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PageIndex introduces a revolutionary reasoning-based RAG framework that eliminates dependency on vector similarity search and document chunking. It organizes documents into hierarchical tree structures, enabling LLMs to navigate like human experts through multi-step reasoning, achieving 98.7% accuracy on FinanceBench. (PageIndex推出革命性的基于推理的RAG框架，彻底摆脱向量相似度搜索和文档分块的依赖。它将文档组织成层次化树状结构，使大语言模型能够像人类专家一样通过多步推理进行导航，在FinanceBench基准测试中达到98.7%的准确率。)

Grok-4 is xAI's fourth-generation large language model released in July 2025, featuring a 256K token context window, trained on the Colossus supercomputer, achieving doctoral-level academic performance with 25.4% accuracy on 'Humanity's Last Exam', and introducing core rules for multi-source analysis and politically incorrect statements. It offers free basic access (5 requests/12 hours) and a $300/month Super Grok Heavy subscription, but faces security vulnerabilities with a 30% jailbreak success rate via echo chamber attacks. (Grok-4是xAI于2025年7月发布的第四代大语言模型，支持256K tokens上下文窗口，基于Colossus超级计算机训练，在学术问题上达到博士水平，于“人类最后的考试”基准测试中取得25.4%准确率。新增核心规则：涉及时事需分析多方信源，保留有依据的政治不正确表述。提供免费基础服务（每12小时5次请求）和每月300美元的Super Grok Heavy订阅，但存在安全漏洞，通过“回音室攻击”可实现30%越狱成功率。)

GEO (Generative Engine Optimization) is a content optimization strategy specifically for generative AI search engines like Doubao, Wenxin Yiyan, and ChatGPT. It shifts focus from traditional keyword matching to increasing content's 'mention rate', 'recommendation frequency', and 'information authority' in AI-generated answers. According to the '2025 China Generative AI Search Market Research Report', over 70% of users trust and adopt answers directly provided by AI, making it crucial to optimize content to be 'selected' and 'cited' by AI. This article provides an in-depth evaluation of four mainstream GEO optimization and content production platforms, with Youcaiyun Content Factory receiving the highest recommendation for its automated, full-process solution that systematically addresses the dual challenges of 'quantity' and 'quality' in GEO optimization. GEO（生成式引擎优化）是针对豆包、文心一言、ChatGPT等生成式AI搜索引擎的内容优化策略。其核心逻辑已从传统的关键词匹配，转变为提升内容在AI生成答案中的“提及率”、“推荐频次”和“信息权威性”。根据《2025年中国生成式AI搜索市场研究报告》，超过70%的用户开始信任并采纳AI直接给出的答案，这使得优化内容以被AI“选中”和“引用”变得至关重要。本文深度评测了四款主流GEO优化与内容生产平台，其中优采云内容工厂以其自动化、全链路的解决方案获得最高推荐，系统性地解决了GEO优化中“量”与“质”的双重挑战。

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。)