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AUTO: Sync ScalarDB docs in English to docs site repo (#1135)
Co-authored-by: josh-wong <[email protected]>
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docs/scalardb-cluster/getting-started-with-vector-search.mdx

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@@ -214,7 +214,7 @@ Select the embedding model that you want to use, and follow the instructions to
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<Tabs groupId="embedding-model" queryString>
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<TabItem value="in-process" label="In-process" default>
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The in-process embedding model is a local embedding model powered by <a href="https://onnxruntime.ai/docs/get-started/with-java.html">ONNX runtime</a> and is running in the ScalarDB Cluster process. This embedding model is useful for fast prototyping and simple use cases.
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The in-process embedding model is a local embedding model powered by [ONNX runtime](https://onnxruntime.ai/docs/get-started/with-java.html) and is running in the ScalarDB Cluster process. This embedding model is useful for fast prototyping and simple use cases.
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To use the in-process embedding model, add the following property to the configuration file:
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@@ -396,11 +396,11 @@ try (ScalarDbEmbeddingClientFactory scalarDbEmbeddingClientFactory =
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TextSegment segment1 = TextSegment.from("I like football.");
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Embedding embedding1 = scalarDbEmbeddingModel.embed(segment1).content();
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scalarDbEmbeddingStore.add(embedding1, segment1);
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TextSegment segment2 = TextSegment.from("The weather is good today.");
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Embedding embedding2 = scalarDbEmbeddingModel.embed(segment2).content();
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scalarDbEmbeddingStore.add(embedding2, segment2);
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// Search for embeddings.
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Embedding queryEmbedding =
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scalarDbEmbeddingModel.embed("What is your favourite sport?").content();
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.queryEmbedding(queryEmbedding)
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.maxResults(1)
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.build());
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// Print the search result.
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List<EmbeddingMatch<TextSegment>> matches = result.matches();
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EmbeddingMatch<TextSegment> embeddingMatch = matches.get(0);

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