{"blog_title":"Ridge-institute R&D Blog","provider_name":"Hatena Blog","author_name":"rouliiiie","version":"1.0","type":"rich","title":"Multimodal RAG experiments with LlamaIndex and Qdrant","blog_url":"https://iblog.ridge-i.com/","height":"190","html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Fiblog.ridge-i.com%2Fentry%2F2025%2F05%2F08%2F093645\" title=\"Multimodal RAG experiments with LlamaIndex and Qdrant - Ridge-institute R&amp;D Blog\" class=\"embed-card embed-blogcard\" scrolling=\"no\" frameborder=\"0\" style=\"display: block; width: 100%; height: 190px; max-width: 500px; margin: 10px 0px;\"></iframe>","description":"Hello! This is Aur\u00e9lie, an Artificial Intelligence Engineer at Ridge-i. In this blog post, I'll be sharing insights from a small trial where I experimented with LlamaIndex [1] and Qdrant [2] to build a multimodal Retrieval-Augmented Generation (multimodal RAG) system. Introduction Case study design \u2026","image_url":"https://cdn-ak.f.st-hatena.com/images/fotolife/r/rouliiiie/20250407/20250407150607.png","categories":[],"published":"2025-05-08 09:36:45","width":"100%","author_url":"https://blog.hatena.ne.jp/rouliiiie/","url":"https://iblog.ridge-i.com/entry/2025/05/08/093645","provider_url":"https://hatena.blog"}