OmniRetrieval is a new retrieval framework designed to handle the fragmentation problem of modern knowledge architectures. Rather than collapsing diverse data sources (unstructured text, relational tables, knowledge graphs, property graphs) into a single shared representation, the system identifies which sources are relevant to a user's natural-language query and dispatches source-native queries to their respective execution engines. Testing across 13 datasets and 309 distinct knowledge bases demonstrated that this approach outperforms single-source baselines.
For commerce practitioners, this matters significantly. E-commerce platforms typically juggle incompatible data silos: product catalogs (relational), customer behavior (text logs), supplier networks (graphs), and inventory (structured tables). OmniRetrieval offers a unified query layer that lets merchandisers, data analysts, and recommendation engines ask cross-domain questions without manual ETL or lossy data translation. The preservation of each source's structural affordances—schemas, ontologies, compositional operators—means queries can exploit the expressive power of graphs for relationship discovery or relational queries for transaction logic simultaneously.
This is particularly relevant for omnichannel retailers building AI-assisted search and personalization systems. Rather than investing in unified data warehouses or vector-only embeddings that flatten structural information, teams can now query heterogeneous sources natively through a single interface, reducing implementation complexity and preserving query precision.