OpenAI API 學習筆記 : 使用 openai 套件串接 GPT 語言模型 (一)

昨天維元老師的 ChatGPT 實戰內訓課程講到 OpenAI API 串接實作, 我因在前一篇測試中已經註冊 OpenAI 帳戶並建立了存取 GPT 模型的金鑰, 所以就能在課堂上邊聽邊在 Colab 上測試, 本篇記錄利用 openai 這個 Python 套件與模型的串接與對話.

本系列之前的文章參考 :

# OpenAI API 學習筆記 : 註冊 OpenAI 帳戶與建立 API 金鑰

要串接 OpenAI API 必須先取得金鑰才能進行本篇測試. 

一. 安裝 openai 套件 : 

串接 OpenAI API 必須先安裝 openai 套件 : 

pip install openai   

如果是在 Colab 則指令前面要冠驚嘆號 :

!pip install openai   

D:\python\test>pip install openai    

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先用 import openai 匯入套件, 然後用 dir() 檢視套件內容 : 

>>> import openai   

>>> print(openai.__version__)  

1.14.0

>>> dir(openai)     

['APIConnectionError', 'APIError', 'APIResponse', 'APIResponseValidationError', 'APIStatusError', 'APITimeoutError', 'AssistantEventHandler', 'AsyncAPIResponse', 'AsyncAssistantEventHandler', 'AsyncAzureOpenAI', 'AsyncClient', 'AsyncOpenAI', 'AsyncStream', 'Audio', 'AuthenticationError', 'AzureOpenAI', 'BadRequestError', 'BaseModel', 'ChatCompletion', 'Client', 'Completion', 'ConflictError', 'Customer', 'DEFAULT_MAX_RETRIES', 'DEFAULT_TIMEOUT', 'Deployment', 'Edit', 'Embedding', 'Engine', 'ErrorObject', 'File', 'FineTune', 'FineTuningJob', 'Image', 'InternalServerError', 'Model', 'Moderation', 'NOT_GIVEN', 'NoneType', 'NotFoundError', 'NotGiven', 'OpenAI', 'OpenAIError', 'PermissionDeniedError', 'ProxiesTypes', 'RateLimitError', 'RequestOptions', 'Stream', 'Timeout', 'Transport', 'UnprocessableEntityError', 'VERSION', '_AmbiguousModuleClientUsageError', '_ApiType', '_AzureModuleClient', '_ModuleClient', '__all__', '__annotations__', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__locals', '__name', '__name__', '__package__', '__path__', '__spec__', '__title__', '__version__', '_azure', '_base_client', '_client', '_compat', '_constants', '_exceptions', '_extras', '_files', '_has_azure_ad_credentials', '_has_azure_credentials', '_has_openai_credentials', '_httpx', '_legacy_response', '_load_client', '_models', '_module_client', '_os', '_qs', '_reset_client', '_resource', '_response', '_setup_logging', '_streaming', '_t', '_te', '_types', '_utils', '_version', 'annotations', 'api_key', 'api_type', 'api_version', 'audio', 'azure_ad_token', 'azure_ad_token_provider', 'azure_endpoint', 'base_url', 'beta', 'chat', 'completions', 'default_headers', 'default_query', 'embeddings', 'file_from_path', 'files', 'fine_tuning', 'http_client', 'images', 'lib', 'max_retries', 'models', 'moderations', 'organization', 'override', 'pagination', 'resources', 'timeout', 'types', 'version']

用 dir() 看不出哪些是類別, 函式, 與模組, 這可用下列自訂模組 members 來達成 :

# members.py

import inspect 

def varname(x): 

    return [k for k,v in inspect.currentframe().f_back.f_locals.items() if v is x][0]

def list_members(parent_obj):

    members=dir(parent_obj)

    parent_obj_name=varname(parent_obj)       

    for mbr in members:

        child_obj=eval(parent_obj_name + '.' + mbr) 

        if not mbr.startswith('_'):

            print(mbr, type(child_obj))

參考 :

# Python 學習筆記 : 檢視物件成員與取得變數名稱字串的方法

將此函式存成 members.py 模組, 放在目前供作目錄下, 然後匯入其 list_members() 函式來檢視 openai 套件 : 

>>> from members import list_members     

>>> list_members(openai)    

APIConnectionError <class 'type'>

APIError <class 'type'>

APIResponse <class 'type'>

APIResponseValidationError <class 'type'>

APIStatusError <class 'type'>

APITimeoutError <class 'type'>

AssistantEventHandler <class 'type'>

AsyncAPIResponse <class 'type'>

AsyncAssistantEventHandler <class 'type'>

AsyncAzureOpenAI <class 'type'>

AsyncClient <class 'type'>

AsyncOpenAI <class 'type'>

AsyncStream <class 'type'>

Audio <class 'openai.lib._old_api.APIRemovedInV1Proxy'>

AuthenticationError <class 'type'>

AzureOpenAI <class 'type'>

BadRequestError <class 'type'>

BaseModel <class 'pydantic._internal._model_construction.ModelMetaclass'>

ChatCompletion <class 'openai.lib._old_api.APIRemovedInV1Proxy'>

Client <class 'type'>

Completion <class 'openai.lib._old_api.APIRemovedInV1Proxy'>

ConflictError <class 'type'>

Customer <class 'openai.lib._old_api.APIRemovedInV1Proxy'>

DEFAULT_MAX_RETRIES <class 'int'>

DEFAULT_TIMEOUT <class 'openai.Timeout'>

Deployment <class 'openai.lib._old_api.APIRemovedInV1Proxy'>

Edit <class 'openai.lib._old_api.APIRemovedInV1Proxy'>

Embedding <class 'openai.lib._old_api.APIRemovedInV1Proxy'>

Engine <class 'openai.lib._old_api.APIRemovedInV1Proxy'>

ErrorObject <class 'openai.lib._old_api.APIRemovedInV1Proxy'>

File <class 'openai.lib._old_api.APIRemovedInV1Proxy'>

FineTune <class 'openai.lib._old_api.APIRemovedInV1Proxy'>

FineTuningJob <class 'openai.lib._old_api.APIRemovedInV1Proxy'>

Image <class 'openai.lib._old_api.APIRemovedInV1Proxy'>

InternalServerError <class 'type'>

Model <class 'openai.lib._old_api.APIRemovedInV1Proxy'>

Moderation <class 'openai.lib._old_api.APIRemovedInV1Proxy'>

NOT_GIVEN <class 'openai.NotGiven'>

NoneType <class 'type'>

NotFoundError <class 'type'>

NotGiven <class 'type'>

OpenAI <class 'type'>

OpenAIError <class 'type'>

PermissionDeniedError <class 'type'>

ProxiesTypes <class 'typing._UnionGenericAlias'>

RateLimitError <class 'type'>

RequestOptions <class 'typing_extensions._TypedDictMeta'>

Stream <class 'type'>

Timeout <class 'type'>

Transport <class 'type'>

UnprocessableEntityError <class 'type'>

VERSION <class 'str'>

annotations <class '__future__._Feature'>

api_key <class 'NoneType'>

api_type <class 'NoneType'>

api_version <class 'NoneType'>

audio <class 'openai._module_client.AudioProxy'>

azure_ad_token <class 'NoneType'>

azure_ad_token_provider <class 'NoneType'>

azure_endpoint <class 'NoneType'>

base_url <class 'NoneType'>

beta <class 'openai._module_client.BetaProxy'>

chat <class 'openai._module_client.ChatProxy'>

completions <class 'openai._module_client.CompletionsProxy'>

default_headers <class 'NoneType'>

default_query <class 'NoneType'>

embeddings <class 'openai._module_client.EmbeddingsProxy'>

file_from_path <class 'function'>

files <class 'openai._module_client.FilesProxy'>

fine_tuning <class 'openai._module_client.FineTuningProxy'>

http_client <class 'NoneType'>

images <class 'openai._module_client.ImagesProxy'>

lib <class 'module'>

max_retries <class 'int'>

models <class 'openai._module_client.ModelsProxy'>

moderations <class 'openai._module_client.ModerationsProxy'>

organization <class 'NoneType'>

override <class 'function'>

pagination <class 'module'>

resources <class 'module'>

timeout <class 'openai.Timeout'>

types <class 'module'>

version <class 'module'>

可見 openai 套件有非常多成員, 但串接 GPT 大模型只會用到 OpenAI 這個類別. 

二. 串接 OpenAI API : 

串接 API 的程式語法是很制式的, 首先從 openai 套件中匯入 OpenAI 類別 :

>>> from openai import OpenAI   

接著定義一個字串變數儲存金鑰, 例如下列範例 (非真實金鑰) : 

>>> api_key='sk-tonyIqevbZyyp3eWNr1966BlbkFJpo5ls6CxBcwvSyHslay2'  

然後呼叫 OpenAI 類別的建構函式 OpenAI() 並將金鑰傳給 api_key 參數, 它會傳回一個 OpenAI 物件 : 

>>> client=OpenAI(api_key=api_key)     

>>> type(client)    

<class 'openai.OpenAI'>    

最後呼叫此 OpenAI 物件的 chat.completions.create() 方法, 並傳入 messages (詢問訊息) 與 model (語言模型) 這兩個參數, 它會傳回一個 ChatCompletion 物件 (即 GPT 生成之回應) :

>>> chat_completion=client.chat.completions.create(

    messages=[

        {"role": "user",

         "content": "請說一個好笑的笑話",

        }],

    model="gpt-3.5-turbo",

    )

>>> type(chat_completion)   

<class 'openai.types.chat.chat_completion.ChatCompletion'>

參數 message 是一個字典串列, 其中 content 屬性就是我們提出的詢問, 因為我使用的是新註冊者的免費帳戶, 所以只能使用 GTP 3.5 模型, 無法使用 GPT 4. 

關於 chat.completions.create() 方法的參數用法參考 OpenAI API 說明 :

# API Reference - OpenAI API

回應物件 ChatCompletion 的內容如下 :

>>> print(chat_completion)   

ChatCompletion(id='chatcmpl-92b7YN2M39rI6XvX8eyKlxuTU2SzK', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='為什麼警察不玩撲克牌？因為他們怕抓到一隻大老鼠！哈哈哈哈哈！', role='assistant', function_call=None, tool_calls=None))], created=1710406376, model='gpt-3.5-turbo-0125', object='chat.completion', system_fingerprint='fp_4f2ebda25a', usage=CompletionUsage(completion_tokens=49, prompt_tokens=20, total_tokens=69))

print() 的輸出是擠成一團的字串, 可讀性差, 可以用第三方套件 rich 的 print() 來輸出可讀性高的物件內容, 關於 rich 參考 :

# Python 學習筆記 : 豐富終端機視覺效果的 rich 套件

>>> import rich 

>>> rich.print(chat_completion)  

ChatCompletion(

    id='chatcmpl-92b7YN2M39rI6XvX8eyKlxuTU2SzK',

    choices=[

        Choice(

            finish_reason='stop',

            index=0,

            logprobs=None,

            message=ChatCompletionMessage(

                content='為什麼警察不玩撲克牌？因為他們怕抓到一隻大老鼠！

哈哈哈哈哈！',

                role='assistant',

                function_call=None,

                tool_calls=None

            )

        )

    ],

    created=1710406376,

    model='gpt-3.5-turbo-0125',

    object='chat.completion',

    system_fingerprint='fp_4f2ebda25a',

    usage=CompletionUsage(

        completion_tokens=49,

        prompt_tokens=20,

        total_tokens=69

    )

) 

觀察 ChatCompletion 物件結構, 可知 GPT 的回應內容放在 choices[0].message.content 屬性裡 :

>>> response_message=chat_completion.choices[0].message.content   

>>> print(response_message)    

為什麼警察不玩撲克牌？因為他們怕抓到一隻大老鼠！哈哈哈哈哈！

完整程式碼如下 :

from openai import OpenAI

api_key='sk-tonyIqevbZyyp3eWNr1966BlbkFJpo5ls6CxBcwvSyHslay2'

client=OpenAI(api_key=api_key)

chat_completion=client.chat.completions.create(

    messages=[

        {"role": "user",

         "content": "請說一個好笑的笑話",

        }],

    model="gpt-3.5-turbo",

    )

print(chat_completion)

response_message=chat_completion.choices[0].message.content

print(response_message)

關於 OpenAI API 的 Python 語法參考官網教學文件 : 

# https://github.com/openai/openai-python

三. 將金鑰儲存在環境變數中 : 

上面直接將金鑰寫在程式中的作法並不妥當, 因為可能在分享時不經意地洩漏出去, 比較好的做法是將金鑰存在環境變數中, 參考 :

# Python 學習筆記 : 如何隱藏 API 金鑰或權杖

首先在目前工作目錄下建立一個檔名為 .env 的純文字隱藏檔, 用 = 把金鑰賦予一個變數名稱, 例如 OPENAI_KEY (注意, 金鑰不可用括號括起來) :

OPENAI_KEY=sk-tonyIqevbZyyp3eWNr1966BlbkFJpo5ls6CxBcwvSyHslay2 

然後安裝第三方套件 python-decouple 或 python-dotenv, 此處以 python-dotenv 為例 :

pip install python-dotenv   

這樣就可以匯入 dotenv 模組的 load_dotenv() 函式來載入環境變數檔 .env, 並配合使用 os.getenv() 來取得環境變數中的指定金鑰了 : 

>>> from dotenv import load_dotenv     

>>> import os   

>>> load_dotenv()   

True

>>> api_key=os.environ.get('OPENAI_API')   

>>> print(api_key)  

sk-tonyIqevbZyyp3eWNr1966BlbkFJpo5ls6CxBcwvSyHslay2    

>>> client=OpenAI(api_key=api_key)     

>>> chat_completion=client.chat.completions.create(

    messages=[

        {"role": "user",

         "content": "請說一個好笑的笑話",

        }],

    model="gpt-3.5-turbo",

    )

>>> response_message=chat_completion.choices[0].message.content   

>>> print(response_message)    

為什麼警察不玩撲克牌？因為他們怕抓到一隻大老鼠！哈哈哈哈哈！

完整程式碼如下 : 

import os

from dotenv import load_dotenv

from openai import OpenAI

load_dotenv()

api_key=os.environ.get('OPENAI_API')

client=OpenAI(api_key=api_key)

chat_completion=client.chat.completions.create(

    messages=[

        {"role": "user",

         "content": "請說一個好笑的笑話",

        }],

    model="gpt-3.5-turbo",

    )

print(chat_completion)

response_message=chat_completion.choices[0].message.content

print(response_message)

這樣就可避免在程式中直接揭露金鑰的問題了.