Metadata-aware Vector Embedding MoE Models | Haystack Conf 2025
Автор: Superlinked
Загружено: 2025-05-15
Просмотров: 210
Описание: This talk presents an approach to vector embeddings that effectively integrates structured metadata with unstructured text. Daniel demonstrates how traditional text encoders struggle with numerical data and proposes a mixture of experts model to address this limitation. The technical implementation creates a directed acyclic graph (DAG) for encoding multiple signal types into a unified embedding space, enabling semantically meaningful representations of complex queries containing location data, numerical values, and textual elements. The architecture sits atop vector databases like Redis, MongoDB and Quadra, providing a smoother optimization surface compared to traditional JSON-based query systems. An Apache 2.0 licensed implementation is available for technical evaluation and experimentation.
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