Python迅速上手: 整合OpenAI API實戰教學; from openai import OpenAI, AzureOpenAI

by 儲蓄保險王 · 2025-08-20

在這篇教學中，我們將學習如何在Python中使用OpenAI API，包括標準版API和Azure OpenAI服務。這個教學適合已有基本Python知識的讀者。

建立環境
首先，安裝必要的套件:

pip install openai

pip install openai

2. 設定API金鑰

要使用OpenAI API，需要有API金鑰。我們可以從JSON檔案讀取金鑰:

import os
import json
from openai import OpenAI, AzureOpenAI

# 讀取API金鑰
dir_api_key = r"你的金鑰路徑"
basename_api_key = "api_key.json"
path_api_key = os.path.join(dir_api_key, basename_api_key)

with open(path_api_key, "r", encoding="utf-8") as f:
    api_key_list = json.load(f)

import os
import json
from openai import OpenAI, AzureOpenAI

# 讀取API金鑰
dir_api_key = r"你的金鑰路徑"
basename_api_key = "api_key.json"
path_api_key = os.path.join(dir_api_key, basename_api_key)

with open(path_api_key, "r", encoding="utf-8") as f:
    api_key_list = json.load(f)

AzureOpenAI除了api_key以外,
還需要端點網址,model不能替換:

Python迅速上手: 整合OpenAI API實戰教學; from openai import OpenAI, AzureOpenAI - 儲蓄保險王

配置客戶端
OpenAI提供兩種API服務: 標準版和Azure版。以下是如何設定:

# 配置選項
config = {"AzureOpenAI": True}  # True使用Azure版，False使用標準版

if config.get("AzureOpenAI") == True:
    # 使用Azure OpenAI
    api_key = api_key_list[0].get("api_key")
    model_name = api_key_list[0].get("model")  # 例如: 'gpt-4'
    endpoint = api_key_list[0].get("Target URI")  # Azure端點
    api_version = "2024-12-01-preview"
    
    client = AzureOpenAI(
        api_version=api_version, 
        api_key=api_key, 
        azure_endpoint=endpoint
    )
else:
    # 使用標準OpenAI API
    api_key = api_key_list[1].get("api_key")
    model_name = "gpt-4.1-nano"  # 可自選模型
    
    client = OpenAI(api_key=api_key)

# 配置選項
config = {"AzureOpenAI": True}  # True使用Azure版，False使用標準版

if config.get("AzureOpenAI") == True:
    # 使用Azure OpenAI
    api_key = api_key_list[0].get("api_key")
    model_name = api_key_list[0].get("model")  # 例如: 'gpt-4'
    endpoint = api_key_list[0].get("Target URI")  # Azure端點
    api_version = "2024-12-01-preview"
    
    client = AzureOpenAI(
        api_version=api_version, 
        api_key=api_key, 
        azure_endpoint=endpoint
    )
else:
    # 使用標準OpenAI API
    api_key = api_key_list[1].get("api_key")
    model_name = "gpt-4.1-nano"  # 可自選模型
    
    client = OpenAI(api_key=api_key)

AzureOpenAI比起標準openAI
除了需要api_key,
還多了api_version, azure_endpoint
AzureOpenAI?

4. 發送請求

現在可以向API發送請求:

response = client.chat.completions.create(
    model=model_name,
    messages=[
        {"role": "user", "content": "Hello, how are you?"}
    ],
    temperature=0.2,
    max_completion_tokens=500
)

# 獲取回應內容
answer = response.choices[0].message.content
print(answer)

response = client.chat.completions.create(
    model=model_name,
    messages=[
        {"role": "user", "content": "Hello, how are you?"}
    ],
    temperature=0.2,
    max_completion_tokens=500
)

# 獲取回應內容
answer = response.choices[0].message.content
print(answer)

client.chat.completions.create?

輸出結果:

5. 追蹤用量和成本

OpenAI API是按使用量收費的，我們可以追蹤花費:

# 獲取token使用量
input_tokens = response.usage.prompt_tokens
output_tokens = response.usage.completion_tokens

# 計算費用 (美元)
price_per_million_input = 0.10
price_per_million_output = 0.40

input_cost = (input_tokens / 10**6) * price_per_million_input
output_cost = (output_tokens / 10**6) * price_per_million_output
total_cost = input_cost + output_cost

print(f"總花費: ${total_cost:.8f}")
print(f"輸入tokens: {input_tokens}, 花費: ${input_cost:.8f}")
print(f"輸出tokens: {output_tokens}, 花費: ${output_cost:.8f}")

# 獲取token使用量
input_tokens = response.usage.prompt_tokens
output_tokens = response.usage.completion_tokens

# 計算費用 (美元)
price_per_million_input = 0.10
price_per_million_output = 0.40

input_cost = (input_tokens / 10**6) * price_per_million_input
output_cost = (output_tokens / 10**6) * price_per_million_output
total_cost = input_cost + output_cost

print(f"總花費: ${total_cost:.8f}")
print(f"輸入tokens: {input_tokens}, 花費: ${input_cost:.8f}")
print(f"輸出tokens: {output_tokens}, 花費: ${output_cost:.8f}")

輸出結果:

費用可以參考:
https://platform.openai.com/docs/pricing

    """# https://platform.openai.com/docs/pricing
    Model	      Input	Cached input	Output
    gpt-5	      $1.25	$0.125	        $10.00
    gpt-5-mini	$0.25	$0.025	        $2.00
    gpt-5-nano	$0.05	$0.005	        $0.40
    """

    """# https://platform.openai.com/docs/pricing
    Model	      Input	Cached input	Output
    gpt-5	      $1.25	$0.125	        $10.00
    gpt-5-mini	$0.25	$0.025	        $2.00
    gpt-5-nano	$0.05	$0.005	        $0.40
    """

批次處理多筆資料
處理多筆資料時，建議逐筆處理並保存結果:

import time

json_data = [{"問題1": "內容1"}, {"問題2": "內容2"}]  # 你的資料
results = []

for i, item in enumerate(json_data):
    # 發送請求
    response = client.chat.completions.create(
        model=model_name,
        messages=[
            {"role": "user", "content": f"請回答: {json.dumps(item, ensure_ascii=False)}"}
        ]
    )
    
    # 獲取回應
    result = item.copy()  # 複製原始資料
    result["回答"] = response.choices[0].message.content
    results.append(result)
    
    # 保存中間結果
    with open("results_temp.json", "w", encoding="utf-8") as f:
        json.dump(results, f, ensure_ascii=False, indent=4)
    
    print(f"已處理 {i+1}/{len(json_data)} 筆資料")
    
# 完成後重命名
os.rename("results_temp.json", "results_final.json")

import time

json_data = [{"問題1": "內容1"}, {"問題2": "內容2"}]  # 你的資料
results = []

for i, item in enumerate(json_data):
    # 發送請求
    response = client.chat.completions.create(
        model=model_name,
        messages=[
            {"role": "user", "content": f"請回答: {json.dumps(item, ensure_ascii=False)}"}
        ]
    )
    
    # 獲取回應
    result = item.copy()  # 複製原始資料
    result["回答"] = response.choices[0].message.content
    results.append(result)
    
    # 保存中間結果
    with open("results_temp.json", "w", encoding="utf-8") as f:
        json.dump(results, f, ensure_ascii=False, indent=4)
    
    print(f"已處理 {i+1}/{len(json_data)} 筆資料")
    
# 完成後重命名
os.rename("results_temp.json", "results_final.json")