Build an Agentic Video Search System Using Voyage AI, MongoDB, and Anthropic

As search systems evolve to accommodate natural language queries and multimodal data, a single retrieval strategy is insufficient to capture the diverse user intents. This tutorial demonstrates building an agentic video search system using MongoDB's capabilities and Voyage AI's voyage-multimodal-3.5 model, which supports text, images, and videos. It focuses on implementing a system that searches cooking videos, adapting its strategy based on the query type, whether visual or textual. The system utilizes a large language model (LLM) as a router to decide between vector and hybrid search methods, based on the user's query. The vector search examines joint embeddings of video segments and captions, while the hybrid search combines vector search with full-text search on captions. The tutorial guides through setting up necessary libraries, embedding video segments, creating search indexes, and defining search functions, culminating in an adaptive search architecture that highlights the potential of the LLM-as-a-router design pattern in handling diverse queries.

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