🚨 GitHub 监控消息提醒 🚨发现关键词：#漏洞#利用#分析 📦项目名称：cve-llm-kg 👤项目作者：nantangqiuweiwan 🛠开发语言： Python ⭐Star数量： 1 | 🍴Fork数量： 0 📅更新时间： 2026-05-03 09:58:37 📝项目描述： 基于 LLM 的 CVE 漏洞知识图谱构建与分析：针对网络协议相关的 CVE 漏洞，设计 Prompt，利用大语言模型作为“智能解析器”，从非结构化的 CVE 描述中自动抽取关键实体和关系。将抽取出的实体和关系导入图数据库，构建一个可视化的物联网漏洞知识图谱。 🔗点击访问项目地址
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@R4marketing · Post #806 · 2021/08/13 08:00
Совсем лайтовая статья для новичков "10 главных конструкций языка R". Содержание: - Комментарии - Переменные и векторы - Внешние модули - Ввод и вывод - Присваивание и сравнение - Условный оператор if - Цикл for - Функции - Классы, методы и объекты #статьи #easy
@testflightynoti · Post #37780 · 2026/05/07 20:30
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@testflightynoti · Post #37748 · 2026/05/07 08:21
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@githubtrending · Post #15433 · 2026/01/23 14:30
#python#deepseek#demo#easy#embedding#flask#gpt#huggingface_transformers#llm#mcp#multimodal#openai#qwen#rag#sentence_transformers#ui#vllm#vlm UltraRAG is a lightweight framework that makes building retrieval-augmented generation (RAG) systems simple and fast. It uses a low-code approach where you write just dozens of lines of YAML configuration instead of complex code to create sophisticated AI workflows with conditional logic and loops. The framework includes a visual development environment where you can drag-and-drop to build pipelines, adjust parameters in real-time, and instantly convert your logic into interactive chat applications. This means you can deploy powerful AI systems that ground answers in your own data—reducing hallucinations and improving accuracy—without needing extensive coding expertise or lengthy development cycles. https://github.com/OpenBMB/UltraRAG