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GEO (Generative Engine Optimization) is an evolution beyond traditional SEO and AEO, focusing on optimizing content to appear directly within AI-generated answers like Google AI Overviews and LLM responses. It emphasizes visibility in zero-click search environments by ensuring brands are referenced and trusted by generative systems. (GEO（生成引擎优化）是超越传统SEO和AEO的演进，专注于优化内容以直接出现在AI生成的答案中，如Google AI概览和LLM响应。它通过确保品牌被生成系统引用和信任，强调在零点击搜索环境中的可见性。)

LLMs.txt is a proposed web standard designed to help large language models (LLMs) better understand and utilize website content by providing a structured, curated list of important pages in Markdown format. It aims to address challenges AI crawlers face with modern websites, such as JavaScript-loaded content and information overload, potentially improving AI-generated responses and reducing training inefficiencies. (LLMs.txt是一项拟议的网络标准，旨在通过以Markdown格式提供结构化、精选的重要页面列表，帮助大型语言模型（LLMs）更好地理解和利用网站内容。它旨在解决AI爬虫在现代网站中面临的挑战，如JavaScript加载内容和信息过载，可能改善AI生成的响应并减少训练低效。)

LangExtract is a Python library that leverages large language models (LLMs) to extract structured information from unstructured text documents, featuring precise source mapping, customizable extraction schemas, and support for multiple model providers. (LangExtract 是一个 Python 库，利用大语言模型从非结构化文本文档中提取结构化信息，具备精确的源文本映射、可定制的提取模式以及多模型提供商支持。)

LangExtract is Google's open-source programmatic extraction tool that transforms unstructured text into structured, traceable data with character-level offsets. It enables efficient long-document processing, multi-round extraction for recall, and direct structured output, reducing traditional RAG overhead. This guide demonstrates building a knowledge graph chatbot using Streamlit, Agraph, and LangExtract with dynamic few-shot template selection. LangExtract是Google开源的程序化抽取工具，可将非结构化文本转化为可追溯的结构化数据，通过字符偏移实现高亮验证。它支持长文档分块并行处理、多轮抽取保证召回率，并直接生成结构化结果，减少传统RAG流程开销。本文演示了使用Streamlit、Agraph和LangExtract构建知识图谱聊天机器人，实现动态few-shot模板选择和实体关系并行抽取。

Generative Engine Optimization (GEO) is an emerging field focused on enhancing information visibility and citation rates within generative AI models like large language models. As AI-powered search and recommendation become prevalent, GEO strategies aim to adapt digital information assets to be more effectively retrieved, trusted, and utilized by AI systems, moving beyond traditional SEO to address new information interaction paradigms. (生成式引擎优化（GEO）是一个新兴领域，专注于提升信息在生成式AI模型（如大型语言模型）中的可见度与引用率。随着AI搜索推荐日益普及，GEO策略旨在使数字信息资产更符合AI的生成逻辑，更易于被检索和信任，从而适应新的信息交互模式，超越了传统搜索引擎优化的范畴。)

Tencent Cloud BI provides end-to-end business intelligence capabilities from data source integration to visualization, featuring ChatBI—an AI-powered analytics agent that enables conversational data analysis, interpretation, and optimization recommendations. (腾讯云BI提供从数据源接入到可视化分析的全流程BI能力，其智能助手ChatBI基于大模型实现对话式数据分析，并提供数据解读与业务优化建议。)

GEO (Generative Engine Optimization) is the practice of optimizing content to be understood, referenced, and recommended by AI systems, making it part of AI-generated answers. This represents a paradigm shift from traditional SEO's 'click-through' focus to a new 'citation' model in the AI era. (GEO（生成式引擎优化）旨在优化内容，使其被AI系统理解、引用和推荐，成为AI生成答案的一部分。这标志着从传统SEO的“点击”思维向AI时代“引用”思维的模式转变。)

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。