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Currently neural-search text_image_processor allows a single document field to be defined for each image and text mapping. A single field can be defined to store embedding in OpenSearch. Example of processor definition:
It should be possible to define multiple field pairs for image, text or image+text. It should be possible to define an OpenSearch field that stores embedding for a model. Request may look something like:
Today it's possible to define multiple embedding processors as part of a single pipeline, and each processor may have it's own definition of mapping and embedding field.
Alternative with multiple processors is harder to maintain then a single processor entry. For instance, if one needs to update model id it should be done in X places vs a single place. Another concern is the performance, having a multiple processors adding some overhead that may be critical in systems with low SLA for search response.
martin-gaievski
changed the title
[FEATURE] Multiple embeddings in one data ingestion request
[FEATURE] Support for multiple field mappings in a single text-image embedding processor
Nov 19, 2024
Is your feature request related to a problem?
Currently neural-search text_image_processor allows a single document field to be defined for each image and text mapping. A single field can be defined to store embedding in OpenSearch. Example of processor definition:
What solution would you like?
It should be possible to define multiple field pairs for image, text or image+text. It should be possible to define an OpenSearch field that stores embedding for a model. Request may look something like:
What alternatives have you considered?
Today it's possible to define multiple embedding processors as part of a single pipeline, and each processor may have it's own definition of mapping and embedding field.
Do you have any additional context?
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