GEO

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SuperLocalMemory V2本地记忆系统详解：2026年AI助手持久记忆指南

SuperLocalMemory V2本地记忆系统详解：2026年AI助手持久记忆指南

SuperLocalMemory V2 is a 100% local, zero-setup memory system for AI assistants that enables persistent context across sessions through real-time coordination, hybrid search, and knowledge graph architecture. (SuperLocalMemory V2是一个完全本地化、零配置的AI助手记忆系统，通过实时协调、混合搜索和知识图谱架构实现跨会话的持久上下文记忆。)
AI大模型2026/2/13
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LLM黑盒优化技术解析：2024实现指南与案例详解

LLM黑盒优化技术解析：2024实现指南与案例详解

LLM Optimize is a proof-of-concept library that enables large language models (LLMs) like GPT-4 to perform blackbox optimization through natural language instructions, allowing optimization of arbitrary text/code strings with explanatory reasoning at each step. (LLM Optimize是一个概念验证库，通过自然语言指令让大语言模型（如GPT-4）执行黑盒优化，能够优化任意文本/代码字符串，并在每个步骤提供解释性推理。)
LLMS2026/2/13
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GEO生成引擎优化指南：2024年AI搜索排名提升策略

GEO生成引擎优化指南：2024年AI搜索排名提升策略

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。)
GEO2026/2/13
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2024年AI爬虫标准指南：LLMs.txt详解与应用

2024年AI爬虫标准指南：LLMs.txt详解与应用

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。)
LLMS2026/2/13
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LangExtract库：利用大语言模型精准提取结构化信息2026指南

LangExtract库：利用大语言模型精准提取结构化信息2026指南

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。)
LLMS2026/2/12
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LangExtract库从非结构化文本提取结构化信息2026指南

LangExtract库从非结构化文本提取结构化信息2026指南

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。
LLMS2026/2/9
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Cognee开源AI记忆引擎重塑知识管理2026年指南

Cognee开源AI记忆引擎重塑知识管理2026年指南

Cognee is an innovative open-source AI memory engine that combines knowledge graphs and vector storage technologies to provide dynamic memory capabilities for large language models (LLMs) and AI agents. This comprehensive evaluation covers its functional features, installation deployment, use cases, and commercial value. (Cognee是一个创新的开源AI记忆引擎，通过结合知识图谱和向量存储技术，为大型语言模型和AI智能体提供动态记忆能力。本测评全面评估其功能特性、安装部署、使用案例及商业价值。)
AI大模型2026/2/6
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打破AI Agent失忆瓶颈：Cognee开源记忆工具2026年技术指南

打破AI Agent失忆瓶颈：Cognee开源记忆工具2026年技术指南

Cognee is an open-source AI memory tool that addresses the 'memory loss' problem in AI Agents through its innovative ECL pipeline architecture, achieving 92.5% answer relevance. It supports dynamic memory updates, multi-source data compatibility, and offers both code and UI operation modes for easy deployment and use. Cognee为AI Agent解决“失忆”问题的开源记忆工具，通过创新的ECL流水线架构实现92.5%的高回答相关性，支持动态记忆更新和多源数据兼容，提供代码与UI双操作模式，部署简便。
AI大模型2026/2/6
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