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@@ -19,7 +19,7 @@ features:
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description: 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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description: 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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link:
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link:
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text: Learn More
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text: Learn More
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url: /documentation/tutorials/hybrid-search-fastembed/
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url: /documentation/beginner-tutorials/hybrid-search-fastembed/
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- id: 2
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- id: 2
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icon:
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icon:
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src: /icons/outline/selection-blue.svg
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src: /icons/outline/selection-blue.svg
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@@ -18,7 +18,7 @@ A neural search service uses artificial neural networks to improve the accuracy
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<aside role="status">
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<aside role="status">
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There is a version of this tutorial that uses <a href="https://github.com/qdrant/fastembed">Fastembed</a> model inference engine instead of Sentence Transformers.
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There is a version of this tutorial that uses <a href="https://github.com/qdrant/fastembed">Fastembed</a> model inference engine instead of Sentence Transformers.
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Check it out <a href="/documentation/tutorials/hybrid-search-fastembed/">here</a>.
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Check it out <a href="/documentation/beginner-tutorials/hybrid-search-fastembed/">here</a>.
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</aside>
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</aside>
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@@ -24,7 +24,7 @@ content:
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title: Search
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title: Search
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description: Build a simple neural search service with Qdrant and FastEmbed. Learn how to upload data, create indexes, and run search queries.
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description: Build a simple neural search service with Qdrant and FastEmbed. Learn how to upload data, create indexes, and run search queries.
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link:
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link:
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url: /documentation/tutorials/hybrid-search-fastembed/
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url: /documentation/beginner-tutorials/hybrid-search-fastembed/
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text: Read More
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text: Read More
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- id: 2
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- id: 2
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image:
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image:
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