详解多模态大模型原理与实践，手把手教你构建视觉语言系统。
Grandes Modelos de Linguagem Multimodais (MLLMs): Da Teoria à Prática
- 融合文本与图像/音频感知，实现跨模态理解生成
- 提供基于LangChain/LangGraph的工程化搭建方案
- 适合想落地多模态应用的研究者与开发者
多模态大语言模型（MLLMs）将大语言模型的语言理解与生成能力，与图像、音频等模态的感知能力相结合，是当前人工智能的关键进展。本文阐述了MLLMs的核心基础与代表性模型，探讨了数据预处理、提示工程及利用LangChain和LangGraph构建多模态流水线的实际技术。配套资料已公开于GitHub：https://github.com/neemiasbsilva/MLLMs-Teoria-e-Pratica。最后，文章讨论了现有挑战并展望了未来趋势。
原文摘要 · Abstract (English)
Multimodal Large Language Models (MLLMs) combine the natural language understanding and generation capabilities of LLMs with perception skills in modalities such as image and audio, representing a key advancement in contemporary AI. This chapter presents the main fundamentals of MLLMs and emblematic models. Practical techniques for preprocessing, prompt engineering, and building multimodal pipelines with LangChain and LangGraph are also explored. For further practical study, supplementary material is publicly available online: https://github.com/neemiasbsilva/MLLMs-Teoria-e-Pratica. Finally, the chapter discusses the challenges and highlights promising trends.
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