When deciding between joint or separate indexes for different data modalities (like text, images, or audio), the choice depends on use case requirements, query patterns, and system constraints. Joint indexes combine multiple modalities into a single index, enabling unified search but requiring careful alignment of data representations. Separate indexes handle each modality independently, offering flexibility and specialized optimizations. The decision hinges on whether queries need cross-modal retrieval, the complexity of aligning data types, and the trade-offs between performance and maintainability.
Use joint indexes when queries require cross-modal retrieval. For example, a product search system might need to find items using both text descriptions and image similarity. A joint index could map text and images into a shared embedding space (e.g., using models like CLIP), allowing users to search with either modality. This approach simplifies query logic and ensures results are directly comparable. However, joint indexing demands robust alignment of modalities during training and may require significant computational resources to maintain consistency. It’s also less practical if modalities have vastly different update frequencies—like frequently changing text metadata vs. static images—since retraining the joint model could become costly.
Separate indexes are preferable when modalities have distinct query patterns or scalability needs. For instance, a video platform might use a text index for metadata (title, tags) and a separate visual index for frame-based similarity searches. This allows each index to use specialized tools: Elasticsearch for text and FAISS for vectors. Separate indexing simplifies updates (e.g., modifying the text index without affecting the image index) and lets teams optimize each system independently. However, combining results from separate indexes requires post-processing (like score fusion), which can add latency. It’s also less intuitive for multimodal queries—for example, a search for “videos with upbeat music and bright colors” would need two separate queries followed by result merging, which might miss nuanced cross-modal relationships. Choose this approach if performance, modularity, or incremental updates are priorities over unified retrieval.