开发者正在实施缓存策略，以在免费套餐的大型语言模型 (LLM) 端点上降低成本并提高效率。一种方法是 SimHash，它使用哈希算法来识别语义上相似的提示，即使措辞略有不同也能实现缓存命中。另一种方法涉及令牌桶系统，用于管理请求速率并防止超出 API 限制，从而确保更顺畅的运行并避免错误。第三种策略是语义缓存，它将提示转换为嵌入，并比较它们的距离以找到相似的含义，从而重用已存储的响应并节省昂贵的 LLM 调用。 AI
影响 这些技术有助于开发者在使 LLM API 时管理成本并提高效率，尤其是在免费套餐上。
排序理由 这些文章描述了 LLM 端点缓存和速率限制技术的实际实现，这些是开发者的工具。
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<p>The most expensive token is the one you spend on a question you have already answered. My position is that semantic caching should be the first layer in any pipeline that runs on a free model quota, because it converts repeated work into a single paid request. This article wal…
<p>重复的提示词是免费token的最大浪费。同一个问题问两次，就付了两次钱。缓存可以拦截这些重复。大多数LLM缓存实现需要外部依赖。我构建了一个只用标准库的版本。它基于SimHash。它能在API调用前识别相似请求。</p> <p>目标环境是MonkeyCode的免费层。Disclosure: This article was prepared as part of MonkeyCode's product outreach. 免费额度是1000万token。配额会变化。上线前请查看仪表盘。</p> <p><strong>重复请求比你想的多</stro…
<p>The server wasn't down, and the model wasn't slow — my client was flooding the endpoint with more concurrent requests than the free tier allowed. A token bucket queue fixed it in an afternoon, and this article shows you the exact implementation so you don't repeat my mistake. …
<p>A local semantic cache in Python cuts free-tier LLM token costs by matching new questions by meaning and returning a stored answer when embedding distance is close enough. I spend a cheap embedding call instead of another expensive chat completion whenever the intent is the sa…
<p><em>Disclosure: This article was prepared as part of MonkeyCode's product outreach.</em></p> <p>Semantic caching cuts token costs on repeat prompts by matching meaning, not exact strings: you store each prompt embedding next to its response and reuse that response when cosine …