.Net: Support for configuring dimensions in Google AI embeddings generation

[ArieSLV]

Motivation and Context

This change addresses a limitation in the current implementation of the Google AI embeddings generation service in Semantic Kernel. Currently, users cannot configure the output dimensionality of the embeddings, even though the underlying Google AI API supports specifying the number of dimensions via the output_dimensionality parameter.

Why is this change required?
Allowing configuration of the dimensions provides greater flexibility for users to tailor the embeddings to their specific use cases—whether for optimizing memory usage, improving performance, or ensuring compatibility with downstream systems that expect a particular embedding size.

What problem does it solve?
It solves the issue of inflexibility by exposing the dimensions parameter in the service constructors, builder methods, and API request payloads. This ensures that developers can leverage the full capabilities of the Google API without being limited to the default embedding size.

What scenario does it contribute to?
This feature is particularly useful in scenarios where:

• Users need to optimize storage or computational resources.
• Downstream tasks or integrations require embeddings of a specific dimensionality.
• Fine-tuning the model output is essential for performance or compatibility reasons.

Relevant issue link: #10488

Description

This PR introduces support for specifying the output dimensionality in the Google AI embeddings generation workflow. The main changes include:

• Service Constructor Update:
  The GoogleAITextEmbeddingGenerationService constructor now accepts an optional dimensions parameter, which is then forwarded to the lower-level client implementations.

• Builder and Extension Methods:
  Extension methods such as AddGoogleAIEmbeddingGeneration have been updated to accept a dimensions parameter. This allows developers to configure the embedding dimensions using the builder pattern.

• Request Payload Enhancement:
  The GoogleAIEmbeddingRequest class now includes a new optional property Dimensions (serialized as output_dimensionality). When provided, this value is included in the JSON payload sent to the Google AI API.

• Metadata and Attributes Update:
  The service’s metadata now reflects the provided dimensions, ensuring consistency in configuration tracking.

• Unit Testing:
  New unit tests have been added to confirm that:

  • When a dimensions value is provided, it is correctly included in the JSON request.
  • When not provided, the default behavior remains unchanged.

This enhancement maintains backward compatibility since the new parameter is optional. Existing implementations that do not specify a dimension will continue to work as before.

Contribution Checklist

• The code builds clean without any errors or warnings.
• The PR follows the SK Contribution Guidelines and the pre-submission formatting script raises no violations.
• All unit tests pass, and I have added new tests where possible.
• I didn't break anyone 😄

[ArieSLV]

Hi team,
I see the Contributor License Agreement (CLA) message and understand that I need to agree to it before my submission can be processed. I've reached out to my manager for approval to sign the CLA under my employer’s name. Once I receive the necessary clearance, I will reply accordingly.
I appreciate your patience.

[ArieSLV]

@microsoft-github-policy-service agree

[ArieSLV]

Hi team,
The Contributor License Agreement issue has been resolved. Are any further actions required from me to get a review?

[RogerBarreto]

This is very specific for Embeddings, not a good candidate to live in the IAIService level. Consider changing it to ITextEmbeddingsGeneration interface.

Same for the DimensionsKey above.

Suggested change

public static int? GetDimensions(this IAIService service)

[RogerBarreto]

Please also add tests checking that the RequestContent generated the right property in the body before calling the API.

like:

1. Dimensions = null don't find output_dimensionality property in the request
2. Dimensions = 2 should find output_dimensionality property in the request with value = 2.

Similar to this:

semantic-kernel/dotnet/src/Connectors/Connectors.OpenAI.UnitTests/Services/OpenAITextToImageServiceTests.cs

Line 120 in 4fdaf67

public async Task GetUriImageContentsImageQualityRequestWorksCorrectlyAsync(string? quality, string? expectedQuality)

[RogerBarreto]

Hi @ArieSLV thanks for you contributions, most of it looking good so far.

• Some small spell check errors to fix.
• Missing Integration tests, ensure we have one integration test (can be marked as skip but working in your local environment tests`.
• Add a sample to the Concepts\Memory\Google_EmbeddingGeneration.cs for this update, will be important exploring this new feature. Similar how we have here: OpenAI_EmbeddingGeneration.cs