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Remove vector dimension from examples #2597

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42 changes: 21 additions & 21 deletions cds/types.md
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Expand Up @@ -13,27 +13,27 @@ status: released
The following table lists the built-in types in CDS, and their most common mapping to
ANSI SQL types, when deployed to a relational database (concrete mappings to specific databases may differ):

| CDS Type | Remarks | ANSI SQL |
|---------------------|------------------------------------------------------------------------|----------------|
| `UUID` | [RFC 4122](https://tools.ietf.org/html/rfc4122)-compliant UUIDs | _NVARCHAR(36)_ |
| `Boolean` | Values: `true`, `false`, `null`, `0`, `1` | _BOOLEAN_ |
| `Integer` | Same as `Int32` by default | _INTEGER_ |
| `Int16` | Signed 16-bit integer, range *[ -2<sup>15</sup> ... +2<sup>15</sup> )* | _SMALLINT_ |
| `Int32` | Signed 32-bit integer, range *[ -2<sup>31</sup> ... +2<sup>31</sup> )* | _INTEGER_ |
| `Int64` | Signed 64-bit integer, range *[ -2<sup>63</sup> ... +2<sup>63</sup> )* | _BIGINT_ |
| `UInt8` | Unsigned 8-bit integer, range *[ 0 ... 255 ]* | _TINYINT_ |
| `Decimal`(`p`,`s`) | Decimal with precision `p` and scale `s` | _DECIMAL_ |
| `Double` | Floating point with binary mantissa | _DOUBLE_ |
| `Date` | e.g. `2022-12-31` | _DATE_ |
| `Time` | e.g. `23:59:59` | _TIME_ |
| `DateTime` | _sec_ precision | _TIMESTAMP_ |
| `Timestamp` | _µs_ precision, with up to 7 fractional digits | _TIMESTAMP_ |
| `String` (`length`) | Default *length*: 255; on HANA: 5000 | _NVARCHAR_ |
| `Binary` (`length`) | Default *length*: 255; on HANA: 5000 | _VARBINARY_ |
| `Vector` (`length`) | for Vector Embeddings [-> see notes below](#vector-embeddings) | ( _DB-specific_ ) |
| `LargeBinary` | Unlimited binary data, usually streamed at runtime | _BLOB_ |
| `LargeString` | Unlimited textual data, usually streamed at runtime | _NCLOB_ |
| `Map` | Mapped to *NCLOB* for HANA. | *JSON* type |
| CDS Type | Remarks | ANSI SQL |
|------------------------|------------------------------------------------------------------------|----------------|
| `UUID` | [RFC 4122](https://tools.ietf.org/html/rfc4122)-compliant UUIDs | _NVARCHAR(36)_ |
| `Boolean` | Values: `true`, `false`, `null`, `0`, `1` | _BOOLEAN_ |
| `Integer` | Same as `Int32` by default | _INTEGER_ |
| `Int16` | Signed 16-bit integer, range *[ -2<sup>15</sup> ... +2<sup>15</sup> )* | _SMALLINT_ |
| `Int32` | Signed 32-bit integer, range *[ -2<sup>31</sup> ... +2<sup>31</sup> )* | _INTEGER_ |
| `Int64` | Signed 64-bit integer, range *[ -2<sup>63</sup> ... +2<sup>63</sup> )* | _BIGINT_ |
| `UInt8` | Unsigned 8-bit integer, range *[ 0 ... 255 ]* | _TINYINT_ |
| `Decimal`(`p`,`s`) | Decimal with precision `p` and scale `s` | _DECIMAL_ |
| `Double` | Floating point with binary mantissa | _DOUBLE_ |
| `Date` | e.g. `2022-12-31` | _DATE_ |
| `Time` | e.g. `23:59:59` | _TIME_ |
| `DateTime` | _sec_ precision | _TIMESTAMP_ |
| `Timestamp` | _µs_ precision, with up to 7 fractional digits | _TIMESTAMP_ |
| `String` (`length`) | Default *length*: 255; on HANA: 5000 | _NVARCHAR_ |
| `Binary` (`length`) | Default *length*: 255; on HANA: 5000 | _VARBINARY_ |
| `Vector` (`dimension`) | for Vector Embeddings [-> see notes below](#vector-embeddings) | ( _DB-specific_ ) |
| `LargeBinary` | Unlimited binary data, usually streamed at runtime | _BLOB_ |
| `LargeString` | Unlimited textual data, usually streamed at runtime | _NCLOB_ |
| `Map` | Mapped to *NCLOB* for HANA. | *JSON* type |

> [!info] Default String Lengths
> Lengths can be omitted, in which case default lengths are used. While this is usual in initial phases of a project, productive apps should always use explicitly defined length. The respective default lengths are configurable through the config options
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6 changes: 3 additions & 3 deletions guides/databases/vector-embeddings.md
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Expand Up @@ -12,11 +12,11 @@ Choose an embedding model that fits your use case and data (for example English
Use the [SAP Generative AI Hub](https://www.sap.com/products/artificial-intelligence/generative-ai-hub.html) for unified consumption of embedding models and LLMs across different vendors and open-source models. Check for available models on the [SAP AI Launchpad](https://help.sap.com/docs/ai-launchpad/sap-ai-launchpad-user-guide/models-and-scenarios-in-generative-ai-hub-fef463b24bff4f44a33e98bb1e4f3148#models).

## Add Embeddings to Your CDS Model
Use the built-in CDL [Vector type](../../cds/types) in your CDS model to store embeddings. Set the vector dimensions to match the embedding model (for example, 768 for *SAP_GXY.20250407*).
Use the built-in CDL [Vector type](../../cds/types) to store embeddings. Use `Vector` without specifying a dimension to simplify changing the embedding model, or make sure the vector dimension matches the embedding model (for example, 768 for *SAP_GXY.20250407*).

```cds
extend Incidents with {
embedding : Vector(768);
embedding : Vector;
}
```

Expand All @@ -34,7 +34,7 @@ To generate vector embeddings on write in SAP HANA, you can use the [vector_embe
```cds
extend Incidents with {
@cds.api.ignore
embedding : Vector(768) = vector_embedding(
embedding : Vector = vector_embedding(
'Title: ' || title || ', Summary: ' || summary,
'DOCUMENT', 'SAP_GXY.20250407'
) stored;
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2 changes: 1 addition & 1 deletion java/cds-data.md
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Expand Up @@ -330,7 +330,7 @@ Map data can be nested and may contain nested maps and lists, which are serializ

## Vector Embeddings { #vector-embeddings }

In CDS [vector embeddings](../guides/databases/vector-embeddings) are stored in elements of type `cds.Vector`:
In CDS [vector embeddings](../guides/databases/vector-embeddings) are stored in elements of type `Vector`:

CAP Java support the vector type on SAP HANA, as well as H2 and SQLite for local testing. On Postgres (beta) support for vectors requires the [pgvector](https://github.com/pgvector/pgvector) extension.

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2 changes: 1 addition & 1 deletion java/working-with-cql/query-api.md
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Expand Up @@ -1658,7 +1658,7 @@ To automatically generate vector embeddings on write in the database, you can de
```cds
extend Incidents with {
@cds.api.ignore
embedding : cds.Vector(768) = vector_embedding(
embedding : Vector = vector_embedding(
'title: ' || title || ', summary: ' || summary,
'DOCUMENT', 'SAP_GXY.20250407') stored;
}
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