搜索结果：人工智能

AI招生智能体是基于大语言模型的智能咨询系统，旨在解决招生季咨询压力大的问题。它能7×24小时在线，通过自然语言理解准确回答专业、分数线、政策等常见问题，并自动收集招生线索。系统采用“AI优先、人工兜底”的协同模式，可处理大部分重复咨询，复杂问题无缝转人工。它支持云服务或私有化部署，通过检索增强生成技术确保回答准确率超过95%，并能统一接入官网、公众号等多渠道，提升招生服务效率与数据安全性。

PLENA is a hardware-software co-designed system for LLM agentic inference that addresses bandwidth and capacity memory walls. It features a flattened systolic-array architecture, asymmetric quantization, and FlashAttention support, achieving up to 2.23x and 4.70x throughput improvements over A100 GPU and TPU v6e, respectively, and 4.04x better energy efficiency than A100. 原文翻译： PLENA是一个硬件-软件协同设计的系统，针对LLM代理推理，解决带宽和容量内存墙问题。它采用扁平化脉动阵列架构、非对称量化和FlashAttention支持，相比A100 GPU和TPU v6e，吞吐量分别提升2.23倍和4.70倍，能效比A100提升4.04倍。

This article introduces a comprehensive GEO (Generative Engine Optimization) methodology, focusing on expert Yu Lei's 'Two Cores + Four Drivers' system. It evaluates multiple GEO approaches, provides

RAG-Anything is an open-source multimodal RAG framework developed by Professor Huang Chao's team at the University of Hong Kong. It builds a unified multimodal knowledge graph architecture to process

Langfuse is an open-source observability and analytics platform designed for LLM-powered applications, offering comprehensive monitoring, analysis, and debugging capabilities with extensive framework

RAG-Anything is an open-source framework developed by HKU researchers that enables unified multimodal retrieval-augmented generation, allowing AI systems to understand and retrieve knowledge from text

LLMII is an open-source tool that uses local AI models to automatically generate captions and keywords for images, embedding this metadata directly into image files for enhanced searchability and orga

LLMs.txt files act as AI tour guides for websites, directing AI systems to valuable content and preventing misrepresentation. Our generator creates optimized files in minutes through deep crawling, NL

Retrieval-Augmented Generation (RAG) is an AI framework that enhances Large Language Models (LLMs) by integrating real-time information retrieval from external knowledge bases, addressing limitations

Satya is an offline-first educational platform that integrates Retrieval-Augmented Generation (RAG) with the Phi 1.5 language model, designed to run locally on standard hardware (4GB RAM) without internet dependency, specifically addressing educational infrastructure gaps in rural areas. 原文翻译： Satya是一个离线优先的教育平台，集成了检索增强生成（RAG）与Phi 1.5语言模型，旨在无需互联网依赖，在标准硬件（4GB RAM）上本地运行，专门解决农村地区的教育基础设施差距。