feat: add GCP Vertex AI image generation support

[altale]

Description

This PR adds OpenAI-compatible image generation translation for GCP Vertex AI with two backend paths:

• Imagen models (imagen-*) -> Vertex predict endpoint.
• Gemini image models (e.g. gemini-2.5-flash-image) -> Vertex generateContent endpoint.

Related Issues/PRs (if applicable)

#1806

Special notes for reviewers (if applicable)

Behavior Summary

• size handling:
  • 1024x1024 -> aspectRatio: "1:1" + sampleImageSize: "1K".
  • "" or "auto" -> omit explicit size fields to preserve backend default.
  • any other size -> ErrInvalidRequestBody.
• quality is not mapped and is rejected (ErrInvalidRequestBody).
• output_format supports only png and jpeg. Unsupported formats (e.g. webp) are rejected.

[nacx]

/gemini summary

[gemini-code-assist]

Summary of Changes

This pull request introduces a new feature that enables the AI Gateway to translate OpenAI-compatible image generation requests to GCP Vertex AI, supporting both Imagen and Gemini models. It includes request and response translation logic, size and format handling, and error management to ensure compatibility and proper functionality with the Vertex AI service.

Highlights

• New Feature: Adds support for GCP Vertex AI image generation, compatible with OpenAI's image generation API.
• Backend Support: Supports two Vertex AI backend paths: Imagen models (via predict endpoint) and Gemini image models (via generateContent endpoint).
• Size Handling: Handles image size conversions, mapping '1024x1024' to specific aspect ratios and sizes for the Imagen backend, while also supporting backend defaults.
• Error Handling: Rejects unsupported parameters like 'quality' and certain 'output_format' values, providing specific error messages.

Changelog

• internal/apischema/gcp/gcp.go
  • Defines the data structures for interacting with the GCP Vertex AI Imagen predict endpoint.
• internal/endpointspec/endpointspec.go
  • Adds GCP Vertex AI as a supported API schema for image generation, linking it to the new translator.
• internal/translator/imagegeneration_openai_gcpvertexai.go
  • Implements the translation logic between OpenAI's image generation API and GCP Vertex AI's Imagen and Gemini endpoints, handling request formatting, response parsing, and error conversion.
• internal/translator/imagegeneration_openai_gcpvertexai_test.go
  • Adds comprehensive unit tests for the new translator, covering request and response transformations, parameter handling, and error cases.
• site/docs/capabilities/llm-integrations/supported-endpoints.md
  • Documents the new GCP Vertex AI image generation support, providing examples for both Imagen and Gemini models.
• tests/data-plane/testupstream_test.go
  • Adds integration tests to verify the end-to-end functionality of the GCP Vertex AI image generation integration, including request translation, response parsing, and error handling.

Activity

• The pull request introduces a new translator for GCP Vertex AI image generation.
• It includes comprehensive tests to ensure the translator functions correctly.
• The documentation has been updated to reflect the new functionality.
• The pull request was summarized by /gemini.

[nacx]

/gemini review

[gemini-code-assist]

Code Review

This pull request adds support for GCP Vertex AI image generation, including both Imagen and Gemini models. The changes are well-structured, with a new translator to handle the conversion from OpenAI's API format to GCP's. The implementation correctly distinguishes between the two backend paths (predict for Imagen, generateContent for Gemini) and handles parameter mapping and response translation. The addition of comprehensive unit and end-to-end tests ensures the new functionality is robust. I have one suggestion to refactor a part of the code for better readability.

[gemini-code-assist]

This block can be refactored to improve readability and separate the logic for creating the backend request from the JSON marshaling. By using an any type for the backend request, you can avoid duplicating the marshaling and error handling logic.

o.isImagenModel = strings.HasPrefix(o.requestModel, "imagen")
	var path string
	var backendReq any
	if o.isImagenModel {
		backendReq, err = openAIToImagenRequest(openAIReq)
		if err != nil {
			return
		}
		path = buildGCPModelPathSuffix(gcpModelPublisherGoogle, string(o.requestModel), gcpMethodPredict)
	} else {
		backendReq, err = openAIToGeminiRequest(openAIReq)
		if err != nil {
			return
		}
		path = buildGCPModelPathSuffix(gcpModelPublisherGoogle, string(o.requestModel), gcpMethodGenerateContent)
	}

	newBody, err = json.Marshal(backendReq)
	if err != nil {
		err = fmt.Errorf("failed to encode request: %w", err)
		return
	}

[altale]

I prefer to keep the explicit typed branches here (ImagePredictRequest vs GenerateContentRequest) for clarity and type safety.

[codecov-commenter]

Codecov Report

❌ Patch coverage is 94.51220% with 9 lines in your changes missing coverage. Please review.
✅ Project coverage is 84.32%. Comparing base (7f77c8a) to head (1dcef4c).
⚠️ Report is 1 commits behind head on main.

Files with missing lines	Patch %	Lines
...l/translator/imagegeneration_openai_gcpvertexai.go	95.67%	4 Missing and 3 partials ⚠️
internal/endpointspec/endpointspec.go	0.00%	2 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #1905      +/-   ##
==========================================
+ Coverage   84.22%   84.32%   +0.09%     
==========================================
  Files         128      129       +1     
  Lines       17828    17992     +164     
==========================================
+ Hits        15016    15171     +155     
- Misses       1868     1874       +6     
- Partials      944      947       +3     

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[altale]

Hi @nacx. Update on the earlier CI failure in E2E Test for Inference Extensions. I re-ran the same changes in my own fork CI, and that job passed successfully:

• Fork run: https://github.com/altale/ai-gateway/actions/runs/23405829791/job/68084486137?pr=1

Also, the scope of this PR is unrelated to the inference-extension e2e logic (it mainly touches Vertex AI image generation translation paths and related tests/docs, not the inference-extension controller/e2e framework itself).
This looks more like an upstream CI environment/timing issue rather than a functional regression.

Could you please help re-run the failed upstream job for confirmation? Thank you so much.