API接口重試的8種方法
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from minimax import Client, TextParams
client = Client(
api_key="your_api_key_here",
group_id="default", # 團隊協作標識
endpoint="https://api.minimax.cn/v1"
)response = client.text_completion(
prompt="寫一篇關于新能源汽車的營銷文案，要求包含三個核心賣點",
params=TextParams(
temperature=0.7, # 創意度調節（0-1）
max_tokens=500, # 最大輸出長度
repetition_penalty=1.2, # 重復懲罰系數
style="marketing" # 預設風格模板
)
)
print(response.text)典型輸出結構：
{
"text": "全新一代XX電動車，重新定義出行體驗...",
"usage": {
"prompt_tokens": 32,
"completion_tokens": 287
},
"suggestions": ["可加入電池壽命數據","建議強化安全性能描述"]
}client.text_completion(
prompt="基于以下數據生成季度財報分析：{數據}",
params=TextParams(
template="financial_report", # 使用預置模板
language="zh-en", # 中英混合輸出
sentiment="positive", # 情感傾向控制
forbidden_words=["虧損","下滑"] # 屏蔽敏感詞
)
)stream = client.text_completion_stream(
prompt="用Python實現快速排序算法",
stream=True
)
for chunk in stream:
print(chunk.text, end="", flush=True)
if chunk.is_final:
print("\n[生成完成]")response = client.code_completion(
context="def calculate_average(nums):",
language="python",
cursor_position=25 # 光標位置
)
print(response.suggestions[0].code)輸出示例：
if not nums:
return 0
return sum(nums) / len(nums)report = client.code_review(
code="public class Test { ... }",
language="java",
check_rules=["performance", "security"]
)
for issue in report.issues:
print(f"Line {issue.line}: {issue.description}")
print(f"建議修改: {issue.suggestion}")tests = client.generate_unit_test(
source_code="math_utils.py",
framework="pytest",
coverage=0.85 # 目標覆蓋率
)
with open("test_math_utils.py", "w") as f:
f.write(tests.code)image_url = client.generate_image(
prompt="賽博朋克風格的城市夜景，有飛行汽車和霓虹廣告牌",
resolution="1024x768",
style="digital_art"
)
print(f"生成圖像地址: {image_url}")analysis = client.analyze_video(
video_url="https://example.com/demo.mp4",
features=["caption", "object", "emotion"]
)
print(f"視頻摘要: {analysis.summary}")
print("關鍵物體檢測:")
for obj in analysis.objects[:3]:
print(f"- {obj.name} (置信度:{obj.confidence:.2f})")results = client.cross_modal_search(
query_image="product.jpg",
target_type="text",
top_k=5
)
for item in results:
print(f"相似度 {item.score:.2f}: {item.text}")from minimax import BatchProcessor
processor = BatchProcessor(
client=client,
concurrency=10 # 最大并發數
)
tasks = [
{"prompt": "生成產品A的廣告語"},
{"prompt": "翻譯用戶手冊到英文"}
]
results = processor.process_batch(
tasks,
endpoint="text_completion"
)
for result in results:
if result.success:
print(result.response.text)training_job = client.start_fine_tuning(
base_model="hailuo-base",
training_data="dataset.jsonl",
params={
"epochs": 5,
"learning_rate": 2e-5
}
)
while not training_job.is_complete():
print(f"訓練進度: {training_job.progress}%")
time.sleep(60)
print(f"新模型ID: {training_job.model_id}")from minimax import RBAC
rbac = RBAC(client)
rbac.create_role(
name="junior_developer",
permissions=["text_completion", "code_completion"]
)
rbac.assign_user(
user_id="user_123",
role="junior_developer"
)from functools import lru_cache
@lru_cache(maxsize=1000)
def cached_completion(prompt: str, params: dict):
return client.text_completion(prompt, params=params)from minimax import LoadBalancer
lb = LoadBalancer(
api_keys=["key1", "key2", "key3"],
strategy="round_robin" # 可選least_conn/latency
)
response = lb.request(
endpoint="text_completion",
payload={"prompt": "..."}
)from minimax import Monitor
monitor = Monitor(client)
monitor.set_alert(
metric="error_rate",
condition=">5%",
receivers=["alert@company.com"]
)
monitor.start_dashboard(port=8080)safe_response = client.text_completion(
prompt="...",
safety_filter={
"categories": ["violence", "porn"],
"action": "block" # 可選censor/replace
}
)from minimax import SecureClient
secure_client = SecureClient(
api_key="your_key",
encryption_key="your_encrypt_key",
algorithm="AES-256-GCM"
)audit_logs = client.get_audit_logs(
start_time="2023-01-01",
end_time="2023-12-31",
event_types=["api_call", "model_training"]
)
export_to_s3(audit_logs, "s3://bucket/audit.log")class CustomerServiceBot:
def __init__(self):
self.knowledge_base = client.load_kb("faq_db")
def respond(self, query):
context = self.knowledge_base.search(query)
return client.text_completion(
prompt=f"用戶問：{query}\n參考信息：{context}",
params={"temperature": 0.3}
)def auto_post_social_media():
trends = client.analyze_trends(keywords=["科技", "創新"])
article = client.text_completion(
prompt=f"根據趨勢{trends}撰寫公眾號文章"
)
image = client.generate_image(article.summary)
post_to_wechat(article, image)report = client.analyze_data(
dataset="sales.csv",
task="預測下季度銷售額",
format="markdown"
)
visualization = client.generate_plot(
data=report["data"],
chart_type="time_series"
)| 錯誤碼 | 含義 | 解決方案 |
| 429 | 請求頻率超限 | 啟用指數退避重試機制 |
| 500 | 服務端內部錯誤 | 檢查請求格式，聯系技術支持 |
| 403 | 權限不足 | 驗證API Key和角色權限 |
| 400 | 參數校驗失敗 | 使用SDK參數驗證工具 |
MiniMax Hailuo AI通過其強大的多模態能力和靈活的開發接口，正在重塑企業智能化轉型的技術路徑。本教程從基礎配置到高階應用，系統性地展示了平臺的核心功能與技術特性。隨著v3.0版本即將推出的強化推理引擎和行業垂直模型，開發者將獲得更強大的工具支持。但需要始終牢記：AI技術的價值在于增強而非替代人類智能，開發者應秉持負責任的態度，在追求效率提升的同時，確保技術的應用符合倫理規范和社會價值。
