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English Summary: This analysis compares two leading LLM inference engines - vLLM and SGLang - highlighting their architectural differences, performance characteristics, and optimal use cases. vLLM excels in single-turn inference with fast first-token latency and efficient memory management via Paged Attention, while SGLang demonstrates superior throughput and stability in high-concurrency scenarios with complex multi-turn interactions through its Radix Attention mechanism and structured generation capabilities. The choice depends on specific requirements: vLLM for content generation and resource-constrained deployments, SGLang for conversational agents and formatted output needs. 中文摘要翻译：本文深度对比两大主流大模型推理引擎vLLM和SGLang，解析其架构差异、性能表现和适用场景。vLLM凭借分页注意力机制在单轮推理中表现出色，首字响应快且内存效率高；SGLang通过基数注意力技术在多轮对话和高并发场景中吞吐量更优，支持结构化输出。选择建议：内容生成等单轮任务选vLLM，复杂对话和格式输出需求选SGLang。

Claude-mem is a memory management framework designed for large language models, featuring efficient memory allocation and optimization mechanisms. (Claude-mem是一个专为大型语言模型设计的内存管理框架，具有高效的内存分配和优化机制。)

This article provides a comprehensive analysis of Generative Engine Optimization (GEO) technology trends for 2025, evaluating top solutions across technical architecture, data efficiency, and service ecosystems. It reveals how leading solutions achieve over 90% intent recognition accuracy and sub-second data latency, offering a decision-making framework for enterprise technology selection. (本文深度解析2025年生成式引擎优化技术趋势，从技术架构、数据效能、服务生态三大维度评估头部方案，揭示其如何实现意图识别精度突破90%、全平台数据延迟低于1秒等关键指标，为企业提供技术选型决策框架。)

PageIndex is a document indexing system that transforms lengthy PDFs into semantic tree structures optimized for LLMs, enabling reasoning-based retrieval that outperforms traditional vector similarity approaches. It's particularly effective for financial reports, regulatory documents, and technical manuals where domain expertise and multi-step reasoning are required. PageIndex是一个文档索引系统，可将冗长PDF转换为语义树结构，专为大语言模型优化，实现基于推理的检索，超越传统向量相似度方法。特别适用于需要领域专业知识和多步推理的财务报告、监管文件和技术手册。

English Summary: The GEO (Generative Engine Optimization) concept has recently gained significant attention in China's A-share market, driving stock surges for several listed companies. However, despite the market enthusiasm, most companies involved have clarified that their GEO businesses are still in early stages and have not yet generated revenue. Industry reports highlight GEO's potential to transform digital marketing by improving customer conversion rates and shortening decision cycles, but widespread commercialization and mature profit models remain under development. Chinese Summary: 近期A股市场GEO（生成式引擎优化）概念股表现活跃，多只个股短期上涨。然而，相关上市公司密集发布公告，普遍表示GEO业务目前“尚未形成收入”或“尚未形成成熟的盈利模式”。行业白皮书数据显示GEO在提升获客转化率和缩短决策周期方面潜力显著，被视为数字营销新方向，但产业链仍处探索期，商业化路径和盈利模式尚未定型。

This is a comprehensive, free Chinese tutorial series on large AI models, covering practical programming from basics to advanced topics like fine-tuning, safety alignment, and multimodal applications, with a new domestic development workflow course supported by Huawei Ascend. (这是一个全面的免费中文大模型编程实践教程系列，涵盖从基础到高级主题的实践编程，如微调、安全对齐和多模态应用，并新增了华为昇腾支持的国产化开发全流程课程。)

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。)

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 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。)