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+---
+layout: default
+title: Disk-based vector search
+nav_order: 16
+parent: k-NN search
+has_children: false
+---
+
+# Disk-based vector search
+**Introduced 2.17**
+{: .label .label-purple}
+
+For low-memory environments, OpenSearch provides _disk-based vector search_, which significantly reduces the operational costs for vector workloads. Disk-based vector search uses [binary quantization]({{site.url}}{{site.baseurl}}/search-plugins/knn/knn-vector-quantization/#binary-quantization), compressing vectors and thereby reducing the memory requirements. This memory optimization provides large memory savings at the cost of slightly increased search latency while still maintaining strong recall.
+
+To use disk-based vector search, set the [`mode`]({{site.url}}{{site.baseurl}}/field-types/supported-field-types/knn-vector/#vector-workload-modes) parameter to `on_disk` for your vector field type. This parameter will configure your index to use secondary storage. 
+
+## Creating an index for disk-based vector search
+
+To create an index for disk-based vector search, send the following request:
+
+```json
+PUT my-vector-index
+{
+  "mappings": {
+    "properties": {
+      "my_vector_field": {
+        "type": "knn_vector",
+        "dimension": 8,
+        "space_type": "innerproduct",
+        "data_type": "float",
+        "mode": "on_disk"
+      }
+    }
+  }
+}
+```
+{% include copy-curl.html %}
+
+By default, the `on_disk` mode configures the index to use the `faiss` engine and `hnsw` method. The default [`compression_level`]({{site.url}}{{site.baseurl}}/field-types/supported-field-types/knn-vector/#compression-levels) of `32x` reduces the amount of memory the vectors require by a factor of 32. To preserve the search recall, rescoring is enabled by default. A search on a disk-optimized index runs in two phases: The compressed index is searched first, and then the results are rescored using full-precision vectors loaded from disk.
+
+To reduce the compression level, provide the `compression_level` parameter when creating the index mapping: 
+
+```json
+PUT my-vector-index
+{
+  "mappings": {
+    "properties": {
+      "my_vector_field": {
+        "type": "knn_vector",
+        "dimension": 8,
+        "space_type": "innerproduct",
+        "data_type": "float",
+        "mode": "on_disk",
+        "compression_level": "16x"
+      }
+    }
+  }
+}
+```
+{% include copy-curl.html %}
+
+For more information about the `compression_level` parameter, see [Compression levels]({{site.url}}{{site.baseurl}}/field-types/supported-field-types/knn-vector/#compression-levels). Note that for `4x` compression, the `lucene` engine will be used.
+{: .note}
+
+If you need more granular fine-tuning, you can override additional k-NN parameters in the method definition. For example, to improve recall, increase the `ef_construction` parameter value:
+
+```json
+PUT my-vector-index
+{
+  "mappings": {
+    "properties": {
+      "my_vector_field": {
+        "type": "knn_vector",
+        "dimension": 8,
+        "space_type": "innerproduct",
+        "data_type": "float",
+        "mode": "on_disk",
+        "method": {
+          "params": {
+            "ef_construction": 512
+          }
+        }
+      }
+    }
+  }
+}
+```
+{% include copy-curl.html %}
+
+The `on_disk` mode only works with the `float` data type.
+{: .note}
+
+## Ingestion
+
+You can perform document ingestion for a disk-optimized vector index in the same way as for a regular vector index. To index several documents in bulk, send the following request:
+
+```json
+POST _bulk
+{ "index": { "_index": "my-vector-index", "_id": "1" } }
+{ "my_vector_field": [1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5], "price": 12.2 }
+{ "index": { "_index": "my-vector-index", "_id": "2" } }
+{ "my_vector_field": [2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5], "price": 7.1 }
+{ "index": { "_index": "my-vector-index", "_id": "3" } }
+{ "my_vector_field": [3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5], "price": 12.9 }
+{ "index": { "_index": "my-vector-index", "_id": "4" } }
+{ "my_vector_field": [4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5], "price": 1.2 }
+{ "index": { "_index": "my-vector-index", "_id": "5" } }
+{ "my_vector_field": [5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5], "price": 3.7 }
+{ "index": { "_index": "my-vector-index", "_id": "6" } }
+{ "my_vector_field": [6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5], "price": 10.3 }
+{ "index": { "_index": "my-vector-index", "_id": "7" } }
+{ "my_vector_field": [7.5, 7.5, 7.5, 7.5, 7.5, 7.5, 7.5, 7.5], "price": 5.5 }
+{ "index": { "_index": "my-vector-index", "_id": "8" } }
+{ "my_vector_field": [8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5], "price": 4.4 }
+{ "index": { "_index": "my-vector-index", "_id": "9" } }
+{ "my_vector_field": [9.5, 9.5, 9.5, 9.5, 9.5, 9.5, 9.5, 9.5], "price": 8.9 }
+```
+{% include copy-curl.html %}
+
+## Search
+
+Search is also performed in the same way as in other index configurations. The key difference is that, by default, the `oversample_factor` of the rescore parameter is set to `3.0` (unless you override the `compression_level`). For more information, see [Rescoring quantized results using full precision]({{site.url}}{{site.baseurl}}/search-plugins/knn/approximate-knn/#rescoring-quantized-results-using-full-precision). To perform vector search on a disk-optimized index, provide the search vector:
+
+```json
+GET my-vector-index/_search
+{
+  "query": {
+    "knn": {
+      "my_vector_field": {
+        "vector": [1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5],
+        "k": 5
+      }
+    }
+  }
+}
+```
+{% include copy-curl.html %}
+
+Similarly to other index configurations, you can override k-NN parameters in the search request:
+
+```json
+GET my-vector-index/_search
+{
+  "query": {
+    "knn": {
+      "my_vector_field": {
+        "vector": [1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5],
+        "k": 5,
+        "method_params": {
+            "ef_search": 512
+        },
+        "rescore": {
+            "oversample_factor": 10.0
+        }
+      }
+    }
+  }
+}
+```
+{% include copy-curl.html %}
+
+[Radial search]({{site.url}}{{site.baseurl}}/search-plugins/knn/radial-search-knn/) does not support disk-based vector search.
+{: .note}
+
+## Model-based indexes
+
+For [model-based indexes]({{site.url}}{{site.baseurl}}/search-plugins/knn/approximate-knn/#building-a-k-nn-index-from-a-model), you can specify the `on_disk` parameter in the training request in the same way that you would specify it during index creation. By default, `on_disk` mode will use the [Faiss IVF method]({{site.url}}{{site.baseurl}}/search-plugins/knn/knn-index/#supported-faiss-methods) and a compression level of `32x`. To run the training API, send the following request:
+
+```json
+POST /_plugins/_knn/models/_train/test-model
+{
+    "training_index": "train-index-name",
+    "training_field": "train-field-name",
+    "dimension": 8,
+    "max_training_vector_count": 1200,
+    "search_size": 100,
+    "description": "My model",
+    "space_type": "innerproduct",
+    "mode": "on_disk"
+}
+```
+{% include copy-curl.html %}
+
+This command assumes that training data has been ingested into the `train-index-name` index. For more information, see [Building a k-NN index from a model]({{site.url}}{{site.baseurl}}/search-plugins/knn/approximate-knn/#building-a-k-nn-index-from-a-model).
+{: .note}
+
+You can override the `compression_level` for disk-optimized indexes in the same way as for regular k-NN indexes.
+
+
+## Next steps
+
+- For more information about binary quantization, see [Binary quantization]({{site.url}}{{site.baseurl}}/search-plugins/knn/knn-vector-quantization/#binary-quantization).
+- For more information about k-NN vector workload modes, see [Vector workload modes]({{site.url}}{{site.baseurl}}/field-types/supported-field-types/knn-vector/#vector-workload-modes).
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