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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"
         ></path>
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-    </button></h2><p>Milvus supports various index types, which are categorized by the type of embedding they handle: <strong>floating-point</strong>, <strong>binary</strong>, and <strong>sparse</strong>.</p>
+    </button></h2><p>Milvus supports various index types, which are categorized by the type of vector embeddings they handle: <strong>floating-point embeddings</strong> (also known as floating point vectors or dense vectors), <strong>binary embeddings</strong> (also known as binary vectors), and <strong>sparse embeddings</strong> (also known as sparse vectors).</p>
 <div class="filter">
   <a href="#floating">Floating-point embeddings</a>
   <a href="#binary">Binary embeddings</a>
   <a href="#sparse">Sparse embeddings</a>
 </div>
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-<h3 id="Indexes-for-floating-point-embeddings" class="common-anchor-header">Indexes for floating-point embeddings</h3><p>For 128-dimensional floating-point embeddings, the storage they take up is 128 * the size of float = 512 bytes. And the <a href="/docs/metric.md">distance metrics</a> used for float-point embeddings are Euclidean distance (<code translate="no">L2</code>) and Inner product (<code translate="no">IP</code>).</p>
+<h3 id="Indexes-for-floating-point-embeddings" class="common-anchor-header">Indexes for floating-point embeddings</h3><p>For 128-dimensional floating-point embeddings (vectors), the storage they take up is 128 * the size of float = 512 bytes. And the <a href="/docs/metric.md">distance metrics</a> used for float-point embeddings are Euclidean distance (<code translate="no">L2</code>) and Inner product (<code translate="no">IP</code>).</p>
 <p>These types of indexes include <code translate="no">FLAT</code>, <code translate="no">IVF_FLAT</code>, <code translate="no">IVF_PQ</code>, <code translate="no">IVF_SQ8</code>, <code translate="no">HNSW</code>, and <code translate="no">SCANN</code> for CPU-based ANN searches.</p>
 </div>
 <div class="filter-binary">

localization/v2.4.x/site/en/reference/metric.md

@@ -21,7 +21,7 @@ title: Similarity Metrics
         ></path>
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     </button></h1><p>In Milvus, similarity metrics are used to measure similarities among vectors. Choosing a good distance metric helps improve the classification and clustering performance significantly.</p>
-<p>The following table shows how these widely used similarity metrics fit with various input data forms and Milvus indexes.</p>
+<p>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).</p>
 <div class="filter">
   <a href="#floating">Floating point embeddings</a>
   <a href="#binary">Binary embeddings</a>
@@ -88,7 +88,7 @@ title: Similarity Metrics
 <div class="alert note">
 Milvus only caculates the value before applying square root when Euclidean distance is chosen as the distance metric.
 </div>
-<h3 id="Inner-product-IP" class="common-anchor-header">Inner product (IP)</h3><p>The IP distance between two embeddings are defined as follows:</p>
+<h3 id="Inner-product-IP" class="common-anchor-header">Inner product (IP)</h3><p>The IP distance between two vector embeddings are defined as follows:</p>
 <p>
   <span class="img-wrapper">
     <img translate="no" src="/docs/v2.4.x/assets/IP_formula.png" alt="ip" class="doc-image" id="ip" />