的名字
#         "resource_name": "",  # https://{resource_name}.openai.azure.com/openai/ 填写resource_name的部分，其他部分不要填写
#         "api_version": "",  # API的版本，不是模型版本
#         "api_key": "",
#         "provider": "AzureWorker",
#     },

#     # 昆仑万维天工 API https://model-platform.tiangong.cn/
#     "tiangong-api": {
#         "version": "SkyChat-MegaVerse",
#         "api_key": "",
#         "secret_key": "",
#         "provider": "TianGongWorker",
#     },

#     # Gemini API https://makersuite.google.com/app/apikey
#     "gemini-api": {
#         "api_key": "",
#         "provider": "GeminiWorker",
#     },

#     # Claude API : https://www.anthropic.com/api
#     # Available models: 
#     # Claude 3 Opus:    claude-3-opus-20240229 
#     # Claude 3 Sonnet	claude-3-sonnet-20240229
#     # Claude 3 Haiku	claude-3-haiku-20240307
#     "claude-api": {
#         "api_key": "",
#         "version": "2023-06-01",
#         "model_name":"claude-3-opus-20240229", 
#         "provider": "ClaudeWorker",
#     }

}

# 在以下字典中修改属性值，以指定本地embedding模型存储位置。支持3种设置方法：
# 1、将对应的值修改为模型绝对路径
# 2、不修改此处的值（以 text2vec 为例）：
#       2.1 如果{MODEL_ROOT_PATH}下存在如下任一子目录：
#           - text2vec
#           - GanymedeNil/text2vec-large-chinese
#           - text2vec-large-chinese
#       2.2 如果以上本地路径不存在，则使用huggingface模型

MODEL_PATH = {
    "embed_model": {
        # "ernie-tiny": "nghuyong/ernie-3.0-nano-zh",
        # "ernie-base": "nghuyong/ernie-3.0-base-zh",
        # "text2vec-base": "shibing624/text2vec-base-chinese",
        # "text2vec": "GanymedeNil/text2vec-large-chinese",
        # "text2vec-paraphrase": "shibing624/text2vec-base-chinese-paraphrase",
        # "text2vec-sentence": "shibing624/text2vec-base-chinese-sentence",
        # "text2vec-multilingual": "shibing624/text2vec-base-multilingual",
        # "text2vec-bge-large-chinese": "shibing624/text2vec-bge-large-chinese",
        # "m3e-small": "moka-ai/m3e-small",
        "m3e-base": "/mnt/workspace/langchain-ChatGLM/m3e-base",
        
        # "m3e-base": "moka-ai/m3e-base",
#         "m3e-large": "moka-ai/m3e-large",

#         "bge-small-zh": "BAAI/bge-small-zh",
#         "bge-base-zh": "BAAI/bge-base-zh",
#         "bge-large-zh": "BAAI/bge-large-zh",
#         "bge-large-zh-noinstruct": "BAAI/bge-large-zh-noinstruct",
#         "bge-base-zh-v1.5": "BAAI/bge-base-zh-v1.5",
#         "bge-large-zh-v1.5": "BAAI/bge-large-zh-v1.5",

#         "bge-m3": "BAAI/bge-m3",

#         "piccolo-base-zh": "sensenova/piccolo-base-zh",
#         "piccolo-large-zh": "sensenova/piccolo-large-zh",
#         "nlp_gte_sentence-embedding_chinese-large": "damo/nlp_gte_sentence-embedding_chinese-large",
        "text2vec-base-chinese": "/mnt/workspace/langchain-ChatGLM/text2vec-base-chinese",
        # "text-embedding-ada-002": "your OPENAI_API_KEY",
    },

    "llm_model": {
        "chatglm2-6b": "/mnt/workspace/chatglm2-6b",
#         "chatglm2-6b-32k": "THUDM/chatglm2-6b-32k",
#         "chatglm3-6b": "THUDM/chatglm3-6b",
#         "chatglm3-6b-32k": "THUDM/chatglm3-6b-32k",

#         "Orion-14B-Chat": "OrionStarAI/Orion-14B-Chat",
#         "Orion-14B-Chat-Plugin": "OrionStarAI/Orion-14B-Chat-Plugin",
#         "Orion-14B-LongChat": "OrionStarAI/Orion-14B-LongChat",

#         "Llama-2-7b-chat-hf": "meta-llama/Llama-2-7b-chat-hf",
#         "Llama-2-13b-chat-hf": "meta-llama/Llama-2-13b-chat-hf",
#         "Llama-2-70b-chat-hf": "meta-llama/Llama-2-70b-chat-hf",

#         "Qwen-1_8B-Chat": "Qwen/Qwen-1_8B-Chat",
#         "Qwen-7B-Chat": "Qwen/Qwen-7B-Chat",
#         "Qwen-14B-Chat": "Qwen/Qwen-14B-Chat",
#         "Qwen-72B-Chat": "Qwen/Qwen-72B-Chat",

#         # Qwen1.5 模型 VLLM可能出现问题
#         "Qwen1.5-0.5B-Chat": "Qwen/Qwen1.5-0.5B-Chat",
#         "Qwen1.5-1.8B-Chat": "Qwen/Qwen1.5-1.8B-Chat",
#         "Qwen1.5-4B-Chat": "Qwen/Qwen1.5-4B-Chat",
#         "Qwen1.5-7B-Chat": "Qwen/Qwen1.5-7B-Chat",
#         "Qwen1.5-14B-Chat": "Qwen/Qwen1.5-14B-Chat",
#         "Qwen1.5-72B-Chat": "Qwen/Qwen1.5-72B-Chat",

#         "baichuan-7b-chat": "baichuan-inc/Baichuan-7B-Chat",
#         "baichuan-13b-chat": "baichuan-inc/Baichuan-13B-Chat",
#         "baichuan2-7b-chat": "baichuan-inc/Baichuan2-7B-Chat",
#         "baichuan2-13b-chat": "baichuan-inc/Baichuan2-13B-Chat",

#         "internlm-7b": "internlm/internlm-7b",
#         "internlm-chat-7b": "internlm/internlm-chat-7b",
#         "internlm2-chat-7b": "internlm/internlm2-chat-7b",
#         "internlm2-chat-20b": "internlm/internlm2-chat-20b",

#         "BlueLM-7B-Chat": "vivo-ai/BlueLM-7B-Chat",
#         "BlueLM-7B-Chat-32k": "vivo-ai/BlueLM-7B-Chat-32k",

#         "Yi-34B-Chat": "https://huggingface.co/01-ai/Yi-34B-Chat",

#         "agentlm-7b": "THUDM/agentlm-7b",
#         "agentlm-13b": "THUDM/agentlm-13b",
#         "agentlm-70b": "THUDM/agentlm-70b",

#         "falcon-7b": "tiiuae/falcon-7b",
#         "falcon-40b": "tiiuae/falcon-40b",
#         "falcon-rw-7b": "tiiuae/falcon-rw-7b",

#         "aquila-7b": "BAAI/Aquila-7B",
#         "aquilachat-7b": "BAAI/AquilaChat-7B",
#         "open_llama_13b": "openlm-research/open_llama_13b",
#         "vicuna-13b-v1.5": "lmsys/vicuna-13b-v1.5",
#         "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",
#         "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",
    },

    "reranker": {
        "bge-reranker-large": "BAAI/bge-reranker-large",
        "bge-reranker-base": "BAAI/bge-reranker-base",
    }
}

# 通常情况下不需要更改以下内容

# nltk 模型存储路径
NLTK_DATA_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "nltk_data")

# 使用VLLM可能导致模型推理能力下降，无法完成Agent任务
VLLM_MODEL_DICT = {
    "chatglm2-6b": "/mnt/workspace/chatglm2-6b",
#     "chatglm2-6b-32k": "THUDM/chatglm2-6b-32k",
#     "chatglm3-6b": "THUDM/chatglm3-6b",
#     "chatglm3-6b-32k": "THUDM/chatglm3-6b-32k",

#     "Llama-2-7b-chat-hf": "meta-llama/Llama-2-7b-chat-hf",
#     "Llama-2-13b-chat-hf": "meta-llama/Llama-2-13b-chat-hf",
#     "Llama-2-70b-chat-hf": "meta-llama/Llama-2-70b-chat-hf",

#     "Qwen-1_8B-Chat": "Qwen/Qwen-1_8B-Chat",
#     "Qwen-7B-Chat": "Qwen/Qwen-7B-Chat",
#     "Qwen-14B-Chat": "Qwen/Qwen-14B-Chat",
#     "Qwen-72B-Chat": "Qwen/Qwen-72B-Chat",

#     "baichuan-7b-chat": "baichuan-inc/Baichuan-7B-Chat",
#     "baichuan-13b-chat": "baichuan-inc/Baichuan-13B-Chat",
#     "baichuan2-7b-chat": "baichuan-inc/Baichuan-7B-Chat",
#     "baichuan2-13b-chat": "baichuan-inc/Baichuan-13B-Chat",

#     "BlueLM-7B-Chat": "vivo-ai/BlueLM-7B-Chat",
#     "BlueLM-7B-Chat-32k": "vivo-ai/BlueLM-7B-Chat-32k",

#     "internlm-7b": "internlm/internlm-7b",
#     "internlm-chat-7b": "internlm/internlm-chat-7b",
#     "internlm2-chat-7b": "internlm/Models/internlm2-chat-7b",
#     "internlm2-chat-20b": "internlm/Models/internlm2-chat-20b",

#     "aquila-7b": "BAAI/Aquila-7B",
#     "aquilachat-7b": "BAAI/AquilaChat-7B",

#     "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",

}

SUPPORT_AGENT_MODEL = [
    # "openai-api",  # GPT4 模型
    # "qwen-api",  # Qwen Max模型
    # "zhipu-api",  # 智谱AI GLM4模型
    # "Qwen",  # 所有Qwen系列本地模型
    # "chatglm3-6b",
    "chatglm2-6b",
    # "internlm2-chat-20b",
    # "Orion-14B-Chat-Plugin",
]

这里主要替换的chatglm2-6B的一些内容（此次仅作model_config.py修改内容的解释）

# 00. 选用的 Embedding 名称为m3e-base
EMBEDDING_MODEL = "m3e-base"

# 01.仅指定 chatglm2-6b
LLM_MODELS = ["chatglm2-6b", ]

# 02.指定为 空
ONLINE_LLM_MODEL = { }

# 03.仅指定 text2vec-base-chinese chatglm2-6b
MODEL_PATH = {
"embed_model": {
        # 我们使用的embedding模型为：m3e-base
        "m3e-base":"/mnt/workspace/langchain-ChatGLM/m3e-base",
        "text2vec-base-chinese": "/mnt/workspace/langchain-ChatGLM/text2vec-base-chinese",
    },

    "llm_model": {
        # 仅指定 这一个
        "chatglm2-6b": "/mnt/workspace/chatglm2-6b",
    },
}


# 04.仅指定 chatglm2
SUPPORT_AGENT_MODEL = [
    "chatglm2",
]

• 修改server_config.py（完全替换内容即可）

import sys
from configs.model_config import LLM_DEVICE

# httpx 请求默认超时时间（秒）。如果加载模型或对话较慢，出现超时错误，可以适当加大该值。
HTTPX_DEFAULT_TIMEOUT = 300.0

# API 是否开启跨域，默认为False，如果需要开启，请设置为True
# is open cross domain
OPEN_CROSS_DOMAIN = False

# 各服务器默认绑定host。如改为"0.0.0.0"需要修改下方所有XX_SERVER的host
DEFAULT_BIND_HOST = "0.0.0.0" if sys.platform != "win32" else "127.0.0.1"

# webui.py server
WEBUI_SERVER = {
    "host": DEFAULT_BIND_HOST,
    "port": 8501,
}

# api.py server
API_SERVER = {
    "host": DEFAULT_BIND_HOST,
    "port": 7861,
}

# fastchat openai_api server
FSCHAT_OPENAI_API = {
    "host": DEFAULT_BIND_HOST,
    "port": 20000,
}

# fastchat model_worker server
# 这些模型必须是在model_config.MODEL_PATH或ONLINE_MODEL中正确配置的。
# 在启动startup.py时，可用通过`--model-name xxxx yyyy`指定模型，不指定则为LLM_MODELS
FSCHAT_MODEL_WORKERS = {
    # 所有模型共用的默认配置，可在模型专项配置中进行覆盖。
    "default": {
        "host": DEFAULT_BIND_HOST,
        "port": 20002,
        "device": LLM_DEVICE,
        # False,'vllm',使用的推理加速框架,使用vllm如果出现HuggingFace通信问题，参见doc/FAQ
        # vllm对一些模型支持还不成熟，暂时默认关闭
        "infer_turbo": False,

        # model_worker多卡加载需要配置的参数
        # "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
        # "num_gpus": 1, # 使用GPU的数量
        # "max_gpu_memory": "20GiB", # 每个GPU占用的最大显存

        # 以下为model_worker非常用参数，可根据需要配置
        # "load_8bit": False, # 开启8bit量化
        # "cpu_offloading": None,
        # "gptq_ckpt": None,
        # "gptq_wbits": 16,
        # "gptq_groupsize": -1,
        # "gptq_act_order": False,
        # "awq_ckpt": None,
        # "awq_wbits": 16,
        # "awq_groupsize": -1,
        # "model_names": LLM_MODELS,
        # "conv_template": None,
        # "limit_worker_concurrency": 5,
        # "stream_interval": 2,
        # "no_register": False,
        # "embed_in_truncate": False,

        # 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

        # tokenizer = model_path # 如果tokenizer与model_path不一致在此处添加
        # 'tokenizer_mode':'auto',
        # 'trust_remote_code':True,
        # 'download_dir':None,
        # 'load_format':'auto',
        # 'dtype':'auto',
        # 'seed':0,
        # 'worker_use_ray':False,
        # 'pipeline_parallel_size':1,
        # 'tensor_parallel_size':1,
        # 'block_size':16,
        # 'swap_space':4 , # GiB
        # 'gpu_memory_utilization':0.90,
        # 'max_num_batched_tokens':2560,
        # 'max_num_seqs':256,
        # 'disable_log_stats':False,
        # 'conv_template':None,
        # 'limit_worker_concurrency':5,
        # 'no_register':False,
        # 'num_gpus': 1
        # 'engine_use_ray': False,
        # 'disable_log_requests': False

    },
    "chatglm2-6b": {
        "device": "cuda",
    },
    "Qwen1.5-0.5B-Chat": {
        "device": "cuda",
    },
    # 以下配置可以不用修改，在model_config中设置启动的模型
    "zhipu-api": {
        "port": 21001,
    },
    "minimax-api": {
        "port": 21002,
    },
    "xinghuo-api": {
        "port": 21003,
    },
    "qianfan-api": {
        "port": 21004,
    },
    "fangzhou-api": {
        "port": 21005,
    },
    "qwen-api": {
        "port": 21006,
    },
    "baichuan-api": {
        "port": 21007,
    },
    "azure-api": {
        "port": 21008,
    },
    "tiangong-api": {
        "port": 21009,
    },
    "gemini-api": {
        "port": 21010,
    },
    "claude-api": {
        "port": 21011,
    },
}

FSCHAT_CONTROLLER = {
    "host": DEFAULT_BIND_HOST,
    "port": 20001,
    "dispatch_method": "shortest_queue",
}

<4> 安装依赖

pip install jq
pip install streamlit_modal

<5> 创建知识库

python init_database.py --recreate-vs

<6> 启动知识库

python startup.py -a

**注意：**此处可能会出现cuda和pytorch不匹配的问题（如下图），pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu113利用这个命令按照相匹配的内容即可解决。

**例外：**当然还有遇到的上传文档显示报403的错误
解决方法：降级streamlit

pip install streamlit==1.28.0

然后退出重启即可解决问题：python startup.py -a