當我們使用ChatGPT完成某些作業的時候，往往需要多輪對話，比如讓ChatGPT分析、翻譯、總結一篇網上的文章或者檔案，再將總結的結果以文本的形式存盤在本地，程序中免不了要和ChatGPT“折沖樽俎”一番，事實上，這個“交涉”的程序也可以自動化，AutoGPT可以幫助我們自動拆解任務，沒錯，程式能做到的事情，人類絕不親力親為，
我們唯一需要做的，就是告訴AutoGPT一個任務目標，AutoGPT會自動根據任務目標將任務拆解成一個個的小任務，并且逐個完成，簡單且高效，
配置AutoGPT
先確保本地環境安裝好了Python3.10.9，
接著運行Git命令拉取專案：
git clone https://github.com/Significant-Gravitas/Auto-GPT.git
隨后進入專案的目錄：
cd Auto-GPT
安裝相關的依賴庫：
pip3 install -r requirements.txt
安裝成功后，復制一下專案的組態檔：
cp .env.template .env
這里通過cp命令將組態檔模版.env.template復制成為一個新的組態檔.env，
隨后將Openai的秘鑰填入組態檔：
### OPENAI
# OPENAI_API_KEY - OpenAI API Key (Example: my-openai-api-key)
# TEMPERATURE - Sets temperature in OpenAI (Default: 0)
# USE_AZURE - Use Azure OpenAI or not (Default: False)
OPENAI_API_KEY=您的秘鑰
TEMPERATURE=0
USE_AZURE=False
除了Openai官方的介面秘鑰，AutoGPT也支持微軟Azure的介面，
如果希望使用微軟Azure的介面，需要將配置中的USE_AZURE設定為True，隨后復制azure.yaml.template配置模版為新的azure.yaml組態檔，
接著將微軟Azure服務的秘鑰填入azure.yaml即可，
由于微軟Azure接入Openai介面需要極其復雜的申請流程，這里還是直接使用OpenAI官方的介面，
當然了，如果不想在本地裝那么多依賴，也可以通過Docker來構建Auto-GPT的容器：
docker build -t autogpt .
docker run -it --env-file=./.env -v $PWD/auto_gpt_workspace:/app/auto_gpt_workspace autogpt
這里Docker會自動讀取專案中的Dockerfile組態檔進行構建，相當方便，
至此，Auto-GPT就配置好了，
運行Auto-GPT
在專案根目錄運行命令：
python3 -m autogpt --debug
即可啟動AutoGPT:
? Auto-GPT git:(master) python -m autogpt --debug
Warning: The file 'AutoGpt.json' does not exist. Local memory would not be saved to a file.
Debug Mode: ENABLED
Welcome to Auto-GPT! Enter the name of your AI and its role below. Entering nothing will load defaults.
Name your AI: For example, 'Entrepreneur-GPT'
AI Name:
首先創建AutoGPT機器人的名字：
AI Name: v3u.cn
v3u.cn here! I am at your service.
Describe your AI's role: For example, 'an AI designed to autonomously develop and run businesses with the sole goal of increasing your net worth.'
v3u.cn is:
創建好名字以后，Auto-GPT就可以隨時為您效勞了，
首先為AutoGPT設定目標：
v3u.cn is: Analyze the contents of this article,the url is https://v3u.cn/a_id_303,and write the result to goal.txt
這里我們要求AutoGPT分析并且總結v3u.cn/a_id_303這篇文章，并且將分析結果寫入本地的goal.txt檔案，
程式回傳：
Enter up to 5 goals for your AI: For example: Increase net worth, Grow Twitter Account, Develop and manage multiple businesses autonomously'
Enter nothing to load defaults, enter nothing when finished.
Goal 1:
Using memory of type: LocalCache
AutoGPT會告訴你可以最多拆解為五個任務，我們可以自己拆解，也可以讓機器人幫助我們拆解，直接按回車，讓AutoGPT自動拆解任務即可，
接著程式會自動爬取這篇文章的內容，然后使用gpt-3.5-turbo模型來進行分析：
Goal 1:
Using memory of type: LocalCache
Using Browser: chrome
Token limit: 4000
Memory Stats: (0, (0, 1536))
Token limit: 4000
Send Token Count: 936
Tokens remaining for response: 3064
------------ CONTEXT SENT TO AI ---------------
System: The current time and date is Mon Apr 17 20:29:37 2023
System: This reminds you of these events from your past:
User: Determine which next command to use, and respond using the format specified above:
----------- END OF CONTEXT ----------------
Creating chat completion with model gpt-3.5-turbo, temperature 0.0, max_tokens 3064
The JSON object is valid.
THOUGHTS: Let's start by browsing the article at https://v3u.cn/a_id_303 to get a better understanding of its contents.
REASONING: Browsing the article will allow us to analyze its contents and determine the appropriate next steps.
PLAN:
- Browse the article
- Analyze its contents
- Determine the appropriate next steps
CRITICISM: None
NEXT ACTION: COMMAND = browse_website ARGUMENTS = {'url': 'https://v3u.cn/a_id_303', 'question': 'analyze the contents of the article'}
Enter 'y' to authorise command, 'y -N' to run N continuous commands, 'n' to exit program, or enter feedback for ...
Input:y
-=-=-=-=-=-=-= COMMAND AUTHORISED BY USER -=-=-=-=-=-=-=
[WDM] - Downloading: 100%|████████████████████████████████████████████| 8.04M/8.04M [00:03<00:00, 2.42MB/s]
Text length: 6977 characters
Adding chunk 1 / 1 to memory
Summarizing chunk 1 / 1
Creating chat completion with model gpt-3.5-turbo, temperature 0.0, max_tokens 300
SYSTEM: Command browse_website returned: Error: This model's maximum context length is 4097 tokens. However, you requested 4339 tokens (4039 in the messages, 300 in the completion). Please reduce the length of the messages or completion.
Token limit: 4000
Memory Stats: (2, (2, 1536))
Token limit: 4000
Send Token Count: 1472
Tokens remaining for response: 2528
------------ CONTEXT SENT TO AI ---------------
System: The current time and date is Mon Apr 17 20:30:19 2023
System: This reminds you of these events from your past:
['Assistant Reply: {\n "thoughts": {\n "text": "Let\'s start by browsing the article at https://v3u.cn/a_id_303 to get a better understanding of its contents.",\n "reasoning": "Browsing the article will allow us to analyze its contents and determine the appropriate next steps.",\n "plan": "- Browse the article\\n- Analyze its contents\\n- Determine the appropriate next steps",\n "criticism": "None",\n "speak": "I suggest we start by browsing the article at the given URL to analyze its contents and determine the appropriate next steps."\n },\n "command": {\n "name": "browse_website",\n "args": {\n "url": "https://v3u.cn/a_id_303",\n "question": "analyze the contents of the article"\n }\n }\n} \nResult: Command browse_website returned: Error: This model\'s maximum context length is 4097 tokens. However, you requested 4339 tokens (4039 in the messages, 300 in the completion). Please reduce the length of the messages or completion. \nHuman Feedback: GENERATE NEXT COMMAND JSON ']
User: Determine which next command to use, and respond using the format specified above:
Assistant: {
"thoughts": {
"text": "Let's start by browsing the article at https://v3u.cn/a_id_303 to get a better understanding of its contents.",
"reasoning": "Browsing the article will allow us to analyze its contents and determine the appropriate next steps.",
"plan": "- Browse the article\n- Analyze its contents\n- Determine the appropriate next steps",
"criticism": "None",
"speak": "I suggest we start by browsing the article at the given URL to analyze its contents and determine the appropriate next steps."
},
"command": {
"name": "browse_website",
"args": {
"url": "https://v3u.cn/a_id_303",
"question": "analyze the contents of the article"
}
}
}
User: Determine which next command to use, and respond using the format specified above:
----------- END OF CONTEXT ----------------
Creating chat completion with model gpt-3.5-turbo, temperature 0.0, max_tokens 2528
最后將分析結果寫入goal.txt檔案：
這篇文章主要闡釋了蘋果Mac電腦可以完成機器學習和深度學習任務，并且通過深度學習框架Tensorflow的安裝和運行進行了佐證，同時也對Tensorflow的CPU和GPU的兩種模型訓練模式進行了深度對比和測驗，
一氣呵成，流暢絲滑，
結語
AutoGPT和其他 AI 程式的不同之處在于，它專門專注于在無需人工干預的情況下生成提示和自動執行多步驟任務，它還具有掃描互聯網或在用戶計算機上執行命令以獲取資訊的能力，這使其有別于可能僅依賴于預先存在的資料集的其他人工智能程式，
AutoGPT的底層邏輯并不復雜：先通過搜索引擎檢索任務，然后把結果和目標丟給gpt讓它給出序列化方案json，再把方案分段丟給gpt，最后用shell去創建Python檔案+json.load并且執行，是一個反復遞回的程序，
不能否認的是，雖然實作邏輯簡單，但這無疑是一種“自我進化”的程序，相信隨著時間的推移，AutoGPT可以更好地處理愈加復雜的任務，
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