Add documentation for SEISMIC

_field-types/supported-field-types/boolean.md

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 layout: default
 title: Boolean
-nav_order: 20
+nav_order: 24
 has_children: false
 parent: Supported field types
 redirect_from:

_field-types/supported-field-types/derived.md

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 layout: default
 title: Derived
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_field-types/supported-field-types/index.md

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 | [`rank_feature`, `rank_features`]({{site.url}}{{site.baseurl}}/field-types/supported-field-types/rank/)    | Boosts or lowers document relevance scores.                                                                                                 |
 | [`knn_vector`]({{site.url}}{{site.baseurl}}/field-types/supported-field-types/knn-vector/)                 | Indexes a vector for k-NN search.                                                                                                         |
 | [`semantic`]({{site.url}}{{site.baseurl}}/field-types/supported-field-types/semantic/)                     | Wraps a text or binary field to simplify semantic search setup.                                                                           |
+| [`sparse_vector`]({{site.url}}{{site.baseurl}}/field-types/supported-field-types/sparse-vector/)               | Indexes a sparse vector for [neural sparse ANN search]({{site.url}}{{site.baseurl}}/vector-search/ai-search/neural-sparse-ann). |
 | [`star_tree`]({{site.url}}{{site.baseurl}}/field-types/supported-field-types/star-tree/)                   | Precomputes aggregations for faster performance using a [star-tree index](https://docs.pinot.apache.org/basics/indexing/star-tree-index). |
 
+
 ## Arrays
 
 There is no dedicated array field type in OpenSearch. Instead, you can pass an array of values into any field. All values in the array must have the same field type.

_field-types/supported-field-types/sparse-vector.md

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+---
+layout: default
+title: Sparse vector
+nav_order: 22
+has_children: false
+parent: Supported field types
+---
+
+# Sparse vector
+**Introduced 3.3**
+{: .label .label-purple }
+
+The `sparse_vector` field supports [neural sparse approximate nearest neighbor (ANN) search]({{site.url}}{{site.baseurl}}/vector-search/ai-search/neural-sparse-ann/), which improves search efficiency while preserving relevance. A `sparse_vector` is stored as a map, in which each key represents the token and each value is a positive [`float`]({{site.url}}{{site.baseurl}}/opensearch/supported-field-types/numeric/) value indicating the token's weight.
+
+## Parameters
+
+The `sparse_vector` field type supports the following parameters.
+
+| Parameter               | Type    | Required | Description                                   | Default               | Range       | 
+|-------------------------|---------|----------|-----------------------------------------------|-----------------------|-------------|
+| `name`                  | String  | Yes | The neural sparse ANN search algorithm. Valid value is `seismic`.                              | -                     | -           | 
+| `n_postings`            | Integer | No | The maximum number of documents to retain in each posting list.            | `0.0005 * doc_count`¹ | (0, ∞) | 
+| `cluster_ratio`         | Float   | No | The fraction of documents in each posting list used to determine the cluster count.             | `0.1`                 | (0, 1)      | 
+| `summary_prune_ratio`   | Float   | No | The fraction of total token weight to retain when pruning cluster summary vectors. For example, if `summary_prune_ratio` is set to `0.5`, tokens contributing to the top 50% of the total weight are kept. Thus, for a cluster summary `{"100": 1, "200": 2, "300": 3, "400": 6}`, the pruned summary is `{"400": 6}`. | 0.4 | (0, 1] |     | `0.4`                 | (0, 1]      | 
+| `approximate_threshold` | Integer | No | The minimum number of documents in a segment required to activate neural sparse ANN search.     | `1000000`           | [0, ∞) | 
+| `quantization_ceiling_search`  | Float   | No | The maximum token weight used for quantization during search. | `16`                  | (0, ∞) | 
+| `quantization_ceiling_ingest` | Float | No | The maximum token weight used for quantization during ingestion. | `3`                   | (0, ∞)     | 
+
+
+¹`doc_count` represents the number of documents within the segment.
+
+For parameter configuration, see [Neural sparse ANN search]({{site.url}}{{site.baseurl}}/vector-search/ai-search/neural-sparse-ann).  
+{: .note }
+
+To increase search efficiency and reduce memory consumption, the `sparse_vector` field automatically performs quantization of the token weight. You can adjust the `quantization_ceiling_search` and `quantization_ceiling_ingest` parameters according to different token weight distributions. For doc-only queries, we recommend setting `quantization_ceiling_search` to the default value (`16`). For bi-encoder queries, we recommend setting `quantization_ceiling_search` to `3`. For more information about doc-only and bi-encoder query modes, see [Generating sparse vector embeddings automatically]({{site.url}}{{site.baseurl}}/vector-search/ai-search/neural-sparse-with-pipelines/).
+{: .note}
+
+## Example
+
+The following example demonstrates using a `sparse_vector` field type.
+
+### Step 1: Create an index
+
+Create a sparse index by setting `index.sparse` to `true` and define a `sparse_vector` field in the index mapping:
+
+```json
+PUT sparse-vector-index
+{
+  "settings": {
+    "index": {
+      "sparse": true
+    },
+    "mappings": {
+      "properties": {
+        "sparse_embedding": {
+          "type": "sparse_vector",
+          "method": {
+            "name": "seismic",
+            "parameters": {
+              "n_postings": 300,
+              "cluster_ratio": 0.1,
+              "summary_prune_ratio": 0.4,
+              "approximate_threshold": 1000000
+            }
+          }
+        }
+      }
+    }
+  }
+}
+```
+{% include copy-curl.html %}
+
+
+### Step 2: Ingest data into the index
+
+Ingest three documents containing `sparse_vector` fields into your index:
+
+```json
+PUT sparse-vector-index/_doc/1
+{
+  "sparse_embedding" : {
+    "1000": 0.1
+  }
+}
+```
+{% include copy-curl.html %}
+
+```json
+PUT sparse-vector-index/_doc/2
+{
+  "sparse_embedding" : {
+    "2000": 0.2
+  }
+}
+```
+{% include copy-curl.html %}
+
+```json
+PUT sparse-vector-index/_doc/3
+{
+  "sparse_embedding" : {
+    "3000": 0.3
+  }
+}
+```
+{% include copy-curl.html %}
+
+### Step 3: Search the index
+
+You can query the sparse index by providing either raw vectors or natural language using a [`neural_sparse` query]({{site.url}}{{site.baseurl}}/query-dsl/specialized/neural-sparse/).
+
+#### Query using a raw vector
+
+To query using a raw vector, provide the `query_tokens` parameter:
+
+```json
+GET sparse-vector-index/_search
+{
+  "query": {
+    "neural_sparse": {
+      "sparse_embedding": {
+        "query_tokens": {
+          "1055": 5.5
+        },
+        "method_parameters": {
+          "heap_factor": 1.0,
+          "top_n": 10,
+          "k": 10
+        }
+      }
+    }
+  }
+}
+```
+{% include copy-curl.html %}
+
+#### Query using natural language
+
+To query using natural language, provide the `query_text` and `model_id` parameters:
+
+```json
+GET sparse-vector-index/_search
+{
+  "query": {
+    "neural_sparse": {
+      "sparse_embedding": {
+        "query_text": "<input text>",
+        "model_id": "<model ID>",
+        "method_parameters": {
+          "k": 10,
+          "top_n": 10,
+          "heap_factor": 1.0
+        }
+      }
+    }
+  }
+}
+```
+{% include copy-curl.html %}
+
+## Related pages
+
+- [Neural sparse ANN search]({{site.url}}{{site.baseurl}}/vector-search/ai-search/neural-sparse-ann/)
+- [Neural sparse query]({{site.url}}{{site.baseurl}}/query-dsl/specialized/neural-sparse/)
+- [Neural sparse ANN search performance tuning]({{site.url}}{{site.baseurl}}/vector-search/performance-tuning-sparse/)

_field-types/supported-field-types/star-tree.md

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 layout: default
 title: Star-tree
-nav_order: 61
+nav_order: 62
 parent: Supported field types
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