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Multilingual Retrieval Augmented Generation (M-RAG) combines knowledge graphs with generative AI to overcome language barriers in global service helpdesks, enabling efficient cross-language information retrieval and consistent customer support. 原文翻译： 多语言检索增强生成（M-RAG）将知识图谱与生成式人工智能相结合，解决全球服务台中的语言障碍问题，实现高效的多语言信息检索和一致的客户支持。

NexaSDK is a high-performance local inference framework that enables developers to run the latest multimodal AI models on NPU, GPU, and CPU across multiple platforms with minimal energy consumption and maximum speed. 原文翻译： NexaSDK是一个高性能本地推理框架，让开发者能够在NPU、GPU和CPU上以最低能耗和最高速度运行最新的多模态AI模型，支持跨多个平台。

This article provides a comprehensive 2026 guide for Chinese users to access and utilize Google's Gemini models, particularly Gemini 3 Pro, overcoming regional access barriers through platforms like n.myliang.cn. It covers practical applications in multimodal tasks, AI-assisted office work, programming, and SEO optimization. 原文翻译： 本文为国内用户提供了一份全面的2026年指南，介绍如何通过n.myliang.cn等平台访问和使用谷歌Gemini模型（特别是Gemini 3 Pro），以克服地域访问限制。内容涵盖多模态任务、AI辅助办公、编程和SEO优化等实际应用。

将AI对话历史导入另一AI的提示词指南，包含用户信息分类、引用原话及格式要求，并附Gemini数据上传链接。 原文翻译： A guide for prompting AI to import conversation history into another AI, including user info categorization, original quote citation, formatting requirements, and a Gemini data upload link.

GLM是清华大学提出的通用语言模型预训练框架，采用自回归空白填充技术，为中文技术社区提供了强大的开源大模型选择。 原文翻译： GLM is a general language model pre-training framework proposed by Tsinghua University, utilizing autoregressive blank filling technology, providing a powerful open-source large model option for the Chinese technical community.

Retrieval-Augmented Generation (RAG) is a technique that combines information retrieval with text generation models to enhance the factual accuracy and reliability of language models for knowledge-intensive tasks, reducing hallucinations by incorporating external knowledge sources. 原文翻译： 检索增强生成（RAG）是一种将信息检索与文本生成模型相结合的技术，通过整合外部知识源来增强语言模型在知识密集型任务中的事实准确性和可靠性，从而减少幻觉问题。

This content explores the emerging field of Generative Engine Optimization (GEO), analyzing how AI systems like ChatGPT select and recommend websites based on contextual coverage and source authority rather than traditional SEO metrics, highlighting the visibility gap in AI traffic attribution. 原文翻译： 本文探讨了生成式引擎优化（GEO）这一新兴领域，分析了ChatGPT等AI系统如何基于上下文覆盖度和来源权威性（而非传统SEO指标）选择和推荐网站，并强调了AI流量归因中的可见性差距。

DeepAgents is LangChain's latest open-source framework designed to simplify the development of complex, multi-step AI agents by providing built-in capabilities for task planning, virtual file systems, and subagent collaboration, enabling developers to build more robust and capable agents with less code. 原文翻译： DeepAgents是LangChain最新推出的开源框架，旨在简化复杂多步骤AI智能体的开发，通过提供任务规划、虚拟文件系统和子智能体协作等内置能力，使开发者能够用更少的代码构建更强大、更可靠的智能体。

OpenViking is an open-source context database for AI Agents that organizes context like a file system, using hierarchical abstraction (L0/L1/L2) and recursive retrieval to reduce token costs and improve task completion rates in long-running, multi-step agent scenarios. 原文翻译： OpenViking 是一个面向 AI Agent 的开源上下文数据库，它将上下文组织成文件系统，采用分层抽象（L0/L1/L2）和递归检索，旨在降低长任务、多步骤 Agent 场景中的 token 成本并提升任务完成率。