Hierarchical embeddings, in the context of multimodal search, refer to structured vector representations that organize data into multiple levels of abstraction. These embeddings enable efficient and accurate search across different data types—like text, images, and audio—by grouping similar concepts at higher levels and fine-grained details at lower levels. For example, a hierarchical embedding for an image might first represent it broadly as “animal,” then as “dog,” and finally as “Golden Retriever.” This layered approach allows search systems to navigate from general to specific, reducing computational overhead and improving relevance in cross-modal queries.
To build hierarchical embeddings, data is typically processed through clustering or partitioning methods that group similar vectors at each level. In multimodal scenarios, this might involve training separate embedding models for each data type (e.g., using CLIP for text-image pairs) and then aligning their hierarchies. For instance, a text query like “red sports car” could map to a high-level “vehicles” cluster in image embeddings, then drill down into “cars,” “sports cars,” and finally filter by color. This structure allows the system to avoid comparing the query against every possible vector in a flat dataset, which is computationally expensive. Instead, it traverses the hierarchy, narrowing the search space at each step. Tools like hierarchical navigable small world (HNSW) graphs or tree-based indexes in vector databases (e.g., FAIR’s FAISS) often implement this logic under the hood.
A practical example of hierarchical embeddings in action is a retail search system that handles product images and descriptions. When a user searches for “black leather backpack,” the system might first identify the “bags” category across image and text embeddings, then focus on “backpacks,” and finally apply filters for material and color. This approach improves speed and accuracy compared to flat embeddings, which treat all features equally and might miss contextual relationships. However, designing effective hierarchies requires careful tuning—such as deciding the depth of levels or ensuring alignment between modalities—to avoid mismatches (e.g., text hierarchies that don’t map cleanly to image clusters). Despite these challenges, hierarchical embeddings are a powerful tool for scaling multimodal search systems, especially when dealing with large, diverse datasets.