Multimodal Search

Introduction The explosion of unstructured data—images, video, audio, text, and sensor streams—has forced modern search engines to move beyond traditional keyword matching. Multimodal search refers to the capability of retrieving relevant items across different media types using a single query that may itself be multimodal (e.g., an image plus a short text caption). At the heart of this capability lies vector similarity search: every item is embedded into a high‑dimensional vector space where semantic similarity translates to geometric proximity. While single‑node approximate nearest neighbor (ANN) libraries such as Faiss, Annoy, or Milvus can handle millions of vectors, real‑world deployments often need to serve billions of vectors, guarantee low latency under heavy load, and support hybrid queries that combine vector similarity with traditional filters (date ranges, categories, user permissions, etc.). ...

Introduction Enterprises are increasingly demanding search experiences that go beyond simple keyword matching. Modern users expect instant, context‑aware results that can combine text, images, audio, and even video—collectively known as multimodal search. At the same time, many organizations must keep data on‑premises or within a private cloud to satisfy regulatory, security, or performance constraints. Retrieval‑augmented generation (RAG) has emerged as a powerful paradigm for fusing large language models (LLMs) with external knowledge bases. The RAGmini variant—lightweight, modular, and designed for low‑latency environments—offers a compelling foundation for building multimodal search pipelines that can run on private clouds. ...