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PageIndex is an open-source document indexing system by Vectify AI designed for high-precision retrieval and analysis of long professional documents. It uses document structure and LLM reasoning instead of vector databases, enabling human-like search workflows. (PageIndex是Vectify AI开源的文档索引系统，面向长篇专业文档的高精度检索与分析。它通过文档结构和LLM推理替代向量数据库，实现类人类的检索流程。)

PageIndex is an open-source, vector-free Retrieval-Augmented Generation (RAG) system developed by VectifyAI. It addresses accuracy issues in long-document retrieval by constructing hierarchical tree-like indexes that mimic human document processing logic, enabling precise retrieval based on reasoning rather than vector matching. It supports features like chunk-free processing and visual retrieval, making it suitable for professional scenarios such as financial reports, academic papers, and legal documents, and can be deployed via self-hosting or cloud services. PageIndex 是由 VectifyAI 开发的开源、无向量检索增强生成（RAG）系统。它通过构建层级树状索引模拟人类处理文档的逻辑，基于推理而非向量匹配实现精准检索，解决了传统向量数据库在长文档检索中依赖语义相似性导致的准确性问题。它支持无分块处理、视觉检索等功能，适用于金融报告、学术论文、法律文档等专业场景，可通过自托管或云服务快速部署使用。

PageIndex is an open-source document indexing system designed for reasoning-based RAG, which structures long documents into hierarchical trees rather than fixed chunks, enabling LLMs to perform targeted traversal and multi-step reasoning for more accurate retrieval in professional domains like finance, law, and technical documentation. PageIndex为推理型RAG设计的开源文档索引系统，通过将长文档构建为层次化树形结构而非固定分块，使大模型能够进行定向遍历和多步推理，在金融、法律、技术文档等专业领域实现更精准的检索。

PageIndex is an open-source reasoning-based RAG framework that replaces vector similarity search with structured document trees and LLM reasoning, achieving 98.7% accuracy on FinanceBench by preserving document context and enabling transparent retrieval paths. (PageIndex 是一个开源的基于推理的 RAG 框架，它用结构化文档树和大模型推理取代向量相似度搜索，通过在 FinanceBench 上实现 98.7% 的准确率，保留了文档上下文并实现了透明的检索路径。)

PageIndex is a vectorless, reasoning-based RAG framework that uses hierarchical tree indexing and LLM reasoning for human-like retrieval over long professional documents, eliminating the need for vector databases and chunking. (PageIndex是一个向量无关、基于推理的RAG框架，通过分层树索引和LLM推理实现类人检索，适用于长专业文档分析，无需向量数据库和分块处理。)

PageIndex simulates human expert knowledge extraction by transforming documents into tree-structured indexes and using LLM reasoning for precise information retrieval. It excels in domain-specific applications like financial reports and legal documents, prioritizing accuracy and explainability over speed. (PageIndex通过模拟人类专家知识提取，将文档转换为树状结构索引，并利用LLM推理进行精确信息检索。它在金融报告和法律文件等特定领域应用中表现出色，优先考虑准确性和可解释性而非速度。)

Mastra is a comprehensive TypeScript framework for building production-ready AI applications, offering integrated workflows, memory systems, streaming responses, evaluation tools, and a visual Studio interface to streamline development. (Mastra是一个全面的TypeScript框架，用于构建生产就绪的AI应用，提供集成的工作流、记忆系统、流式响应、评估工具和可视化Studio界面，以简化开发流程。)

本白皮书首次系统阐述了生成式引擎优化（GEO）的定义、核心框架、市场价值与发展路径。随着生成式AI在信息获取中占比超过40%，传统搜索引擎优化（SEO）的流量逻辑正被重构。GEO通过构建“主题权威”、优化语义架构、适配多模态检索，帮助企业在AI原生环境中建立可持续的认知资产与增长动能。报告基于企业调研与专家访谈，首次提出GEO实施的“四维能力模型”与效果评估的“C-ARM指标体系”。研究发现，早期部署GEO策略的企业，其AI端品牌提及率平均提升147%，高质量线索获取周期缩短35%。报告同时指出行业面临标准缺失、合规不确定性与人才断层等挑战，并倡议各方共同推进方法论标准化、建立合规协作机制、培育复合型人才，以把握生成式AI带来的市场机遇。

The llms.txt proposal introduces a standardized markdown file at website roots to provide LLM-friendly content, addressing context window limitations by offering curated, structured information with links to detailed resources. (llms.txt提案通过在网站根目录引入标准化markdown文件，为大型语言模型提供友好内容，通过精选结构化信息和详细资源链接，解决上下文窗口限制问题。)