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Learn how to build a custom LlamaIndex retriever that leverages Superlinked's mixture of encoders approach to overcome the limitations of single text encoders in search and recommendation systems. Discover how specialized embeddings for different data modalities—including language, numbers, images, time, and categories—can be combined to represent data more optimally than forcing everything through a single text encoder. Explore the technical implementation of fusing multiple encoders while persisting vectors in Qdrant, enabling unified retrieval across both structured and unstructured attributes without sacrificing accuracy. Examine configuration templates designed for common use cases such as e-commerce catalogs and enterprise knowledge bases, and review internal evaluation results demonstrating accuracy improvements over single-encoder baselines. Gain practical guidance for deploying these multi-encoder systems in production pipelines, with insights into real-world performance gains and implementation considerations for enhanced search and recommendation capabilities.
