Superlinked: Unified Vector Search Without Re-ranking
Traditional vector search often relies on time-consuming and computationally expensive re-ranking to improve result relevance. Superlinked, a Python framework for building high-performance search and recommendation systems, elegantly solves this problem by unifying structured and unstructured data into multimodal vectors. It uses a mixture of encoders at index time to combine text semantics, numerical ranges, and categorical attributes into unified embeddings, eliminating the need for re-ranking to achieve more relevant, faster, and more efficient results at query time. Superlinked supports dynamic intent capture and hard filtering, allowing users to adjust weights and filter out irrelevant results at query time, further improving search accuracy and efficiency.
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