chore: enable librarian generation for google-cloud-vectorsearch

[jskeet]

No description provided.

[gemini-code-assist]

Code Review

This pull request removes the skip_generate flag for the Vector Search API in librarian.yaml and modifies the formatting of .repo-metadata.json. Feedback was provided to revert the indentation changes from 4 spaces back to 2 spaces and restore the trailing newline in .repo-metadata.json to maintain consistency with the repository standards and POSIX compliance.

[gemini-code-assist]

The indentation in this file has been changed from 2 spaces to 4 spaces, and the trailing newline has been removed. To maintain consistency with other packages in this repository and adhere to POSIX standards, please revert to 2-space indentation and ensure the file ends with a newline.

Suggested change

	{
	"api_description": "The Vector Search API provides a fully-managed, highly performant, and\nscalable vector database designed to power next-generation search,\nrecommendation, and generative AI applications. It allows you to store,\nindex, and query your data and its corresponding vector embeddings through\na simple, intuitive interface. With Vector Search, you can define custom\nschemas for your data, insert objects with associated metadata,\nautomatically generate embeddings from your data, and perform fast\napproximate nearest neighbor (ANN) searches to find semantically similar\nitems at scale.",
	"api_id": "vectorsearch.googleapis.com",
	"api_shortname": "vectorsearch",
	"client_documentation": "https://cloud.google.com/python/docs/reference/google-cloud-vectorsearch/latest",
	"default_version": "v1",
	"distribution_name": "google-cloud-vectorsearch",
	"issue_tracker": "https://issuetracker.google.com/issues/new?component=1899904",
	"language": "python",
	"library_type": "GAPIC_AUTO",
	"name": "google-cloud-vectorsearch",
	"name_pretty": "Vector Search API",
	"product_documentation": "https://docs.cloud.google.com/vertex-ai/docs/vector-search-2/overview",
	"release_level": "preview",
	"repo": "googleapis/google-cloud-python"
	}
	"api_description": "The Vector Search API provides a fully-managed, highly performant, and\nscalable vector database designed to power next-generation search,\nrecommendation, and generative AI applications. It allows you to store,\nindex, and query your data and its corresponding vector embeddings through\na simple, intuitive interface. With Vector Search, you can define custom\nschemas for your data, insert objects with associated metadata,\nautomatically generate embeddings from your data, and perform fast\napproximate nearest neighbor (ANN) searches to find semantically similar\nitems at scale.",
	"api_id": "vectorsearch.googleapis.com",
	"api_shortname": "vectorsearch",
	"client_documentation": "https://cloud.google.com/python/docs/reference/google-cloud-vectorsearch/latest",
	"default_version": "v1",
	"distribution_name": "google-cloud-vectorsearch",
	"issue_tracker": "https://issuetracker.google.com/issues/new?component=1899904",
	"language": "python",
	"library_type": "GAPIC_AUTO",
	"name": "google-cloud-vectorsearch",
	"name_pretty": "Vector Search API",
	"product_documentation": "https://docs.cloud.google.com/vertex-ai/docs/vector-search-2/overview",
	"release_level": "preview",
	"repo": "googleapis/google-cloud-python"
	}
	{
	"api_description": "The Vector Search API provides a fully-managed, highly performant, and\nscalable vector database designed to power next-generation search,\nrecommendation, and generative AI applications. It allows you to store,\nindex, and query your data and its corresponding vector embeddings through\na simple, intuitive interface. With Vector Search, you can define custom\nschemas for your data, insert objects with associated metadata,\nautomatically generate embeddings from your data, and perform fast\napproximate nearest neighbor (ANN) searches to find semantically similar\nitems at scale.",
	"api_id": "vectorsearch.googleapis.com",
	"api_shortname": "vectorsearch",
	"client_documentation": "https://cloud.google.com/python/docs/reference/google-cloud-vectorsearch/latest",
	"default_version": "v1",
	"distribution_name": "google-cloud-vectorsearch",
	"issue_tracker": "https://issuetracker.google.com/issues/new?component=1899904",
	"language": "python",
	"library_type": "GAPIC_AUTO",
	"name": "google-cloud-vectorsearch",
	"name_pretty": "Vector Search API",
	"product_documentation": "https://docs.cloud.google.com/vertex-ai/docs/vector-search-2/overview",
	"release_level": "preview",
	"repo": "googleapis/google-cloud-python"
	}

[chalmerlowe]

LGTM