How to Superpower Your Semantic Search Using a Vector Database Vector Space Talks - Qdrant

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Malt successfully transitioned to Qdrant for their freelancer matching application, significantly reducing latency from 10 seconds to 1 second. This involved adopting a retriever-ranker architecture, utilizing multilingual transformer-based encoder models for high-fidelity embeddings, and leveraging Qdrant's capabilities for geospatial filtering and scaling.