perf: Update OpenAI Embedding with latest embedding model

[dciborow]

Related Issues/PRs

Provide an example for the user of using the latest embedding model if it is available.

What changes are proposed in this pull request?

Briefly describe the changes included in this Pull Request.

How is this patch tested?

• I have written tests (not required for typo or doc fix) and confirmed the proposed feature/bug-fix/change works.

Does this PR change any dependencies?

• No. You can skip this section.
• Yes. Make sure the dependencies are resolved correctly, and list changes here.

Does this PR add a new feature? If so, have you added samples on website?

• No. You can skip this section.
• Yes. Make sure you have added samples following below steps.

1. Find the corresponding markdown file for your new feature in website/docs/documentation folder.
   Make sure you choose the correct class estimators/transformers and namespace.
2. Follow the pattern in markdown file and add another section for your new API, including pyspark, scala (and .NET potentially) samples.
3. Make sure the DocTable points to correct API link.
4. Navigate to website folder, and run yarn run start to make sure the website renders correctly.
5. Don't forget to add <!--pytest-codeblocks:cont--> before each python code blocks to enable auto-tests for python samples.
6. Make sure the WebsiteSamplesTests job pass in the pipeline.

[github-actions]

Hey @dciborow 👋!
Thank you so much for contributing to our repository 🙌.
Someone from SynapseML Team will be reviewing this pull request soon.

We use semantic commit messages to streamline the release process.
Before your pull request can be merged, you should make sure your first commit and PR title start with a semantic prefix.
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• fix: Fix LightGBM crashes with empty partitions
• feat: Make HTTP on Spark back-offs configurable
• docs: Update Spark Serving usage
• build: Add codecov support
• perf: improve LightGBM memory usage
• refactor: make python code generation rely on classes
• style: Remove nulls from CNTKModel
• test: Add test coverage for CNTKModel

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[dciborow]

@mhamilton723 , i deployed the 'text-embedding-ada-002' endpoint. This should be all ready for testing.

[dciborow]

/azp run

[azure-pipelines]

Azure Pipelines successfully started running 1 pipeline(s).

[codecov-commenter]

Codecov Report

Merging #1938 (63aa883) into master (9911bfb) will decrease coverage by 0.04%.
The diff coverage is n/a.

@@            Coverage Diff             @@
##           master    #1938      +/-   ##
==========================================
- Coverage   87.01%   86.98%   -0.04%     
==========================================
  Files         305      305              
  Lines       15993    15993              
  Branches      839      839              
==========================================
- Hits        13917    13911       -6     
- Misses       2076     2082       +6     

see 1 file with indirect coverage changes

[dciborow]

/azp run

[azure-pipelines]

Azure Pipelines successfully started running 1 pipeline(s).

[github-actions]

Summary by GPT-4

The changes in the code are as follows:

1. Added a comment with a link to learn more about selecting which embedding model to choose.
2. Changed the deployment_name_embeddings variable value from "text-search-ada-doc-001" to "text-embedding-ada-002".
3. Removed the deployment_name_embeddings_query variable and its value "text-search-ada-query-001".
4. In the embedding_query code block, replaced .setDeploymentName(deployment_name_embeddings_query) with .setDeploymentName(deployment_name_embeddings).

These changes update the deployment name for the embeddings and remove an unnecessary variable, simplifying the code.

Suggestions

No suggestions are needed as the changes in this PR are clear and straightforward.

[dciborow]

/azp run

[azure-pipelines]

Azure Pipelines successfully started running 1 pipeline(s).

[mhamilton723]

/azp run

[azure-pipelines]

Azure Pipelines successfully started running 1 pipeline(s).