From eaf75ca7d0bd6c785f80bf7e2898bd58d039bb56 Mon Sep 17 00:00:00 2001 From: Matthias Schur <107557548+MattSchur@users.noreply.github.com> Date: Thu, 28 May 2026 12:35:22 +0200 Subject: [PATCH 1/2] Remove vector dimension from examples --- cds/types.md | 42 +++++++++++++-------------- guides/databases/vector-embeddings.md | 6 ++-- java/cds-data.md | 2 +- java/working-with-cql/query-api.md | 2 +- 4 files changed, 26 insertions(+), 26 deletions(-) diff --git a/cds/types.md b/cds/types.md index d872358c0..c876abf6d 100644 --- a/cds/types.md +++ b/cds/types.md @@ -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 *[ -215 ... +215 )* | _SMALLINT_ | -| `Int32` | Signed 32-bit integer, range *[ -231 ... +231 )* | _INTEGER_ | -| `Int64` | Signed 64-bit integer, range *[ -263 ... +263 )* | _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 *[ -215 ... +215 )* | _SMALLINT_ | +| `Int32` | Signed 32-bit integer, range *[ -231 ... +231 )* | _INTEGER_ | +| `Int64` | Signed 64-bit integer, range *[ -263 ... +263 )* | _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 diff --git a/guides/databases/vector-embeddings.md b/guides/databases/vector-embeddings.md index 99f2822ed..ca9213e4d 100644 --- a/guides/databases/vector-embeddings.md +++ b/guides/databases/vector-embeddings.md @@ -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; } ``` @@ -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; diff --git a/java/cds-data.md b/java/cds-data.md index 3798486b1..761289f16 100644 --- a/java/cds-data.md +++ b/java/cds-data.md @@ -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. diff --git a/java/working-with-cql/query-api.md b/java/working-with-cql/query-api.md index 02261a8d7..e0516d1fc 100644 --- a/java/working-with-cql/query-api.md +++ b/java/working-with-cql/query-api.md @@ -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; } From c7e820d20ecd25560efdd0c0fa6c404fb1541eaf Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Ren=C3=A9=20Jeglinsky?= Date: Mon, 1 Jun 2026 09:33:33 +0200 Subject: [PATCH 2/2] Apply suggestion from @MattSchur Co-authored-by: Matthias Schur <107557548+MattSchur@users.noreply.github.com> --- guides/databases/vector-embeddings.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/guides/databases/vector-embeddings.md b/guides/databases/vector-embeddings.md index ca9213e4d..86bd135bb 100644 --- a/guides/databases/vector-embeddings.md +++ b/guides/databases/vector-embeddings.md @@ -12,7 +12,7 @@ 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) 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*). +Use the built-in CDL [Vector type](../../cds/types) to store embeddings. Use `Vector` without specifying a dimension to simplify changing the embedding model. If you specify a vector dimension, make sure it matches the embedding model (for example, 768 for *SAP_GXY.20250407*). ```cds extend Incidents with {