[Obs AI Assistant] Add support for dense vector embeddings in API tests#219199
[Obs AI Assistant] Add support for dense vector embeddings in API tests#219199sorenlouv merged 5 commits intoelastic:mainfrom
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Added one comment. The rest LGTM.
Thanks @sorenlouv
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| async startTrainedModelDeploymentES(deploymentId: string) { |
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In the route definition for /internal/ml//trained_models/{modelId}/deployment/_start the path param seems to be the modelId and deploymentId is an optional query param.
I'm not sure whether the /_ml/trained_models/${deploymentId}/deployment/_start definition is different from the /internal/ml/trained_models/{modelId}/deployment/_startl definition.
If it's the same, can we call this modelId instead of deploymentId for clarity?
| async startTrainedModelDeploymentES(deploymentId: string) { | |
| async startTrainedModelDeploymentES(modelId: string) { |
...on/deployment_agnostic/apis/observability/ai_assistant/knowledge_base/knowledge_base.spec.ts
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…lity/ai_assistant/knowledge_base/knowledge_base.spec.ts Co-authored-by: Viduni Wickramarachchi <viduni.ushanka@gmail.com>
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…ts (elastic#219199) This adds support for the `tiny_text_embedding` model that generated dense vector embeddings in API tests. This will be useful when we support multiple models and need to confirm that we can switch between models, and embeddings are correctly updated. Also adds some small refactorings/cleanup --------- Co-authored-by: Viduni Wickramarachchi <viduni.ushanka@gmail.com>
This adds support for the
tiny_text_embeddingmodel that generated dense vector embeddings in API tests. This will be useful when we support multiple models and need to confirm that we can switch between models, and embeddings are correctly updated.Also adds some small refactorings/cleanup