results = extractor.extract(contract_text, schema)
当然，就我个人而言，微信社交能多一些AI辅助功能也不错，我是个社交喜欢用表情包的人，如果聊天时随时生成符合当下语境的表情包，而不是从那些固有的弹出几个，就太棒了。，更多细节参见WPS官方版本下载
，这一点在谷歌浏览器【最新下载地址】中也有详细论述
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Scenario generation + real conversation import - Our scenario generation agent bootstraps your test suite from a description of your agent. But real users find paths no generator anticipates, so we also ingest your production conversations and automatically extract test cases from them. Your coverage evolves as your users do.Mock tool platform - Agents call tools. Running simulations against real APIs is slow and flaky. Our mock tool platform lets you define tool schemas, behavior, and return values so simulations exercise tool selection and decision-making without touching production systems.Deterministic, structured test cases - LLMs are stochastic. A CI test that passes "most of the time" is useless. Rather than free-form prompts, our evaluators are defined as structured conditional action trees: explicit conditions that trigger specific responses, with support for fixed messages when word-for-word precision matters. This means the synthetic user behaves consistently across runs - same branching logic, same inputs - so a failure is a real regression, not noise.Cekura also monitors your live agent traffic. The obvious alternative here is a tracing platform like Langfuse or LangSmith - and they're great tools for debugging individual LLM calls. But conversational agents have a different failure mode: the bug isn't in any single turn, it's in how turns relate to each other. Take a verification flow that requires name, date of birth, and phone number before proceeding - if the agent skips asking for DOB and moves on anyway, every individual turn looks fine in isolation. The failure only becomes visible when you evaluate the full session as a unit. Cekura is built around this from the ground up.，推荐阅读PDF资料获取更多信息
触控能力将推动 macOS 引入全新的动态界面：系统会根据用户的操作方式在界面更大的触控模式与传统鼠标点按模式之间切换。系统还将支持与 iPhone、iPad 类似的快速滚动与双指缩放。