Notebook - OpenSearch embedding enrichment #102
Merged
andy-k-improving merged 7 commits intomainfrom Mar 24, 2026
Merged
Conversation
acarbonetto
reviewed
Mar 23, 2026
acarbonetto
approved these changes
Mar 23, 2026
Contributor
acarbonetto
left a comment
acarbonetto
left a comment
There was a problem hiding this comment.
Looks great. Thanks Andy.
| "\n", | ||
| "The connector does not yet support the native knn_vector field type in OpenSearch. Queries against indices containing knn_vector fields may fail during schema resolution.\n", | ||
| "\n", | ||
| "Support for the native knn_vector type is currently under development.\n", |
Contributor
There was a problem hiding this comment.
you can link to the issue and then when it's complete, we can update the notebook
Diff
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
2 participants
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
Summary 📝
Write an overview about it.
This PR aims to introduce a new notebook to demonstrate how to perform embedding data enrichment with OpenSearch service.
Also a small update has been made on util.py to simplify the embedding handling.
Support for knn_vector:
awslabs/aws-athena-query-federation#3315
awslabs/aws-athena-query-federation#3316
Test plan:
Permissions
By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.