gemini-code-assist[bot] commented on PR #37651: URL: https://github.com/apache/beam/pull/37651#issuecomment-3929071196
## Summary of Changes Hello @claudevdm, I'm Gemini Code Assist[^1]! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request addresses a breaking change within the BigQuery vector storage components by refactoring the `SchemaConfig` class for improved initialization and introducing deprecation handling for an older parameter name. It also standardizes terminology in a related BigQuery vector search query. ### Highlights * **SchemaConfig Refactoring**: Refactored the `SchemaConfig` class in `bigquery.py` from a dataclass to a standard class, introducing an `__init__` method for more controlled initialization and validation. * **Backward Compatibility and Deprecation**: Implemented backward compatibility and deprecation warnings for the `chunk_to_dict_fn` parameter within `SchemaConfig`'s constructor, guiding users towards the preferred `embeddable_to_dict_fn`. * **BigQuery Query Terminology Update**: Updated a BigQuery vector search query in `bigquery_vector_search.py` to use `chunks` instead of `embeddable_items` as an alias for the aggregated results. * **CI Trigger Update**: Incremented a modification counter in the `beam_PostCommit_Python_Xlang_Gcp_Direct.json` CI trigger file. 🧠**New Feature in Public Preview:** You can now enable **Memory** to help **Gemini Code Assist** learn from your team's feedback. This makes future code reviews more consistent and personalized to your project's style. **Click [here](https://codeassist.google/code-review/login) to enable Memory in your admin console.** <details> <summary><b>Changelog</b></summary> * **.github/trigger_files/beam_PostCommit_Python_Xlang_Gcp_Direct.json** * Updated the modification counter to trigger CI. * **sdks/python/apache_beam/ml/rag/enrichment/bigquery_vector_search.py** * Renamed the `embeddable_items` alias to `chunks` in the `VECTOR_SEARCH` query. * **sdks/python/apache_beam/ml/rag/ingestion/bigquery.py** * Converted the `SchemaConfig` class from a dataclass to a standard class with an `__init__` method. * Added a deprecation warning for the `chunk_to_dict_fn` parameter in `SchemaConfig`'s constructor. * Made `embeddable_to_dict_fn` a required parameter in the `SchemaConfig` constructor. * Imported the `warnings` module. </details> <details> <summary><b>Activity</b></summary> * The pull request was created by claudevdm. </details> <details> <summary><b>Using Gemini Code Assist</b></summary> <br> The full guide for Gemini Code Assist can be found on our [documentation page](https://developers.google.com/gemini-code-assist/docs/review-github-code), here are some quick tips. <b>Invoking Gemini</b> You can request assistance from Gemini at any point by creating a comment using either `/gemini <command>` or `@gemini-code-assist <command>`. 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