[ML] Integrate SageMaker with OpenAI Embeddings (#126856)#127610
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elasticsearchmachine merged 1 commit intoelastic:8.19from May 1, 2025
Merged
[ML] Integrate SageMaker with OpenAI Embeddings (#126856)#127610elasticsearchmachine merged 1 commit intoelastic:8.19from
elasticsearchmachine merged 1 commit intoelastic:8.19from
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Integrating with SageMaker. Current design: - SageMaker accepts any byte payload, which can be text, csv, or json. `api` represents the structure of the payload that we will send, for example `openai`, `elastic`, `common`, probably `cohere` or `huggingface` as well. - `api` implementations are extensions of `SageMakerSchemaPayload`, which supports: - "extra" service and task settings specific to the payload structure, so `cohere` would require `embedding_type` and `openai` would require `dimensions` in the `service_settings` - conversion logic from model, service settings, task settings, and input to `SdkBytes` - conversion logic from responding `SdkBytes` to `InferenceServiceResults` - Everything else is tunneling, there are a number of base `service_settings` and `task_settings` that are independent of the api format that we will store and set - We let the SDK do the bulk of the work in terms of connection details, rate limiting, retries, etc.
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>enhancement
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Integrating with SageMaker.
Current design:
apirepresents the structure of the payload that we will send, for exampleopenai,elastic,common, probablycohereorhuggingfaceas well.apiimplementations are extensions ofSageMakerSchemaPayload, which supports:coherewould requireembedding_typeandopenaiwould requiredimensionsin theservice_settingsSdkBytesSdkBytestoInferenceServiceResultsservice_settingsandtask_settingsthat are independent of the api format that we will store and set