How to find and unlock the data hidden within videos

Video content is largely unsearchable due to its complex multimodal nature, but a practical pipeline can unlock it. The approach involves preprocessing videos by detecting scene changes and extracting key visual snapshots using image embeddings (e.g., CLIP), then enriching those snapshots with text descriptions via a VLM. These are indexed in Vespa, an open-source search platform with native multivector and tensor support, enabling hybrid search that combines vector similarity and keyword signals in a single ranking expression. The result is a system that can retrieve specific visual moments across videos alongside other document types, with flexibility to add audio transcription and newer multimodal models over time.