| Search with Qdrant |
Qdrant enhances search, offering semantic, similarity, multimodal, and hybrid search capabilities for accurate, user-centric results, serving applications in different industries like e-commerce to healthcare. |
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Semantic Search |
Qdrant optimizes similarity search, identifying the closest database items to any query vector for applications like recommendation systems, RAG and image retrieval, enhancing accuracy and user experience. |
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/documentation/concepts/search/ |
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Hybrid Search for Text |
By combining dense vector embeddings with sparse vectors e.g. BM25, Qdrant powers semantic search to deliver context-aware results, transcending traditional keyword search by understanding the deeper meaning of data. |
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/documentation/beginner-tutorials/hybrid-search-fastembed/ |
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Multimodal Search |
Qdrant's capability extends to multi-modal search, indexing and retrieving various data forms (text, images, audio) once vectorized, facilitating a comprehensive search experience. |
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/documentation/tutorials/multimodal-search-fastembed/ |
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Single Stage filtering That Works |
Qdrant enhances search speeds and control and context understanding through filtering on any nested entry in our payload. Unique architecture allows Qdrant to avoid expensive pre-filtering and post-filtering stages, making search faster and accurate. |
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/articles/filtrable-hnsw/ |
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