From 2875235e7b2a704d6c1e38f5f821b1cf8525064b Mon Sep 17 00:00:00 2001 From: Milvus-doc-bot Date: Fri, 22 Nov 2024 07:55:48 +0000 Subject: [PATCH] Generate en docs --- localization/v2.4.x/site/en/reference/index.md | 4 ++-- localization/v2.4.x/site/en/reference/metric.md | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/localization/v2.4.x/site/en/reference/index.md b/localization/v2.4.x/site/en/reference/index.md index 90d86989e..0758b4bfc 100644 --- a/localization/v2.4.x/site/en/reference/index.md +++ b/localization/v2.4.x/site/en/reference/index.md @@ -57,14 +57,14 @@ Currently, a vector field only supports one index type. Milvus automatically del d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z" > -

Milvus supports various index types, which are categorized by the type of embedding they handle: floating-point, binary, and sparse.

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Milvus supports various index types, which are categorized by the type of vector embeddings they handle: floating-point embeddings (also known as floating point vectors or dense vectors), binary embeddings (also known as binary vectors), and sparse embeddings (also known as sparse vectors).

Floating-point embeddings Binary embeddings Sparse embeddings
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Indexes for floating-point embeddings

For 128-dimensional floating-point embeddings, the storage they take up is 128 * the size of float = 512 bytes. And the distance metrics used for float-point embeddings are Euclidean distance (L2) and Inner product (IP).

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Indexes for floating-point embeddings

For 128-dimensional floating-point embeddings (vectors), the storage they take up is 128 * the size of float = 512 bytes. And the distance metrics used for float-point embeddings are Euclidean distance (L2) and Inner product (IP).

These types of indexes include FLAT, IVF_FLAT, IVF_PQ, IVF_SQ8, HNSW, and SCANN for CPU-based ANN searches.

diff --git a/localization/v2.4.x/site/en/reference/metric.md b/localization/v2.4.x/site/en/reference/metric.md index a2b94127c..a66b5a6ca 100644 --- a/localization/v2.4.x/site/en/reference/metric.md +++ b/localization/v2.4.x/site/en/reference/metric.md @@ -21,7 +21,7 @@ title: Similarity Metrics >

In Milvus, similarity metrics are used to measure similarities among vectors. Choosing a good distance metric helps improve the classification and clustering performance significantly.

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The following table shows how these widely used similarity metrics fit with various input data forms and Milvus indexes.

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The following table shows how these widely used similarity metrics fit with various input data forms and Milvus indexes. Currently, Milvus supports various types of data, including floating point embeddings (often known as floating point vectors or dense vectors), binary embeddings (also known as binary vectors), and sparse embeddings (also known as sparse vectors).

Floating point embeddings Binary embeddings @@ -88,7 +88,7 @@ title: Similarity Metrics
Milvus only caculates the value before applying square root when Euclidean distance is chosen as the distance metric.
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Inner product (IP)

The IP distance between two embeddings are defined as follows:

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Inner product (IP)

The IP distance between two vector embeddings are defined as follows:

ip