…13229)…ync support### Motivation and Context<!-- Thank you for your contribution to the semantic-kernel repo!Please help reviewers and future users, providing the followinginformation:  1. Why is this change required?  2. What problem does it solve?  3. What scenario does it contribute to?  4. If it fixes an open issue, please link to the issue here.-->This change is required to enable Semantic Kernel users to store andretrieve embeddings using Oracle databases. Currently, Semantic Kernelsupports vector storage for several backends, but Oracle was missing.This connector solves that gap by providing full async support, nativeVECTOR type handling, and vector index management.### Description<!-- Describe your changes, the overall approach, the underlying design.These notes will help understanding how your code works. Thanks! -->This PR introduces a new Oracle connector for Semantic Kernel with thefollowing features:- Asynchronous upsert, get, delete and search operations for memoryrecords.- Native Oracle VECTOR type support for storing embeddings efficiently.- Support for HNSW and IVFFLAT vector indexes for similarity search.- Integration with Semantic Kernel collections, enabling semantic searchand memory operations.- Comprehensive unit tests to ensure correctness and stability.The connector is designed to work seamlessly with existing SemanticKernel memory abstractions and follows the same async patterns as othervector stores.Integration tests have also been implemented and verified locally;however, they are not included in this PR because the current CIenvironment setup for Oracle Database support is unknown.Once guidance is provided on Oracle DB availability in the CI pipeline,integration tests can be enabled and added in a follow-up PR.### Contribution Checklist<!-- Before submitting this PR, please make sure: -->- [x] The code builds clean without any errors or warnings- [x] The PR follows the [SK ContributionGuidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md)and the [pre-submission formattingscript](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts)raises no violations- [x] All unit tests pass, and I have added new tests where possible- [x] I didn't break anyone 😄

) (#13277)### Motivation and ContextThis PR fixes issue[#13250](#13250)by ensuring that the Google AI embedding connector correctly includesthe taskType field in outgoing requests when EmbeddingGenerationOptionscontains a task_type value.Previously, the connector ignored this field, causing task-specificembeddings (RETRIEVAL_DOCUMENT, RETRIEVAL_QUERY, etc.) to default togeneric embeddings.### DescriptionChangesExtended GoogleAIEmbeddingRequest.FromData() to include an optionaltaskType parameter.Updated GoogleAIEmbeddingClient to extract task_type fromEmbeddingGenerationOptions.AdditionalProperties.Added a new overload in GoogleAITextEmbeddingGenerationService to passEmbeddingGenerationOptions while maintaining backward compatibility.Added unit test FromData_Should_Include_TaskType_When_Provided to verifythat "taskType" appears correctly in serialized JSON.### Contribution Checklist<!-- Before submitting this PR, please make sure: -->- [ Done] The code builds clean without any errors or warnings- [ Done] The PR follows the [SK ContributionGuidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md)and the [pre-submission formattingscript](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts)raises no violations- [ Done] All unit tests pass, and I have added new tests where possible- [ Done] I didn't break anyone 😄---------Co-authored-by: Mark Wallace <127216156+markwallace-microsoft@users.noreply.github.com>Co-authored-by: Roger Barreto <19890735+rogerbarreto@users.noreply.github.com>