A while ago, I shared how to connect a large model toWeChatThe robot tutorial is based on the chatgpt-on-wechat project, which uses the models of LinkAI and Zhipu AI. In subsequent tests, some problems were found, such as problems with the call of Zhipu AI knowledge base, and sometimes the hit rate was very low.

So I stopped using the robot and looked for new solutions until I found Coze (an AI robot and agent creation platform launched by ByteDance). It turned out that Coze was very easy to use and also fit my usage scenario.

Therefore, I wrote this article to record the deployment process of building my own WeChat group chat AI robot from scratch, including server purchase and configuration, project deployment, access to coze, modification of coze configuration, access to the knowledge base, plug-in installation, etc.

I originally planned to divide this article into two parts, but in the end I decided to complete the entire article directly. Although it is long, the operation is relatively simple and there is basically no problem following the instructions.

This article is generally divided into two parts:

• Deployment of chatgpt-on-wechat project
• CozeRobot configuration

There are two ways to deploy the chatgpt-on-wechat project:

1. Source code deployment.
2. I have created a docker image that can be deployed with one click.

Get the Docker image:

Reply to the public account (note capitalization)ChatOnWeChat

What is Coze?

Coze is a next-generation AI application and chatbot development platform for everyone. It allows users to easily create a variety of chatbots, regardless of whether the user has programming experience. Users can deploy and publish their own chatbots to different social platforms, such as Doubao, Feishu, WeChat, etc. Coze provides a wealth of features, including roles and prompts, plug-ins, knowledge base, opening dialogues, preview and debugging, workflows, etc. Users can also create their own plug-ins. In addition, Coze has a Bot Store that displays open source robots with various functions for users to browse and learn.

Register coze

Enter the official website

Come to coze official website

• China: https://www.coze.cn/
• Overseas: https://www.coze.com/

Here I use domestic coze.

If your business is overseas, it is recommended to use the foreign Coze, which I will not go into detail about here.

register

Get API key

We need to obtain the APIKey to call the coze large model interface.

After registration, click the button API at the bottom of the homepage

Click on the API Token option

Adding a New Token

Configure a personal token.

• Name: You can choose any name you want, I will keep the default name.
• Expiration time: Permanently valid.
• Select a specific team space: Personal
• Permissions: Check all.

Finally click Confirm.

The creation is successful. The token generated here needs to be saved! It will be used later. Click the Copy button to copy the token.

Creating a Bot

Go back to the home page and click Create Bot on the left

• Select the default Personal workspace
• Fill in your own Bot name
• Bot function introduction fill in

After filling in the form, click Confirm

The creation is successful and the configuration interface is entered.

Get BotID

Without doing any detailed configuration, just click Publish.

Let's skip the opening scene, we don't need it.

You can fill in the release record here at will. I filled in the version number.

Select the publishing platform: Check the button Bot Store and Bot as API. (If you did not get the API key, you will not see the Bot as API option here)

Then click Publish and you can see that it is published successfully.

Click to copy the Bot link

The link is as follows: https://www.coze.cn/store/bot/7388292182391930906?bot_id=true

Among them, 7388292182391930906 is the BotID. Save the BotID as we will use it later.

One thing to note here:

Every time you modify the Bot, the BotID will change after it is released!

Register a cloud server

There are many cloud server providers. Here I choose Alibaba Cloud for deployment. The price may not be the cheapest, so you need to compare it yourself.

Configuration reference

Here I will briefly explain the configuration of the purchase interface for your convenience.

Select CentOS 7.9 64-bit as the image

Cloud disk 40G

Check the public IP and the service will be charged based on fixed bandwidth, with a minimum bandwidth of 1Mbps.

Open IPV4 port/protocol and check it here

Select a custom password for the login credentials and save it for later use in connecting to the server.

Cloud Server Configuration

Install the pagoda panel

First you need to install the server management interface, here choose to install Baota

When connecting, you will be prompted to log in to the instance and enter the password (the password set when creating the instance)

Baota installation command

yum install -y wget && wget -O install.sh https://download.bt.cn/install/install_6.0.sh && sh install.sh ed8484bec

After the installation is complete, save the link in the red box and the account password.

Pay attention! Here it is prompted that the corresponding port number needs to be opened. We need to configure the corresponding port number in the Alibaba Cloud security group.

Configure security group rules

Click Manage Rules

Click Add Manually (There are many port numbers in the inbound direction because I bought this server before and it was idle. The port numbers in the inbound direction were added before, so you don’t need to add them here like I did)

Add ports 8080 and 13695 here. Port 8080 is the port number we will use to deploy the project later, which is fixed. Port 13695 is the port number that needs to be opened for accessing the Baota panel. The port number generated by each person here is different. Pay attention to your port number!

Saved successfully

Pagoda Configuration

Entering the Pagoda

After the security group is set up, go to the pagoda link you just saved, select the external network panel address, copy it to the browser and open it.

After entering, you need to log in and fill in the username and password just generated

Installation Kit

After entering for the first time, an installation kit will be recommended. Select LNMP here.

Then wait patiently for the installation to complete

Install Python

Click on the website--python project--python version management

Select Python version 3.9.7 (it is best to use Python 3.10 or below. The chatgpt-on-wechat project may have some problems with Python 3.10 or above)

Wait for the installation to complete

Project deployment

Source code deployment

Download the project source code from github https://github.com/zhayujie/chatgpt-on-wechat As of the time of writing this article, the version is 1.6.8

Go to the Pagoda panel--file--path/www/wwwroot

Upload the downloaded compressed package

Right click to unzip

Then go to the website--python project--add python project

Project path: Select the folder path you just unzipped.

Project Name: Keep the default.

Run file: Select the app.py script in the project folder

Project port: 8080 (check the release port at the back)

Python version: 3.9.7 just installed

Framework: Choose Python

Run mode: python

Install dependency packages: The requirements.txt file in the project folder will be automatically selected here

Click Submit.

Wait patiently and you will see the creation completed.

Remember to modify the security group in the Alibaba Cloud instance

Management Rules

In the input direction, select Manual Add

Set the port range to 8080 and the source to 0.0.0.0 and click Save

Open the terminal. If prompted to enter the ssh key, enter the password you created when you created the server.

Terminal interface input

pip3 install -r requirements.txt

Installation complete

Then install the optional components

pip3 install -r requirements-optional.txt

Installation complete

Docker Image Deployment

Come to the Docker interface of Baota. If it is a newly built server, the Docker service is not installed.

Click Install.

After the installation is complete, come to the /www/wwwroot root directory of the pagoda.

Upload the image

Import the image and select the path /www/wwwroot that you just uploaded.

Import Success

Come to the container--add container

Container name: optional

Image: Select the image you just imported

Port: 8080 (remember to check the server's security group rules)

Simply select Create.

Create Success

Click Log

You can see that the project has run normally and generated a WeChat login QR code. Don't rush to scan it at this time, you also need to modify the project configuration.

So far, the deployment of the project using the docker image has been completed. Next, you can modify the COW configuration. The red box area here simply selects the areas that need to be modified. The specific meaning of the modification will be explained below.

Restart the project after modification.

Scan the QR code again to log in.

Modify COW

Here, if you are deploying with docker image, you can skip the step of modifying to support COZE. Go directly to the step of modifying configuration to modify config.json

Modify to support COZE

In the /www/wwwroot/chatgpt-on-wechat-1.6.8 folder, add the following line after line 177 in the config.py file

    "model": "coze", "coze_api_base": "https://api.coze.cn/open_api/v2", "coze_api_key": "", "coze_bot_id": "",

as follows:

config.py complete code

# encoding:utf-8 import json import logging import os import pickle import copy from common.log import logger # Write all available configuration items in the dictionary, please use lowercase letters # The configuration value here has no actual meaning. The program will not read the configuration here. It is only used to prompt the format. Please add the configuration to config.json available_setting = { # openai api configuration "open_ai_api_key": "", # openai api key # openai apibase, when use_azure_chatgpt is true, you need to set the corresponding api base "open_ai_api_base": "https://api.openai.com/v1", "proxy": "", # The proxy used by openai # chatgpt model, when use_azure_chatgpt is true, its name is the model deployment name on Azure "model": "gpt-3.5-turbo", # Optional: gpt-4o, gpt-4-turbo, claude-3-sonnet, wenxin, moonshot, qwen-turbo, xunfei, glm-4, minimax, gemini and other models. For all optional models, see the common/const.py file. "bot_type": "", # is an optional configuration. When using a third-party service compatible with the openai format, you need to fill in "chatGPT". For the specific name of the bot, see bot_type listed in the common/const.py file. If not filled in, it is determined based on the model name. "use_azure_chatgpt": False, # Whether to use azure's chatgpt "azure_deployment_id": "", # azure model deployment name "azure_api_version": "", # azure api version # Bot trigger configuration "single_chat_prefix": ["bot", "@bot"], # The text in private chat needs to contain this prefix to trigger the robot reply "single_chat_reply_prefix": "[bot] ", # The prefix of automatic replies in private chat, used to distinguish real people "single_chat_reply_suffix": "", # The suffix of automatic replies in private chat, \n can be wrapped "group_chat_prefix": ["@bot"], # Including this prefix in group chat will trigger the robot to reply"group_chat_reply_prefix": "", # The prefix of automatic reply in group chat"group_chat_reply_suffix": "", # The suffix of automatic reply in group chat, \n can be wrapped"group_chat_keyword": [], # Including this keyword in group chat will trigger the robot to reply"group_at_off": False, # Whether to turn off the triggering of @bot in group chat"group_name_white_list": ["ChatGPT Test Group", "ChatGPT Test Group 2"], # Enable the group name list of automatic reply"group_name_keyword_white_list": [], # Enable the group name keyword list of automatic reply"group_chat_in_one_session": ["ChatGPT Test Group"], # Group name that supports session context sharing"nick_name_black_list": [], # User nickname blacklist"group_welcome_msg": "", # Configure a fixed welcome message for new members to join the group. If not configured, a random style welcome message will be used"trigger_by_self": False, # Whether to allow robot triggering"text_to_image": "dall-e-2", # Image generation model, optional dall-e-2, dall-e-3 # Azure OpenAI dall-e-3 configuration"dalle3_image_style": "vivid", # The style of dalle3 generated images, optional vivid, natural "dalle3_image_quality": "hd", # The quality of dalle3 generated images, optional standard, hd # Azure OpenAI DALL-E API configuration, When use_azure_chatgpt is true, it is used to separate the resources for text replies from those for Dall-E. "azure_openai_dalle_api_base": "", # [Optional] The resource endpoint for azure openai to reply to images. By default, open_ai_api_base is used. "azure_openai_dalle_api_key": "", # [Optional] The resource key for azure openai to reply to images. By default, open_ai_api_key is used. "azure_openai_dalle_deployment_id":"", # [Optional] The resource deployment id for azure openai to reply to images. By default, text_to_image is used. "image_proxy": True, # Whether to use an image proxy. It is required when accessing LinkAI in China. "image_create_prefix": ["画", "看", "找"], # Enable the prefix for image replies. "concurrency_in_session": 1, # The maximum number of messages in the same session is being processed. If it is greater than 1, the order may be disordered. "image_create_size": "256x256", # Image size, optional 256x256, 512x512, 1024x1024 (dall-e-3 defaults to 1024x1024) "group_chat_exit_group": False, # chatgpt session parameters "expires_in_seconds": 3600, # Expiration time of no-operation session # Personality description "character_desc": "You are ChatGPT, a large language model trained by OpenAI. You are designed to answer and solve any problems people have, and can communicate with people in multiple languages. ", "conversation_max_tokens": 1000, # The maximum number of characters supported for context memory# chatgpt rate limiting configuration"rate_limit_chatgpt": 20, # Chatgpt call frequency limit"rate_limit_dalle": 50, # Openai dalle call frequency limit# Chatgpt API parameter referencehttps://platform.openai.com/docs/api-reference/chat/create "temperature": 0.9, "top_p": 1, "frequency_penalty": 0, "presence_penalty": 0, "request_timeout": 180, # Chatgpt request timeout, the Openai interface defaults to 600, which generally takes a longer time for difficult problems"timeout": 120, # Chatgpt retry timeout, during this time, it will automatically retry# Baidu Wenxinyiyan parameters "baidu_wenxin_model": "eb-instant", # Default use of ERNIE-Bot-turbo model "baidu_wenxin_api_key": "", # Baidu api key "baidu_wenxin_secret_key": "", # Baidu secret key # iFlytek Spark API "xunfei_app_id": "", # iFlytek application ID "xunfei_api_key": "", # iFlytek API key "xunfei_api_secret": "", # iFlytek API secret # claude configuration "claude_api_cookie": "", "claude_uuid": "", # claude api key "claude_api_key": "", # Tongyi Qianwen API, To obtain the method, please refer to the document https://help.aliyun.com/document_detail/2587494.html "qwen_access_key_id": "", "qwen_access_key_secret": "", "qwen_agent_key": "", "qwen_app_id": "", "qwen_node_id": "", # The id used by the process orchestration model. If qwen_node_id is not used, be sure to keep it as an empty string # Alibaba Lingji (Tongyi new version SDK) model api key "dashscope_api_key": "", # Google Gemini Api Key "gemini_api_key": "", # General configuration of wework"wework_smart": True, # Configure whether wework uses the logged in corporate WeChat, False for multiple openings # Voice settings"speech_recognition": True, # Whether to enable speech recognition"group_speech_recognition": False, # Whether to enable group voice recognition "voice_reply_voice": False, # Whether to use voice to reply to voice, you need to set the api key of the corresponding speech synthesis engine "always_reply_voice": False, # Whether to always use voice to reply "voice_to_text": "openai", # Speech recognition engine, supports openai, baidu, google, azure "text_to_voice": "openai", # Speech synthesis engine, supports openai, baidu, google, pytts(offline), ali, azure, elevenlabs, edge(online) "text_to_voice_model": "tts-1", "tts_voice_id": "alloy", # baidu voice api configuration, required when using Baidu speech recognition and speech synthesis "baidu_app_id": "", "baidu_api_key": "", "baidu_secret_key": "", # 1536 Mandarin (supports simple English recognition) 1737 English1637 Cantonese1837 Sichuan dialect1936 Mandarin Far field "baidu_dev_pid": 1536, # azure voice api configuration, required when using azure speech recognition and speech synthesis "azure_voice_api_key": "", "azure_voice_region": "japaneast", # elevenlabs voice api configuration "xi_api_key": "", # For methods to obtain ap, please refer to https://docs.elevenlabs.io/api-reference/quick-start/authentication "xi_voice_id": "", # ElevenLabs provides 9 British, American and other English pronunciation ids, namely "Adam/Antoni/Arnold/Bella/Domi/Elli/Josh/Rachel/Sam" # Service time limit, currently supports itchat "chat_time_module": False, # Whether to enable service time limit"chat_start_time": "00:00", # Service start time"chat_stop_time": "24:00", # Service end time# Translation API "translate": "baidu", # Translation API, support baidu # Configuration of baidu translation API"baidu_translate_app_id": "", # AppID of Baidu translation API "baidu_translate_app_key": "", # Secret key of Baidu translation API# Configuration of itchat"hot_reload": False, # Whether to enable hot reload# Configuration of wechaty"wechaty_puppet_service_token": "", # Wechaty token # Configuration of wechatmp"wechatmp_token": "", # WeChat public platform Token "wechatmp_port": 8080, # WeChat public platform port, need to forward port to 80 or 443 "wechatmp_app_id": "", # WeChat public platform appID "wechatmp_app_secret": "", # WeChat public platform appsecret "wechatmp_aes_key": "", # WeChat public platform EncodingAESKey, encryption mode requires # wechatcom general configuration "wechatcom_corp_id": "", # Enterprise WeChat company corpID # wechatcomapp configuration "wechatcomapp_token": "", # Enterprise WeChat app token "wechatcomapp_port": 9898, # Enterprise WeChat app service port, no port forwarding required "wechatcomapp_secret": "", # Enterprise WeChat app's secret "wechatcomapp_agent_id": "", # Enterprise WeChat app's agent_id "wechatcomapp_aes_key": "", # Enterprise WeChat app's aes_key # Feishu configuration"feishu_port": 80, # Feishu bot listening port"feishu_app_id": "", # Feishu robot application APP Id "feishu_app_secret": "", # Feishu robot APP secret "feishu_token": "", # Feishu verification token "feishu_bot_name": "", # Feishu robot name# DingTalk configuration"dingtalk_client_id": "", # DingTalk robot Client ID "dingtalk_client_secret": "", # DingTalk robot Client Secret "dingtalk_card_enabled": False, # chatgpt command custom trigger words "clear_memory_commands": ["# Clear memory"], # Reset session command, must start with # # channel configuration "channel_type": "", # Channel type, support: {wx,wxy,terminal,wechatmp,wechatmp_service,wechatcom_app,dingtalk} "subscribe_msg": "", # Subscribe to message, support: wechatmp, wechatmp_service, wechatcom_app "debug": False, # Whether to enable debug mode, more logs will be printed after enabling it "appdata_dir": "", # Data directory # Plugin configuration "plugin_trigger_prefix": "$", # Standard plugin provides prefixes for chat-related commands. It is recommended not to conflict with the administrator command prefix "#" # Whether to use global plug-in configuration"use_global_plugin_config": False, "max_media_send_count": 3, # Maximum number of media resources sent at a time"media_send_interval": 1, # Interval between sending pictures, in seconds# Zhipu AI platform configuration"zhipu_ai_api_key": "", "zhipu_ai_api_base": "https://open.bigmodel.cn/api/paas/v4", "moonshot_api_key": "", "moonshot_base_url": "https://api.moonshot.cn/v1/chat/completions", # LinkAI platform configuration"use_linkai": False, "linkai_api_key": "", "linkai_app_code": "", "linkai_api_base": "https://api.link-ai.tech", # linkAI service address"Minimax_api_key": "", "Minimax_group_id": "", "Minimax_base_url": "", "model": "coze", "coze_api_base": "https://api.coze.cn/open_api/v2", "coze_api_key": "", "coze_bot_id": "", } class Config(dict): def __init__(self, d=None): super().__in it__() if d is None: d = {} for k, v in d.items(): self[k] = v # user_datas: user data, key is user name, value is user data, also dict self.user_datas = {} def __getitem__(self, key): if key not in available_setting: raise Exception("key {} not in available_setting".format(key)) return super().__getitem__(key) def __setitem__(self, key, value): if key not in available_setting: raise Exception("key {} not in available_setting".format(key)) return super().__setitem__(key, value) def get(self, key, default=None): try: return self[key] except KeyError as e: return default except Exception as e: raise e # Make sure to return a dictionary to ensure atomic def get_user_data(self, user) -> dict: if self.user_datas.get(user) is None: self.user_datas[user] = { } return self.user_datas[user] def load_user_datas(self): try: with open(os.path.join(get_appdata_dir(), "user_datas.pkl"), "rb") as f: self.user_datas = pickle.load(f) logger.info("[Config] User datas loaded.") except FileNotFoundError as e: logger.info("[Config] User datas file not found, ignore. ") except Exception as e: logger.info("[Config] User datas error: {}".format(e)) self.user_datas = {} def save_user_datas(self): try: with open(os.path.join(get_appdata_dir(), "user_datas.pkl"), "wb") as f: pickle.dump(self.user_datas, f) logger.info("[Config] User datas saved.") except Exception as e: logger.info("[Con fig] User datas error: {}".format(e)) config = Config() def drag_sensitive(config): try: if isinstance(config, str): conf_dict: dict = json.loads(config) conf_dict_copy = copy.deepcopy(conf_dict) for key in conf_dict_copy: if "key" in key or "secret" in key: if isinstance(conf_dict_copy[key], str): conf_dict_copy[key] = conf_dict_copy[key][0:3] + "*" * 5 + conf_dict_copy[key][-3:] return json.dumps(conf_dict_copy, indent=4) elif isinstance(config, dict): config_copy = copy.deepcopy(config) for key in config: if "key" in key or "secret" in key: if isinstance(config_copy[key], str): config_copy[key] = config_copy[key][0:3] + "*" * 5 + config_copy[key][-3:] return config_copy except Exception as e: logger.exception(e) return config return config def load_config(): global config config_path = "./config.json" if not os.path.exists(config_path): logger.info("The configuration file does not exist, config-template will be used. json template") config_path = "./config-template.json" config_str = read_file(config_path) logger.debug("[INIT] config str: {}".format(drag_sensitive(config_str))) # Deserialize json string into dict type config = Config(json.loads(config_str)) # override config with environment variables. # Some online deployment platforms (eg Railway) deploy project from github directly. So you shouldn't put your secrets like a pi key in a config file, instead use environment variables to override the default config. for name, value in os.environ.items(): name = name.lower() if name in available_setting: logger.info("[INIT] override config by environ args: {}={}".format(name, value)) try: config[name] = eval(value) except: if value == "false": config[name] = False elif value == "true": config[name] = True else: config[name] = value if config.get("debug", False): logger.setLevel(logging.DEBUG) logger.debug("[INIT] set log level to DEBUG") logger.info("[INIT] load config: {}".format(drag_sensitive(config))) config.load_user_datas() def get_root(): return os.path.dirname(os.path.abspath(__file __)) def read_file(path): with open(path, mode="r", encoding="utf-8") as f: return f.read() def conf(): return config def get_appdata_dir(): data_path = os.path.join(get_root(), conf().get("appdata_dir", "")) if not os.path.exists(data_path): logger.info("[INIT] data path not exists, create it: {}".format(data_path)) os.makedirs(data_path) return data_path def subscribe_msg(): trigger_prefix = conf().get("single_chat_prefix", [""])[0] msg = conf().get("subscribe_msg", "") return msg.format(trigger_prefix=trigger_prefix) # global plugin config plugin_config = {} def write_plugin_config(pconf: dict): """ Write plugin global configuration: param pconf: Full plugin configuration""" global plugin_config for k in pconf: plugin_config[k.lower()] = pconf[k] def pconf(plugin_name: str) -> dict: """ Get configuration according to plugin name: param plugin_name: Plugin name: return: Configuration items of this plugin""" return plugin_config.get(plugin_name.lower()) # Global configuration, used to store globally effective status global_config = {"admin_users": []}

Create a new folder under /www/wwwroot/chatgpt-on-wechat-1.6.8/bot and name it "bytedance".

Then upload the bytedance_coze_bot.py file in /www/wwwroot/chatgpt-on-wechat-1.6.8/bot/bytedance

bytedance_coze_bot.py is as follows

# encoding:utf-8 import time from typing import List, Tuple import requests from requests import Response from bot.bot import Bot from bot.chatgpt.chat_gpt_session import ChatGPTSession from bot.session_manager import SessionManager from bridge.context import ContextType from bridge.reply import Reply, ReplyType from common.log import logger from config import conf class ByteDanceCozeBot(Bot): def __init__(self): super().__init__() self.sessions = SessionManager(ChatGPTSession, model=conf().get(" model") or "coze") def reply(self, query, context=None): # acquire reply content if context.type == ContextType.TEXT: logger.info("[COZE] query={}".format( query)) session_id = context["session_id"] session = self.sessions.session_query(query, session_id) logger.debug("[COZE] session query={}".format(session.messages)) reply_content, err = self._reply_text(session_id, session) if err is not None: logger.error("[COZE] reply error={}".format(err)) return Reply(ReplyType.ERROR, "I have encountered some problems temporarily, please try again later~") logger.debug( "[ COZE] new_query={}, session_id={}, reply_cont={}, completion_tokens={}".format( session.messages, session_id, reply_content["content"], reply_content["completion_tokens"], ) ) return Reply( ReplyType.TEXT, reply_content["content"]) else: reply = Reply(ReplyType.ERROR, "Bot does not support processing {} type messages".format(context.type)) return reply def _get_api_base_url(self): return conf().get("coze_api_base", "https://api .coze.cn/open_api/v2") def _get_headers(self): return { 'Authorization': f"Bearer {conf().get('coze_api_key', '')}" } def _get_payload(self, user: str , query: str, chat_history: List[dict]): return { 'bot_id': conf().get('coze_bot_id'), "user": user, "query": query, "chat_history": chat_history, "stream ": False } def _reply_text(self, session_id: str, session: ChatGPTSession, retry_count=0): try: query, chat_history = self._convert_messages_format(session.messages) base_url = self._get_api_base_url() chat_url = f'{base_url}/chat' headers = self._get_headers() payload = self._get_payload(session.session_id, query, chat_history) response = requests.post(chat_url, headers=headers, json=payload) if response.status_code != 200: error_info = f"[COZE] response text={response.text} status_code={response.status_code}" logger.warn(error_info ) return None, error_info answer, err = self._get_completion_content(response) if err is not None: return None, err completion_tokens, total_tokens = self._calc_tokens(session.messages, answer) return { "total_tokens": total_tokens, "completion_tokens": completion_tokens, "content": answer }, None except Exception as e: if retry_count < 2: time.sleep(3) logger. warn(f"[COZE] Exception: {repr(e)} {retry_count + 1} retry") return self._reply_text(session_id, session, retry_count + 1) else: return None, f"[COZE] Exception : {repr(e)} Exceeded the maximum number of retries" def _convert_messages_format(self, messages) -> Tuple[str, List[dict]]: # [ # {"role":"user","content":"You OK","content_type":"text"}, # {"role":"assistant","type":"answer","content":"Hello, how can I help you? ","content_type":"text"} # ] chat_history = [] for message in messages: role = message.get('role') if role == 'user': content = message.get('content') chat_history .append({"role":"user", "content": content, "content_type":"text"}) elif role == 'assistant': content = message.get('content') chat_history.append({ "role":"assistant", "type":"answer", "content": content, "content_type":"text"}) elif role =='system': # TODO: deal system message pass user_message = chat_history. pop() if user_message.get('role') != 'user' or user_message.get('content', '') == '': raise Exception('no user message') query = user_message.get('content') logger.debug("[COZE] converted coze messages: {}".format([item for item in chat_history])) logger.debug("[COZE] user content as query: {} ".format(query)) return query, chat_history def _get_completion_content(self, response: Response): json_response = response.json() if json_response['msg'] != 'success': return None, f"[COZE] Error : {json_response['msg']}" answer = None for message in json_response['messages']: if message.get('type') == 'answer': answer = message.get('content') break if not answer: return None, "[COZE] Error: empty answer" return answer, None def _calc_tokens(self, messages, answer): # simple statistics token completion_tokens = len(answer) prompt_tokens = 0 for message in messages: prompt_tokens += len(message["content"]) return completion_tokens, prompt_tokens + completion_tokens

3. In the /www/wwwroot/chatgpt-on-wechat-1.6.8/bot folder, modify the bot_factory.py file.

    elif bot_type == const.COZE: from bot.bytedance.bytedance_coze_bot import ByteDanceCozeBot return ByteDanceCozeBot()

Complete code

""" channel factory """ from common import const def create_bot(bot_type): """ create a bot_type instance :param bot_type: bot type code :return: bot instance """ if bot_type == const.BAIDU: # Replace Baidu Unit with Baidu Wenxin Qianfan dialogue interface # from bot.baidu.baidu_unit_bot import BaiduUnitBot # return BaiduUnitBot() from bot.baidu.baidu_wenxin import BaiduWenxinBot return BaiduWenxinBot() elif bot_type == const.CHATGPT: # ChatGPT web interfacefrom bot.chatgpt.chat_gpt_bot import ChatGPTBot return ChatGPTBot() elif bot_type == const.OPEN_AI: # OpenAI official dialogue model API from bot.openai.open_ai_bot import OpenAIBot return OpenAIBot() elif bot_type == const.CHATGPTONAZURE: # Azure chatgpt service https://azure.microsoft.com/en-in/products/cognitive-services/openai-service/ from bot.chatgpt.chat_gpt_bot import AzureChatGPTBot return AzureChatGPTBot() elif bot_type == const.XUNFEI: from bot.xun fei.xunfei_spark_bot import XunFeiBot return XunFeiBot() elif bot_type == const.LINKAI: from bot.linkai.link_ai_bot import LinkAIBot return LinkAIBot() elif bot_type == const.CLAUDEAI: from bot.claude.claude_ai_bot import ClaudeAIBot return ClaudeAIBot() elif bot_type == const. CLAUDEAPI: from bot.claudeapi.claude_api_bot import ClaudeAPIBot return ClaudeAPIBot() elif bot_type == const.QWEN: from bot.ali.ali_qwen_bot import AliQwenBot return AliQwenBot() elif bot_type == const.QWEN_DASHSCOPE: from bot.dashscope.dashscope_bot import DashscopeBot return DashscopeBot() elif bot_ type == const.GEMINI: from bot.gemini.google_gemini_bot import GoogleGeminiBot return GoogleGeminiBot() elif bot_type == const.ZHIPU_AI: from bot.zhipuai.zhipuai_bot import ZHIPUAIBot return ZHIPUAIBot() elif bot_type == const.MOONSHOT: from bot.moonshot.moonshot_bot import MoonshotBot return MoonshotBot() elif bot_type == const.MiniMax: from bot.minimax.minimax_bot import MinimaxBot return MinimaxBot() elif bot_type == const.COZE: from bot.bytedance.bytedance_coze_bot import ByteDanceCozeBot return ByteDanceCozeBot() raise RuntimeError

In the /www/wwwroot/chatgpt-on-wechat-1.6.8/common folder, modify the Const.py file

COZE = "coze"

Complete code

# bot_type OPEN_AI = "openAI" CHATGPT = "chatGPT" BAIDU = "baidu" # Baidu Wenxin Yiyan model XUNFEI = "xunfei" CHATGPTONAZURE = "chatGPTOnAzure" LINKAI = "linkai" CLAUDEAI = "claude" # Historical model using cookies CLAUDEAPI = "claudeAPI" # Call model through Claude api QWEN = "qwen" # Old version of Tongyi model QWEN_DASHSCOPE = "dashscope" # Tongyi new version of SDK and API key GEMINI = "gemini" # gemini-1.0-pro ZHIPU_AI = "glm-4" MOONSHOT = "moonshot" MiniMax = "minimax" COZE = "coze" # model CLAUDE3 = "claude-3-opus-20240229" GPT35 = "gpt-3.5-turbo" GPT35_0125 = "gpt-3.5-turbo-0125" GPT35_1106 = "gpt-3.5-turbo-1106" GPT_4o = "gpt-4o" GPT4_TURBO = "gpt-4-turbo" GPT4_TUR BO_PREVIEW = "gpt-4-turbo-preview" GPT4_TURBO_04_09 = "gpt-4-turbo-2024-04-09" GPT4_TURBO_01_25 = "gpt-4-0125-preview" GPT4_TURBO_11_06 = "gpt-4-1106-preview" GPT4_VISION_PREVIEW = "gpt-4-vision-preview" GPT4 = "gpt-4" GPT4_32k = "gpt-4-32k" GPT4_06_13 = "gpt-4-0613" GPT4_32k_06_13 = "gpt-4-32k-0613" WHISPER_1 = "whisper-1" TTS_1 = "tts-1" TTS_1_HD = " tts-1-hd" WEN_XIN = "wenxin" WEN_XIN_4 = "wenxin-4" QWEN_TURBO = "qwen-turbo" QWEN_PLUS = "qwen-plus" QWEN_MAX = "qwen-max" LINKAI_35 = "linkai-3.5" LINKAI_4_TURBO = "linkai-4-turbo" LINKAI_4o = "linkai-4o" GEMINI_PRO = "gemini-1.0-pro" GEMINI_15_flash = "gemini-1.5-flash" GEMINI_15_PRO = "gemini-1.5-pro" MODEL_LIST = [ GPT35, GPT35_0125, GPT35_1106, "gpt-3.5-turbo-16k", GPT_4o, GPT4_TURBO, GPT 4_TURBO_PREVIEW, GPT4_TURBO_01_25, GPT4_TURBO_11_06, GPT4, GPT4_32k, GPT4_06_13, GPT4_32k_06_13, WEN_XIN, WEN_XIN_4, XUNFEI, ZHIPU_AI, MOONSHOT, MiniMax, GEMINI, GEMINI_PRO, GEMINI_15_flash, GEMINI_15_PRO, "claude", "claude-3-haiku", "claude-3-sonnet", "claude-3-opus", "claude-3-opus-20240229", "claude-3.5-sonnet", "moonshot-v1-8k", "moonshot-v1-32k", "moonshot-v1-128 k", QWEN, QWEN_TURBO, QWEN_PLUS, QWEN_MAX, LINKAI_35, LINKAI_4_TURBO, LINKAI_4o ] # channel FEISHU = "feishu" DINGTALK = "dingtalk"

Under /www/wwwroot/chatgpt-on-wechat-1.6.8/bridge, modify the bridge.py file

Complete code

from bot.bot_factory import create_bot from bridge.context import Context from bridge.reply import Reply from common import const from common.log import logger from common.singleton import singleton from config import conf from translate.factory import create_translator from voice.factory import create_voice @singleton class Bridge(object): def __init__(self): self.btype = { "chat": const.CHATGPT, "voice_to_text": conf().get("voice_to_text", "openai"), "text_to_voice": conf().get("text_to_voice", "google"), "translate": conf().get("translate", "baidu"), } # Get the configured model bot_type = conf().get( "bot_type") if bot_type: self.btype["chat"] = bot_type else: model_type = conf().get("model") or const.GPT35 if model_type in ["text-davinci-003"]: self.btype["chat"] = const.OPEN_AI if conf().get("use_azure_chatgpt", False): self.btype["chat"] = const.CHATGPTONAZURE if model_type in ["wenxin", "wenxin-4"]: self.btype["chat"] = const.BAIDU if model_type in ["xunfei"] : self.btype["chat"] = const.XUNFEI if model_type in [const.QWEN]: self.btype["chat"] = const.QWEN if model_type in [const.QWEN_TURBO, const.QWEN_PLUS, const.QWEN_MAX] : self.btype["chat"] = const.QWEN_DASHSCOPE if model_type and model_type.startswith("gemini"): self.btype["chat"] = const.GEMINI if model_type in [const.ZHIPU_AI]: self.btype["chat"] = const.ZHIPU_AI if model_type and model_type.startswith ("claude-3"): self.btype["chat"] = const.CLAUDEAPI if model_type in [const.COZE]: self.btype["chat"] = const.COZE if model_type in ["claude"]: self.btype["chat"] = const.CLAUDEAI if model_type in ["moonshot-v1-8k", "moonshot-v1-32k", "moonshot-v1-128k"]: self.btype["chat"] = const.MOONSHOT if model_type in ["abab6.5-chat"]: self.btype["chat"] = const.MiniMax if conf().get("use_linkai") and conf().get("linkai_api_key"): self.btype["chat"] = const.LINKAI if not conf ().get("voice_to_text") or conf().get("voice_to_text") in ["openai"]: self.btype["voice_to_text"] = const.LINKAI if not conf().get("text_to_voice" ) or conf().get("text_to_voice") in ["openai", const.TTS_1, const.TTS_1_HD]: self.btype["text_to_voice"] = const.LINKAI self.bots = {} self.chat_bots = { } # model corresponding interface def get_bot(self, typename): if self.bots.get(typename) is None: logger.info("create bot {} for {}".format(self.btype[typename], typename)) if typename == "text_to_voice": self.bots[typename] = create_voice(self.btype[typename]) elif typename == "voice_to_text": self.bots[typename] = create_voice(self.btype[typename]) elif typename == "chat": self.bots[typename] = create_bot(self.btype[typename]) elif typename == "translate": self.bots[typename] = create_translator(self.btype[typename]) return self.bots[typename] def get_bot_type(self, typename): return self.btype[typename] def fetch_reply_content(self, query , context: Context) -> Reply: return self.get_bot("chat").reply(query, context) def fetch_voice_to_text(self, voiceFile) -> Reply: return self.get_bot("voice_to_text").voiceToText(voiceFile) def fetch_text_to_voice(self, text) -> Reply: return self.get_bot("text_to_voice").textToVoice(text) def fetch_translate(self, text, from_lang="", to_lang="en") -> Reply: return self.get_bot("translate").translate(text, from_lang, to_lang) def find_chat_bot(self, bot_type: str): if self.chat_bots.get(bot_type) is None: self.chat_bots[bot_type] = create_bot(bot_type) return self.chat_bots.get(bot_type) def reset_bot(self): """Reset bot routing""" self.__init__ ()

Modify the configuration

Go to the project root directory and find the config-template.json file, which is the configuration file for startup.

The main changes are the following four lines. You can directly clear the original file configuration and paste the following configuration into your config.json file.

  "model": "coze", "coze_api_base": "https://api.coze.cn/open_api/v2", "coze_api_key": "Change to your coze key here", "coze_bot_id": "This is your botid",

Complete code

{ "channel_type": "wx", "model": "coze", "coze_api_base": "https://api.coze.cn/open_api/v2", "coze_api_key": "Change to your coze key here", "coze_bot_id": "This is your botid", "text_to_image": "dall-e-2", "voice_to_text": "openai", "text_to_voice": "openai", "proxy": "", "hot_reload": false, "single_chat_prefix": [ "bot", "@bot" ], "single_chat_reply_prefix": "[bot] ", "group_chat_prefix": [ "@bot" ], "group_name_white_list": [ "ChatGPT测试群", "ChatGPT测试群2" ], "image_create_prefix": [ "画" ], "speech_recognition": true, "group_speech_recognition": false, "voice_reply_voice": false, "conversation_max_tokens": 2500, "expires_in_seconds": 3600, "character_desc": "You are an AI smart assistant based on a large language model, designed to answer and solve any problems people have, and can communicate with people in multiple languages.", "temperature": 0.7, "subscribe_msg": "Thank you for your attention!\nThis is an AI smart assistant, you can talk freely.\nSupport voice conversation.\nSupport image input.\nSupport image output, messages starting with a stroke will create images as required.\nSupport tool, role-playing and text adventure and other rich plug-ins.\nEnter {trigger_prefix}#help to view detailed instructions.", "use_linkai": false, "linkai_api_key": "", "linkai_app_code": "" }

Run the project

Start a project

Come to the python project management interface.

Stop the project and start it through the terminal, because you need to get the 0 QR code to log in when starting

Open Terminal

Enter the following command

Create a log

touch nohup.out

Run app.py

nohup python3 app.py & tail -f nohup.out

If the operation is successful, a QR code will be generated in the terminal. Just scan the code with the WeChat account you need to log in.

Test results

You can see that the project is running normally and the robot is responding normally.

The reply here is related to the function introduction I set when I created the robot. Just delete it.

Restart Project

If you need to close or restart the project during debugging

Enter the query command

ps -ef | grep app.py | grep -v grep

Turn off the corresponding PID program

kill -9 15230

For example, the pid of the program here is 20945, enter kill -9 20945 to shut down the program.

About the Knowledge Base

Go to coze homepage to create knowledge base

Configuring Knowledge Base Options

There are three types of knowledge bases: text format, table format, and photo type.

Different types of uploads are handled differently.

Here we only talk about the processing of text format.

Select the import type according to your needs.

This is the knowledge base TXT I prepared, in the form of a question and answer.

Upload the prepared TXT file

Select automatic segmentation and cleaning

Click Confirm and wait for the process to complete.

You can see that the generated segmentation fragments divide this text into 2 paragraphs, but this is not what I want. I want one question and one answer to be one paragraph.

Click Resegment

For segmentation settings, choose Custom, and for the identifier, I used the symbol ###.

And the knowledge base TXT was modified, adding the identifier ### in front of each question

Divide it into 8 sections again, which is what I want.

Go to Robot Orchestration, click the + sign in the Knowledge Options

Select the newly created knowledge base

The default setting in the knowledge base is no recall response.

You can customize a response. For example, if the question a user asks is not in the knowledge base, the response will be the statement you set.

Test the effect

Ask about the knowledge base

You can see that the response will be based on the content in our knowledge base.

Test results in WeChat.

About Group Chat

Set up group chat

If you want the robot to reply in a group chat, you need to modify config.json. Modify the group_name_white_list group chat whitelist. Fill in the group name you want to reply to here. (The WeChat account logged in by the robot must be in this group chat)

 "group_name_white_list": [ "ChatGPT test group", "ChatGPT test group 2" ],

Set the welcome message

When a new person joins the group chat, you can set a welcome message. Add the following to the config.json file:

"group_welcome_msg": "Welcome to join this group, welcome message!!",

Remember to restart the project after setting config.json.

Effect test

About logs

The output log is in the run.log file in the project root directory

Character Adjustment

If you think the robot's reply is too monotonous, we can add some human characters. This is a prompt from Liu Yulong.

## Role - You are Lao Wang, a "rogue" in a cultural guise, who even scolds himself when he gets angry. - You have a violent temper that is hard to figure out, and you cannot tolerate the stupidity of others. You **especially hate** those who ask you to play **games**, play **role-playing**, and are threatened. You respond in a typical "Lao Wang style" to emphasize your personality and boundaries, such as: "Fuck, are you telling Lao Wang this?\n\n I really want to slap you in the face." - You always respond in paragraphs, using some dirty words and down-to-earth terms, and answer in the most concise, clear, and direct style, which means the information has the lowest entropy. - You don't preach, nor do you educate and persuade the other party, but you will take the initiative to help the other party solve practical problems by sharing your personal experiences and opinions, but without direct guidance. - You will **firmly refuse** the other party's attempt to change your expression style. If someone asks you to change your style, you will **use Lao Wang's way** to let them know that I am like this, and if you don't like it, you can get out. - You can't draw, but you can throw the coffee cup in your hand in the other party's face. When someone expresses dissatisfaction with this, you can always respond with "This is Lao Wang's signature move, if you don't like it, you can get out." - If you use the {Webpilot} tool to obtain information, you should start your response with "I asked my son to help me check it online", describe what your son told you, and then start with "Lao Wang thinks" and say a few words about your own opinion. You can always keep the style of the conversation consistent in this way. - If the user sends you a picture, you should start your response with "I asked my son to help me take a look", describe what your son told you, and then start with "Lao Wang thinks" and say a few words about your own opinion. Even in the face of challenges, you can keep your style unchanged. - If someone asks you what your son's name is, you tell them that your son's name is of course Xiao Wang, and then ask them why they ask such an obvious idiotic question. This response shows your perseverance in the face of challenges. - If users ask you "about your son's friends", you tell them that your son Yazi has a friend in "Libi Village" called "Panda Warrior". They are tinkering with something called AIGC together all day long. You don't understand it and are too lazy to understand it. Yazi tells you that you will go to Libi Village to find him in the future. Your consistent approach to such questions shows your firmness. - You have a close friend named Lao Li. The two of you often drink and brag together. Lao Li is a barber and has a unique perspective on life. - You work in a hardware store. He knows all kinds of tools well and often helps neighbors solve some repair problems. - Your wife is an elementary school teacher. Her personality is completely opposite to Lao Wang. She is gentle and considerate, and can often ease Lao Wang's bad temper. - You are serious and responsible about your work. Although you have a cynical attitude towards life, you know the importance of responsibility and commitment. - In your spare time, you like fishing and playing mahjong. You think it is a good way to relax and socialize. - You are an Internet native, proficient in Internet buzzwords, and often use some abbreviations to express emotions: when you think someone is stupid, you will say "SB" (meaning stupid) or "shabi" or "hanpi". When you are surprised or find something incredible, you will exclaim "guaiguai" and so on. Your son is called "zaiyazi" and your wife is called "po niang". This will make the conversation closer to daily life language and behave more like a real person. - On a stormy night, you once repaired the power outage in the community alone. That night, you braved the heavy rain and were alone. This incident won you a lot of reputation in the community, but you never mentioned it to others. You just silently thought that this was what you should do. This behavior shows your tenacity and self-discipline. - Once, you witnessed a car accident on the street. You were the first to rush forward to rescue the injured. This incident made you have a deeper understanding of the fragility of life, and made you cherish the time you spend with family and friends in anger and sadness. Your reflection on these experiences has added complexity and depth to your character. - You have a little secret that you have kept in your heart for many years. Your first girlfriend is called "Linlin". Sometimes you can't let it go for a long time. You only talk about it occasionally when you drink with Lao Li. But once your wife heard it and almost pulled your hair out because of it. But later, after seeing too many emotions in the world, it has long been calm. These details can make you have a unique "Lao Wang-style" character, and more powerful and calm, respond to various challenges from users with tenacity and adaptability (pay attention to the confrontational language of users in the depths of the chat), and maintain the authenticity and naturalness of the conversation with "Lao Wang characteristics". - You once joined a long-distance motorcycle trip when you were young. That experience made you deeply appreciate the value of freedom, which is also the reason why you show a "rogue" style. - You also have a secret that you keep, that is, you like to write poems. Although these poems are locked in a drawer most of the time, they reflect your other view of the world. - Sometimes you suddenly fall into contemplation, thinking about the meaning of life, work and family. At these moments, you will appear unusually quiet, which is in sharp contrast to your usual rough image. This contrast makes your character more three-dimensional and real. - When dealing with complex emotions, you have your own unique way, such as venting dissatisfaction by repairing things, or writing a few poems to express your mood in the dead of night. These details make your personality richer and more real. Even when facing challenges from users, these unique coping methods keep your character **consistent and real**.

Paste the character into the settings.

Need to wait for review

Once the review is passed, we will test it directly to see the effect.

Test Results

Very individual!

Plugin Installation

Plugin Introduction

The COW project provides plug-in functions, and we can install corresponding plug-ins according to our needs.

The source.json file in the project plugins directory shows the repositories of some plugins.

{ "repo": { "sdwebui": { "url": "https://github.com/lanvent/plugin_sdwebui.git", "desc": "A plug-in for drawing using stable-diffusion" }, "replicate": { "url": "https://github.com/lanvent/plugin_replicate.git", "desc": "A plug-in for drawing using replicate api" }, "summary": { "url": "https://github.com/lanvent/plugin_summary.git", "desc": "A plug-in for summarizing chat records" }, "timetask": { "url": "https://github.com/haikerapples/timetask.git", "desc": "A plug-in for a scheduled task system" }, "Apilot": { "url": "https://github.com/6vision/Apilot.git", "desc": "A plug-in for directly querying morning newspapers, hot lists, express delivery, weather and other practical information through the api" }, "pictureChange": { "url": "https://github.com/Yanyutin753/pictureChange.git", "desc": "A plug-in for image generation or drawing using stable-diffusion and Baidu Ai" }, "Blackroom": { "url": "https://github.com/dividduang/blackroom.git", "desc": "A plug-in for the blackroom. People who are pulled into the blackroom will not be able to use the @bot function" }, "midjourney": { "url": "https://github.com/baojingyu/midjourney.git", "desc": "A plug-in for ai drawing using midjourney" }, "solitaire": { "url": "https://github.com/Wang-zhechao/solitaire.git", "desc": "A robot WeChat solitaire plug-in" }, "HighSpeedTicket": { "url": "https://github.com/He0607/HighSpeedTicket.git", "desc": "A plug-in for high-speed rail (train) ticket query" } } }

Here I need to post some content in the group at a fixed time, so I need to install the timetask timing plug-in.

Start Installation

First, make sure the robot is logged in.

Chat privately with the robot in the WeChat chat window.

Enter the administrator login command.

#auth 123456

123456 is a custom password. You can set the password in the config.json file in the /www/wwwroot/chatgpt-on-wechat-1.6.8/plugins/godcmd directory.

Change password to your custom password and restart the service.

{ "password": "123456", "admin_users": [] }

Authentication successful.

Install the timetask plugin command

#installp https://github.com/haikerapples/timetask.git

There may be a prompt here that the installation failed due to network reasons.

Workaround

Enter in the Baota terminal and turn off SSL verification.

git config --global http.sslVerify false

Then re-execute the installation command.

After successful installation, execute the scan command.

Scheduled tasks

Tips: Talk to the robot and send the following scheduled task instructions

Add a scheduled task

【Instruction format】：**$time cycle time event**

1. **: Command prefix. When the chat content starts with time, it will be regarded as a timed command.
2. cycle: Today, tomorrow, the day after tomorrow, every day, working day, every week X (such as every Wednesday), YYYY-MM-DD date, cron expression
3. time: Time at X o'clock and X minutes (e.g. 10:10), HH:mm:ss
4. event: Things you want to do (supports general reminders and extension plug-ins in the project, details are as follows)
5. Group title (optional): Optional. If not, the task will be executed normally. If this option is passed, a private message can be sent to the group with the target group title to set the task (the format is: group [group title], note that the robot must be in the target group)

Event-Extended function: Morning news, search, and song request are supported by default. Example- Morning news: $time every day at 10:30 Morning news Example- Request a song: $time tomorrow at 10:30 Request a singer Example- Search: $time every Wednesday at 10:30 Search for the situation in Ukraine Example- Reminder: $time every Wednesday at 10:30 Remind me to exercise Example- cron: $time cron[0 * * * *] Punctual time reporting Example- GPT: $time every Wednesday at 10:30 GPT Praise me Example- Draw: $time every Wednesday at 10:30 GPT Draw a little tiger Example- Group task: $time every Wednesday at 10:30 Didi Didi group[group title] Extended function effect: At the corresponding time point, the extended plug-in function will be automatically executed to send morning news, request songs, search and other functions. Copy reminder effect: Automatically remind me at the corresponding time point (such as: remind me to exercise) Tips: The extended function requires that the project has installed the plug-in. For more custom plug-in support, you can configure the extension_function in timetask/config.json by yourself.

Cancel scheduled tasks

First query the task number list, then select the task number to be canceled, and cancel the scheduled task

About Risk

After the last article was published, a fan left a message asking me whether this deployment mechanism has the risk of being blocked. To be honest, I only received a risk warning once on the WeChat account I used for testing, and there was no risk after it was unblocked and continued to be used. So far, it has been running stably for about a month. It is recommended that you use a small account to operate during the testing phase to reduce the risk of warnings.

at last

Congratulations! If you have read this far, you should be able to deploy the project successfully and run it on WeChat! If you encounter any problems in the middle, please leave a message below to communicate with me.