instructor-structured-outputInstructor:声明 Pydantic BaseModel 即可从 20 个 LLM provider 拿到类型化实例。核心是 monkey-patch(instructor.patch / from_*)拦截 create(),注入 schema-aware kwargs,tenacity 重试 + In...
Install via ClawdBot CLI:
clawdbot install tangweigang-jpg/instructor-structured-outputGrade Limited — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Calls external URL not in known-safe list
https://doramagic.ai/zh/crystal/instructor-structured-outputAudited Apr 26, 2026 · audit v1.0
Generated May 12, 2026
从非结构化文本(如邮件、报告、聊天记录)中提取姓名、日期、金额等字段,直接输出Pydantic模型,省去手写解析逻辑。
将用户填写的自然语言表单内容(如保险申请、问卷调查)自动转换为结构化数据,并验证必填字段和格式。
作为中间层将LLM输出直接转化为预定义的API响应模型,确保下游消费者获得类型安全的数据,减少运行时错误。
使用同一个Pydantic schema从20种LLM provider获取一致的函数调用参数,无需为每个provider编写适配代码。
在关键数据抽取任务中自动重试并修正格式错误,将失败的输出作为XML注入提示词,提升最终输出质量。
企业客户按每月结构化输出请求数付费,定价分层(如5000次/月基础包),适合中大型公司。
免费层限1个provider和每日100次调用;付费解锁所有provider、自定义重试策略、优先级支持。
将Instructor核心以SDK形式授权给企业,允许嵌入自有产品,按年收取技术许可费。
💬 Integration Tip
在环境变量中设置LLM API密钥,并通过seed.yaml中的intent_router快速匹配用户意图;优先使用from_openai等高层API简化集成。
Scored May 12, 2026
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