Advanced Video Retrieval at Scale: A Quick Start Using Vespa and TwelveLabs

Emerging video search use cases in enterprise environments have driven interest in advanced retrieval systems that extend beyond simple transcript searches, prompting exploration into more sophisticated solutions like Vespa and TwelveLabs. These systems address complex requirements such as searching based on visual content and context within videos, which traditional methods like audio-to-text conversion might not fully capture. TwelveLabs offers a multi-modal embedding model capable of capturing visual expressions, body language, spoken words, and overall video context, while Vespa provides a robust platform for scalable video storage and search, utilizing billion-scale vector search and hybrid search capabilities that combine lexical and semantic approaches. Vespa's advanced ranking capabilities, including a multi-phase ranking approach, allow efficient retrieval and ranking of relevant video clips amidst extensive video collections. The text details the implementation of video search using TwelveLabs' embedding models and Vespa's distributed architecture, demonstrating the integration of these technologies to perform complex video searches with enriched metadata and multi-vector representations, alongside a practical example using sample videos.

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