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LangExtract is Google's official open-source Python library designed for extracting structured data (JSON, Pydantic objects) from text, PDFs, and invoices. Unlike standard prompt engineering, it's built for enterprise-grade extraction with three core advantages: precise grounding (mapping fields to source coordinates), schema enforcement (ensuring output matches Pydantic definitions), and model agnosticism (compatible with Gemini, DeepSeek, OpenAI, and LlamaIndex). This guide provides practical insights for Chinese developers on local configuration, cost optimization, and handling long documents. LangExtract是Google官方开源的Python库，专为从文本、PDF和发票中提取结构化数据（JSON、Pydantic对象）而设计。与普通Prompt工程不同，它为企业级数据提取打造，具备三大核心优势：精准溯源（字段可映射回原文坐标）、Schema强约束（保证输出符合数据结构）、模型无关性（兼容Gemini、DeepSeek、OpenAI及LlamaIndex）。本指南基于真实项目经验，涵盖国内环境配置、API成本优化和长文档处理技巧。

Search engine product updates significantly impact SEO strategies, requiring technical professionals to adapt through continuous monitoring, algorithm understanding, and user-centric optimization. (搜索引擎产品更新对SEO策略产生重大影响，要求技术专业人员通过持续监控、算法理解和以用户为中心的优化来适应变化。)

This article provides a comprehensive learning roadmap and curated resources for beginners in data science, covering foundational concepts, essential tools, and practical applications. (本文为数据科学初学者提供了全面的学习路线图和精选资源，涵盖基础概念、核心工具和实际应用。)

Grok-1 is an open-source large language model developed by xAI, featuring 314 billion parameters and a Mixture-of-Experts architecture. It demonstrates strong performance in reasoning, coding, and multilingual tasks while maintaining full transparency through open weights and documentation. (Grok-1是由xAI开发的开源大语言模型，拥有3140亿参数和专家混合架构。该模型在推理、编程和多语言任务中表现出色，并通过开放的权重和文档保持完全透明。)

This comprehensive SEO guide covers the Mozlow hierarchy framework with 7 essential steps: crawl accessibility, compelling content, keyword optimization, user experience, share-worthy content, title/URL optimization, and schema markup. It provides structured learning from basics to advanced techniques with practical certification options. (本综合SEO指南涵盖Mozlow层次框架的7个关键步骤：爬虫可访问性、引人入胜的内容、关键词优化、用户体验、值得分享的内容、标题/URL优化和结构化数据标记。提供从基础到高级技术的结构化学习路径及实用认证选项。)

Schema标记是网站结构化数据的核心技术，通过为网页内容添加语义标签，帮助搜索引擎更精确理解页面信息，从而提供更丰富的搜索结果展示，显著提升SEO效果和用户体验。

Schema标记是结构化数据的核心技术，通过为网页内容添加语义标签，帮助搜索引擎更准确地理解内容含义，从而提升搜索结果显示效果、点击率和用户体验。本文详细解析了Schema标记的定义、类型、实施方法及其对SEO的重要影响。

本文以德国刑法“侵占罪”为例，展示如何运用Schema标记的结构化思维解析复杂法律概念。通过分解客观要件、主观要件等模块，清晰呈现法律条文的内在逻辑，为技术文档编写和知识管理提供方法论参考。

Schema标记是提升搜索结果点击率的关键技术，通过为搜索引擎提供结构化数据，使您的网站在搜索结果中获得视觉增强，从而在竞争中脱颖而出。本文详细解析了Schema标记的原理、实施方法和最佳实践。