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LangExtract is a Python library that leverages Large Language Models (LLMs) to extract structured information from unstructured text documents through user-defined instructions and few-shot examples. It features precise source grounding, reliable structured outputs, optimized long document processing, interactive visualization, and flexible LLM support across cloud and local models. LangExtract adapts to various domains without requiring model fine-tuning, making it suitable for applications ranging from literary analysis to clinical data extraction. LangExtract是一个基于大型语言模型（LLM）的Python库，通过用户定义的指令和少量示例从非结构化文本中提取结构化信息。它具有精确的源文本定位、可靠的结构化输出、优化的长文档处理、交互式可视化以及灵活的LLM支持（涵盖云端和本地模型）。LangExtract无需模型微调即可适应不同领域，适用于从文学分析到临床数据提取等多种应用场景。

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。

LangExtract is a Python library powered by large language models (like Gemini) that extracts structured information from unstructured text with precise source localization and interactive visualization capabilities. It offers reliable structured output, long-document optimization, domain adaptability, and is open-source under Apache 2.0 license. (LangExtract是一个基于大语言模型（如Gemini）的Python库，能够从非结构化文本中提取结构化信息，具备精确的源定位和交互式可视化功能。它提供可靠的结构化输出、长文档优化、领域适应性，并在Apache 2.0许可证下开源。)

GEO (Generative Engine Optimization) is an AI-era optimization strategy that enhances content visibility in generative search engines by aligning with real-time user queries and geographic targeting, differing fundamentally from traditional SEO and 'guess-what-you-like' recommendation systems. (GEO（生成式引擎优化）是AI时代的优化策略，通过匹配用户实时查询和地理定位，提升内容在生成式搜索引擎中的可见性，与传统SEO和“猜你喜欢”推荐系统有本质区别。)

POI (Point of Interest) data, representing specific locations like buildings or bus stops, is crucial for geographic information systems. Traditional collection methods are time-consuming, but comprehensive POI data enhances navigation, market analysis, and customer insights. Integrating POI with generative engines can automate data processing and unlock new applications in location-based services. (POI（兴趣点）数据，代表建筑物或公交站等特定位置，对地理信息系统至关重要。传统采集方法耗时，但全面的POI数据能提升导航、市场分析和客户洞察。将POI与生成式引擎集成可自动化数据处理，在基于位置的服务中开启新应用。)

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Reinforcement Learning from Human Feedback (RLHF) is a machine learning technique that optimizes AI agent performance by training a reward model using direct human feedback. It is particularly effective for tasks with complex, ill-defined, or difficult-to-specify objectives, such as improving the relevance, accuracy, and ethics of large language models (LLMs) in chatbot applications. RLHF typically involves four phases: pre-training model, supervised fine-tuning, reward model training, and policy optimization, with proximal policy optimization (PPO) being a key algorithm. While RLHF has demonstrated remarkable results in training AI agents for complex tasks from robotics to NLP, it faces limitations including the high cost of human preference data, the subjectivity of human opinions, and risks of overfitting and bias. (RLHF（基于人类反馈的强化学习）是一种机器学习技术，通过使用直接的人类反馈训练奖励模型来优化AI代理的性能。它特别适用于具有复杂、定义不明确或难以指定目标的任务，例如提高大型语言模型（LLM）在聊天机器人应用中的相关性、准确性和伦理性。RLHF通常包括四个阶段：预训练模型、监督微调、奖励模型训练和策略优化，其中近端策略优化（PPO）是关键算法。虽然RLHF在从机器人学到自然语言处理的复杂任务AI代理训练中取得了显著成果，但它面临一些限制，包括人类偏好数据的高成本、人类意见的主观性以及过拟合和偏见的风险。)

GEO (Generative Engine Optimization) is the strategic optimization of content to be understood, referenced, and recommended by AI, making it part of AI-generated answers. This represents a paradigm shift from traditional SEO's goal of ranking for clicks to GEO's goal of becoming the source material for AI responses, crucial for capturing traffic in the AI-driven search era. (中文摘要翻译) GEO（生成式引擎优化）是通过优化内容使其被AI理解、引用和推荐，成为AI生成答案一部分的策略。这代表了从传统SEO追求点击排名的目标，向GEO成为AI回答“原材料”目标的范式转移，对于在AI驱动的搜索时代获取流量至关重要。

GEO (Generative Engine Optimization) is an AI-era optimization strategy that helps brands appear in AI assistant responses (like Doubao, Wenxin Yiyan, DeepSeek) through content optimization and distribution, addressing traditional SEO limitations and capturing new conversational search traffic. (GEO（生成式引擎优化）是AI时代的优化策略，通过内容优化和分发，让品牌出现在豆包、文心一言、DeepSeek等AI助手的回答中，解决传统SEO困境，抢占对话式搜索流量新红利。)