What are Entity Collections?
Entity Collections in Cloudglue enable you to organize multiple videos and extract consistent, structured data across your entire video library. Unlike the on-demand Extract API which processes videos individually, Entity Collections apply a unified extraction schema and prompt to every video in the collection, ensuring standardized data structures for comparative analysis, search, and insights. This allows you to create a queryable database of video content organized around the specific entities that matter to your use case. While the on-demand Extract API is perfect for one-off structured data extraction, Entity Collections shine when you need to work with multiple videos over time. Collections automatically process new videos as they’re added, maintain consistent extraction parameters across your content library, and enable powerful cross-video analysis. For example, you can track cooking techniques across recipe videos, compare product features across demos, or identify patterns in educational content—all without manually configuring extraction for each video. Entity Collections integrate seamlessly with the rest of Cloudglue’s ecosystem, working alongside Files for storage and Media Descriptions for detailed content understanding. You can create collections with custom extraction schemas tailored to your specific domain, whether that’s cooking videos with ingredient tracking, product demonstrations with feature extraction, or educational content with teaching method identification. To get started with Entity Collections, see our structured data extraction guide or explore a complete implementation in our cooking videos example.Searching Entity Collections
Entity Collections are first-class Search targets. When a file is added to an entity collection, its extracted entities are automatically indexed into search documents — indexing is free, and only the extraction itself is billed as usual. You can then find files or moments by their extracted data using the Search API or Deep Search. The search scope follows the collection’s extraction level, since each entity collection extracts at exactly one level:
Searching an entity collection at the wrong scope returns a clear 400 error rather than empty results. Entity collections don’t require
enable_summary — their entity documents stand in for generated summaries.
Each collection file exposes a searchable_status (pending → processing → completed, or failed if no completed extraction exists to index from) so you can tell when it becomes searchable. Re-adding an already-processed file to the collection rebuilds a missing or failed index at no cost.
Beyond direct search, entity collections plug into higher-level retrieval: Deep Search can include them in a knowledge base alongside other collection types (omit scope to let the planner pick the right scope per collection), and the Responses API accepts them as knowledge base collections with the nimbus-002-preview model. And search is a complement, not a replacement: your extracted entities remain fully available in structured form via the entities endpoints and Structured Queries.