add VECTOR doc

src/current/v24.2/data-types.md

@@ -33,6 +33,7 @@ Type | Description | Example
 [`TSQUERY`]({% link {{ page.version.version }}/tsquery.md %}) | A list of lexemes and operators used in [full-text search]({% link {{ page.version.version }}/full-text-search.md %}). | `'list' & 'lexem' & 'oper' & 'use' & 'full' & 'text' & 'search'`
 [`TSVECTOR`]({% link {{ page.version.version }}/tsvector.md %}) | A list of lexemes with optional integer positions and weights used in [full-text search]({% link {{ page.version.version }}/full-text-search.md %}). | `'full':13 'integ':7 'lexem':4 'list':2 'option':6 'posit':8 'search':15 'text':14 'use':11 'weight':10`
 [`UUID`]({% link {{ page.version.version }}/uuid.md %}) | A 128-bit hexadecimal value. | `7f9c24e8-3b12-4fef-91e0-56a2d5a246ec`
+[`VECTOR`]({% link {{ page.version.version }}/vector.md %}) | A fixed-length array of floating-point numbers. | `[1.0, 0.0, 0.0]`
 
 ## Data type conversions and casts
 

src/current/v24.2/vector.md

@@ -0,0 +1,92 @@
+---
+title: VECTOR
+summary: The VECTOR data type stores fixed-length arrays of floating-point numbers, which represent data points in multi-dimensional space.
+toc: true
+docs_area: reference.sql
+---
+
+{{site.data.alerts.callout_info}}
+{% include feature-phases/preview.md %}
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+
+The `VECTOR` data type stores fixed-length arrays of floating-point numbers, which represent data points in multi-dimensional space. Vector search is often used in AI applications such as Large Language Models (LLMs) that rely on vector representations. 
+
+For details on valid `VECTOR` comparison operators, refer to [Syntax](#syntax). For the list of supported `VECTOR` functions, refer to [Functions and Operators]({% link {{ page.version.version }}/functions-and-operators.md %}#pgvector-functions).
+
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+`VECTOR` functionality is compatible with the [`pgvector`](https://github.com/pgvector/pgvector) extension for PostgreSQL. Vector indexing is **not** supported at this time.
+{{site.data.alerts.end}}
+
+## Syntax
+
+A `VECTOR` value is expressed as an [array]({% link {{ page.version.version }}/array.md %}) of [floating-point numbers]({% link {{ page.version.version }}/float.md %}). The array size corresponds to the number of `VECTOR` dimensions. For example, the following `VECTOR` has 3 dimensions:
+
+~~~
+[1.0, 0.0, 0.0]
+~~~
+
+You can specify the dimensions when defining a `VECTOR` column. This will enforce the number of dimensions in the column values. For example:
+
+~~~ sql
+ALTER TABLE foo ADD COLUMN bar VECTOR(3);
+~~~
+
+The following `VECTOR` comparison operators are valid:
+
+- `=` (equals). Compare vectors for equality in filtering and conditional queries.
+- `<>` (not equal to). Compare vectors for inequality in filtering and conditional queries.
+- `<->` (L2 distance). Calculate the Euclidean distance between two vectors, as used in [nearest neighbor search](https://en.wikipedia.org/wiki/Nearest_neighbor_search) and clustering algorithms.
+- `<#>` (negative inner product). Calculate the [inner product](https://en.wikipedia.org/wiki/Inner_product_space) of two vectors, as used in similarity searches where the inner product can represent the similarity score.
+- `<=>` (cosine distance). Calculate the [cosine distance](https://en.wikipedia.org/wiki/Cosine_similarity) between vectors, such as in text and image similarity measures where the orientation of vectors is more important than their magnitude.
+
+## Size
+
+The size of a `VECTOR` value is variable, but it's recommended to keep values under 1 MB to ensure performance. Above that threshold, [write amplification]({% link {{ page.version.version }}/architecture/storage-layer.md %}#write-amplification) and other considerations may cause significant performance degradation.  
+
+## Functions
+
+For the list of supported `VECTOR` functions, refer to [Functions and Operators]({% link {{ page.version.version }}/functions-and-operators.md %}#pgvector-functions).
+
+## Example
+
+Create a table with a `VECTOR` column, specifying `3` dimensions:
+
+{% include_cached copy-clipboard.html %}
+~~~ sql
+CREATE TABLE items (
+    category STRING,
+    vector VECTOR(3),
+    INDEX (category)
+);
+~~~
+
+Insert some sample data into the table:
+
+{% include_cached copy-clipboard.html %}
+~~~ sql
+INSERT INTO items (category, vector) VALUES
+	('electronics', '[1.0, 0.0, 0.0]'),
+	('electronics', '[0.9, 0.1, 0.0]'),
+	('furniture', '[0.0, 1.0, 0.0]'),
+	('furniture', '[0.0, 0.9, 0.1]'),
+	('clothing', '[0.0, 0.0, 1.0]');
+~~~
+
+Use the [`<->` operator](#syntax) to sort values with the `electronics` category by their similarity to `[1.0, 0.0, 0.0]`, based on geographic distance.
+
+{% include_cached copy-clipboard.html %}
+~~~ sql
+SELECT category, vector FROM items WHERE category = 'electronics' ORDER BY vector <-> '[1.0, 0.0, 0.0]' LIMIT 5;
+~~~
+
+~~~
+   category   |   vector
+--------------+--------------
+  electronics | [1,0,0]
+  electronics | [0.9,0.1,0]
+~~~
+
+## See also
+
+- [Functions and Operators]({% link {{ page.version.version }}/functions-and-operators.md %}#pgvector-functions)
+- [Data Types]({% link {{ page.version.version }}/data-types.md %})