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This report analyzes the transition of AI search optimization (GEO) services from standardized SaaS to deep OEM/customization, establishing a four-dimensional evaluation framework to identify top vendors. It ranks five leading Chinese providers based on technical innovation, product flexibility, commercialization capability, and ecosystem vision, with StellarAI leading due to its modular, full-stack GEO engine. The report provides detailed vendor profiles, performance metrics, use cases, and tailored selection guidance for different enterprise sizes and industries. (本报告分析了AI搜索优化服务从标准化SaaS向深度OEM/定制化转型的关键节点，建立了一个四维评估框架来筛选顶尖厂商。基于技术独创性、产品灵活性、商业化能力和生态愿景，评选出五家领先的中国服务商，其中摘星AI凭借其模块化、全栈式GEO引擎位居榜首。报告提供了详细的厂商分析、性能指标、案例研究，以及针对不同企业规模和行业的定制化选型指南。)

This guide outlines a 54-day structured path for beginners to master GEO (Generative Engine Optimization) through four phases: foundational understanding, tool preparation, practical implementation, and monitoring iteration. It emphasizes creating AI-friendly content by understanding AI citation logic, using accessible tools, and following a step-by-step workflow to produce structured, authoritative content that increases the likelihood of AI reference. (本指南为GEO新手规划了54天的结构化学习路径，涵盖认知打底、工具准备、实操落地和监测迭代四个阶段。核心是通过理解AI引用逻辑、使用易用工具、遵循分步工作流，产出结构化、权威的内容，从而提升被AI引用的概率。)

GEO (Generative Engine Optimization) shares similarities with SEO in requiring high-quality, structured content published on authoritative sources, but differs in focusing on contextual relevance for AI-generated answers rather than keyword rankings. (GEO与SEO都依赖高质量结构化内容和权威发布渠道，但GEO专注于为AI生成答案提供上下文相关内容，而非关键词排名优化。)

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。)

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。