搜索结果：结构化数据

LLM Knowledge Graph Builder transforms unstructured data into Neo4j knowledge graphs, offering superior accuracy over traditional RAG through GraphRAG's relationship-aware retrieval mechanism. 原文翻译： LLM知识图谱构建器将非结构化数据转换为Neo4j知识图谱，通过GraphRAG的关系感知检索机制，提供比传统RAG更优越的准确性。

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 analyzes the winning solutions from the Enterprise RAG Challenge, detailing the architectures, models, and experimental approaches used by top performers like Ilya Rice and Emil Shagiev. It provides insights into effective RAG patterns, including dense retrieval, LLM reranking, router patterns, and structured outputs. 原文翻译： 本文分析了企业RAG挑战赛的获胜解决方案，详细介绍了Ilya Rice和Emil Shagiev等顶级选手使用的架构、模型和实验方法。文章深入探讨了有效的RAG模式，包括密集检索、LLM重排序、路由模式和结构化输出。

Librarium is a command-line tool that parallelizes research queries across multiple search and deep-research APIs, collecting and normalizing results into structured output. It features 18 built-in providers across three tiers (deep-research, AI-grounded, raw-search) and supports custom providers. 原文翻译： Librarium 是一个命令行工具，可将研究查询并行分发到多个搜索和深度研究 API，收集并规范化结果，输出结构化数据。它包含 18 个内置提供商，分为三个层级（深度研究、AI 基础搜索、原始搜索），并支持自定义提供商。

A new group standard for Generative Engine Optimization (GEO) has been established in China to address risks like data poisoning and misinformation in AI-generated content, aiming to build a trustwort

GEO (Generative Engine Optimization) is a systematic strategy to make brand information understood, prioritized, and integrated into AI-generated answers, shifting focus from traditional search rankin

This comprehensive guide explains Retrieval-Augmented Generation (RAG) as a solution to large language models' knowledge cutoff and hallucination issues, detailing its five-generation evolution, pract

RAG投毒是一种针对检索增强生成系统的攻击方式，攻击者通过向知识库注入伪造文档，使大语言模型将虚假信息作为权威事实输出。攻击成功需满足两个关键条件：伪造文档在语义上与目标查询高度相似，且能超越真实文档被优先检索。实验表明，仅需三份精心设计的伪造文档即可在本地环境中成功实施攻击，导致系统输出完全错误的财务信息。这种攻击具有持久性、隐蔽性和低门槛的特点，而嵌入异常检测被证明是最有效的防御策略。

DeepGEMM is a high-performance matrix multiplication library optimized for NVIDIA Hopper GPUs, achieving over 1350 FP8 TFLOPS. It supports standard and Mixture-of-Experts (MoE) computations with just

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