> ## Documentation Index > Fetch the complete documentation index at: https://docs.tokenlab.sh/llms.txt > Use this file to discover all available pages before exploring further. # 最佳实践 > 优化你的 TokenLab API 使用，以提升成本、性能和可靠性 ## 模型选择 选择合适的模型会显著影响成本和质量。 ### 基于任务的推荐 | 任务 | 推荐模型 | 原因 | | --------- | ------------------------------------------------- | ------------ | | **简单问答** | `gpt-5-mini`, `gemini-3.5-flash` | 快速、便宜、足够好用 | | **复杂推理** | `gpt-5.4`, `claude-opus-4-6`, `deepseek-r1` | 更好的逻辑和规划能力 | | **编程** | `claude-sonnet-4-6`, `gpt-4o`, `deepseek-v3-2` | 针对代码进行了优化 | | **创意写作** | `claude-sonnet-4-6`, `gpt-4o` | 更好的文风质量 | | **视觉/图像** | `gpt-4o`, `claude-sonnet-4-6`, `gemini-3.5-flash` | 原生视觉支持 | | **长上下文** | `gemini-2.5-pro`, `claude-sonnet-4-6` | 1M+ token 窗口 | | **成本敏感** | `gpt-5-mini`, `gemini-3.5-flash`, `deepseek-v3-2` | 性价比最佳 | ### 成本层级 ``` $$$$ Premium: gpt-5.4, claude-opus-4-6 $$$ Standard: claude-sonnet-4-6, gpt-4o $$ Budget: gpt-5-mini, gemini-3.5-flash $ Economy: deepseek-v3-2, deepseek-r1 ``` ## 成本优化 ### 1. 优先使用更小的模型 ```python theme={null} def smart_query(question: str, complexity: str = "auto"): """Use cheaper models for simple tasks.""" if complexity == "simple": model = "gpt-5-mini" elif complexity == "complex": model = "gpt-4o" else: # Start cheap, escalate if needed model = "gpt-5-mini" response = client.chat.completions.create( model=model, messages=[{"role": "user", "content": question}] ) return response ``` ### 2. 设置 max\_tokens 始终设置合理的 `max_tokens` 限制： ```python theme={null} # ❌ Bad: No limit, could generate thousands of tokens response = client.chat.completions.create( model="gpt-4o", messages=[{"role": "user", "content": "Summarize this article"}] ) # ✅ Good: Limit response length response = client.chat.completions.create( model="gpt-4o", messages=[{"role": "user", "content": "Summarize this article"}], max_tokens=500 # Reasonable limit for a summary ) ``` ### 3. 优化 Prompt ```python theme={null} # ❌ Verbose prompt (more input tokens) prompt = """ I would like you to please help me by analyzing the following text and providing a comprehensive summary of the main points. Please be thorough but also concise in your response. The text is as follows: {text} """ # ✅ Concise prompt (fewer tokens) prompt = "Summarize the key points:\n{text}" ``` ### 4. 批量处理相似请求 ```python theme={null} # ❌ Many small requests for question in questions: response = client.chat.completions.create( model="gpt-4o", messages=[{"role": "user", "content": question}] ) # ✅ Fewer larger requests combined_prompt = "\n".join([f"{i+1}. {q}" for i, q in enumerate(questions)]) response = client.chat.completions.create( model="gpt-4o", messages=[{"role": "user", "content": f"Answer each question:\n{combined_prompt}"}] ) ``` ## 性能优化 ### 5. 为 UX 使用流式输出 流式输出可以改善感知性能： ```python theme={null} stream = client.chat.completions.create( model="gpt-4o", messages=[{"role": "user", "content": "Write a long essay"}], stream=True ) for chunk in stream: if chunk.choices[0].delta.content: print(chunk.choices[0].delta.content, end="", flush=True) ``` ### 6. 为交互式使用选择快速模型 | 使用场景 | 推荐 | 延迟 | | -------------- | -------------------------------- | --------------- | | Chat UI | `gpt-5-mini`, `gemini-3.5-flash` | \~200ms 首 token | | Tab completion | `claude-haiku-4-5` | \~150ms 首 token | | 后台处理 | `gpt-4o`, `claude-sonnet-4-6` | \~500ms 首 token | ### 7. 设置超时 ```python theme={null} client = OpenAI( api_key="sk-your-key", base_url="https://api.tokenlab.sh/v1", timeout=60.0 # 60 second timeout ) ``` ## 可靠性 ### 8. 实现重试机制 ```python theme={null} import time from openai import RateLimitError, APIError def chat_with_retry(messages, max_retries=3): for attempt in range(max_retries): try: return client.chat.completions.create( model="gpt-4o", messages=messages ) except RateLimitError: wait = 2 ** attempt print(f"Rate limited, waiting {wait}s...") time.sleep(wait) except APIError as e: if attempt == max_retries - 1: raise time.sleep(1) raise Exception("Max retries exceeded") ``` ### 9. 优雅地处理错误 ```python theme={null} from openai import APIError, AuthenticationError, RateLimitError try: response = client.chat.completions.create(...) except AuthenticationError: # Check API key notify_admin("Invalid API key") except RateLimitError: # Queue for later or use backup add_to_queue(request) except APIError as e: if e.status_code == 402: notify_admin("Balance low") elif e.status_code >= 500: # Server error, retry later schedule_retry(request) ``` ### 10. 使用回退模型 ```python theme={null} FALLBACK_CHAIN = ["gpt-4o", "claude-sonnet-4-6", "gemini-3.5-flash"] def chat_with_fallback(messages): for model in FALLBACK_CHAIN: try: return client.chat.completions.create( model=model, messages=messages ) except APIError: continue raise Exception("All models failed") ``` ## 安全性 ### 11. 保护 API Key ```python theme={null} # ❌ Never hardcode keys client = OpenAI(api_key="sk-abc123...") # ✅ Use environment variables import os client = OpenAI(api_key=os.environ["TOKENLAB_API_KEY"]) ``` ### 12. 验证用户输入 ```python theme={null} def validate_message(content: str) -> bool: """Validate user input before sending to API.""" if len(content) > 100000: raise ValueError("Message too long") # Add other validation as needed return True ``` ### 13. 设置 API Key 限额 为以下场景创建带有消费限额的独立 API Key： * 开发/测试 * 生产环境 * 不同应用程序 ## 监控 ### 14. 跟踪使用情况 定期检查你的仪表盘，关注： * 按模型统计的 token 使用量 * 成本明细 * 缓存命中率 * 错误率 ### 15. 记录重要指标 ```python theme={null} import logging response = client.chat.completions.create(...) logging.info({ "model": response.model, "prompt_tokens": response.usage.prompt_tokens, "completion_tokens": response.usage.completion_tokens, "total_tokens": response.usage.total_tokens, }) ``` ### 16. 设置告警 在你的仪表盘中配置低余额告警，以避免服务中断。 ## 检查清单 * [ ] 为每项任务使用合适的模型 * [ ] 设置 max\_tokens 限制 * [ ] Prompt 简洁 * [ ] 在适用场景启用缓存 * [ ] 批量处理相似请求 * [ ] 为交互式 UX 使用流式输出 * [ ] 为实时使用选择快速模型 * [ ] 已配置超时 * [ ] 已实现重试逻辑 * [ ] 已具备错误处理机制 * [ ] 已配置回退模型 * [ ] API Key 存储在环境变量中 * [ ] 输入验证 * [ ] 为 dev/prod 使用独立 Key * [ ] 已设置消费限额