搜索结果：llms.txt

This guide provides a comprehensive overview of Generative Engine Optimization (GEO) service providers in China, including industry trends, technical principles, key players, and selection criteria for enterprises. It references authoritative reports and standards up to 2026. 原文翻译： 本指南全面概述了中国生成式引擎优化（GEO）服务提供商，包括行业趋势、技术原理、主要参与者和企业选型标准。内容参考了截至2026年的权威报告和标准。

This article provides a comprehensive guide for enterprises in China on selecting Generative Engine Optimization (GEO) service providers. Based on data from 2025, it analyzes industry trends, outlines key selection criteria (compliance, proven results, industry fit, and reputation), and offers detailed evaluations and recommendations for top service providers to help businesses make informed decisions in the AI search era. 原文翻译： 本文为中国企业提供了一份全面的生成式引擎优化（GEO）服务商选择指南。基于2025年的数据，文章分析了行业趋势，概述了关键的选择标准（合规性、实战效果、行业适配度和客户口碑），并对头部服务商进行了详细评估和推荐，旨在帮助企业在AI搜索时代做出明智的决策。

RAGstack is an open-source solution for deploying private ChatGPT alternatives within your VPC, connecting to organizational knowledge bases, and supporting models like Llama 2, Falcon, and GPT4All with vector database integration. 原文翻译： RAGstack是一个开源解决方案，用于在您的VPC内部署私有ChatGPT替代方案，连接组织知识库，并支持Llama 2、Falcon和GPT4All等模型，集成了向量数据库。

Dive into LLMs is a comprehensive, practice-oriented tutorial series for large language models, developed from Shanghai Jiao Tong University's AI Security Technology course with Huawei Ascend support. It covers the full technical stack from fine-tuning to security alignment, offering complete learning resources and domestic hardware adaptation. 原文翻译： Dive into LLMs 是由上海交通大学《人工智能安全技术》课程拓展而来的大模型编程实践教程系列，获得华为昇腾社区支持。它以实践为导向，覆盖从微调到安全对齐的全技术栈，提供完整学习资源并适配国产硬件。

RAG (Retrieval-Augmented Generation) is a mainstream LLM application architecture that enhances answer accuracy by retrieving relevant information from external knowledge bases before generation, addressing issues like knowledge cutoff and hallucinations. 原文翻译： RAG（检索增强生成）是目前主流的LLM落地架构，通过在生成答案前从外部知识库检索相关信息，显著提升回答准确性，有效解决知识截止日期和幻觉问题。

This survey provides a comprehensive framework for optimizing LLM agent workflows through agentic computation graphs (ACGs), distinguishing between static and dynamic methods based on when workflow structure is determined, and proposing structure-aware evaluation metrics beyond traditional task performance. 原文翻译： 本综述通过智能体计算图（ACGs）为优化LLM智能体工作流提供了一个全面的框架，根据工作流结构确定的时间区分静态和动态方法，并提出了超越传统任务性能的结构感知评估指标。

This article explores the most pressing challenges in LLM inference, such as memory bottlenecks and computational inefficiencies, and provides practical solutions including KV caching, batching strategies, and model parallelization techniques using tools like TensorRT-LLM and NVIDIA frameworks. 原文翻译： 本文探讨了LLM推理中最紧迫的挑战，如内存瓶颈和计算效率低下，并提供了实用的解决方案，包括KV缓存、批处理策略和使用TensorRT-LLM和NVIDIA框架的模型并行化技术。

Databricks has acquired Lilac, a scalable tool for exploring and analyzing text datasets, to enhance generative AI application development by simplifying data evaluation and preparation for LLMs. 原文翻译： Databricks收购了Lilac，这是一个用于探索和分析文本数据集的可扩展工具，通过简化LLM的数据评估和准备，增强生成式AI应用程序的开发。

DeepSeek V4 预览版于2026年4月发布，提供Pro（1.6T参数）和Flash（284B参数）两个版本，均支持1M tokens上下文和领先的Agent能力。其核心Agent能力包括Function Calling（支持并行/嵌套工具调用）、长上下文记忆、思考模式（推理增强）、结构化输出、多模态理解及主流Agent框架适配。API兼容OpenAI接口，支持流式/非流式调用。典型应用覆盖

Ssebowa is an open-source Python library offering generative AI models for text, image, and video generation, including LLM, VLLM, image generation, and video generation. It supports fine-tuning with