Add spannerio vector writer.

[claudevdm]

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[gemini-code-assist]

Summary of Changes

Hello @claudevdm, I'm Gemini Code Assist¹! 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 enhances Apache Beam's machine learning capabilities by adding a robust integration for writing vector embeddings and their associated contextual data to Google Cloud Spanner. It offers a highly customizable framework for defining how Chunk objects are transformed into Spanner table rows, supporting both default and user-defined schemas, and includes mechanisms for handling metadata flexibly. This addition streamlines the process of building RAG pipelines that leverage Spanner as a vector store.

Highlights

• Spanner Vector Writer: Introduces a new SpannerVectorWriterConfig for ingesting vector embeddings and metadata into Google Cloud Spanner within Beam RAG pipelines.
• Flexible Schema Definition: Provides SpannerColumnSpecsBuilder to define custom Spanner table schemas, allowing mapping of Chunk fields (ID, embedding, content, metadata) to columns.
• Metadata Flattening: Enables flattening specific metadata fields from Chunk.metadata into dedicated Spanner table columns, alongside a JSON column for remaining metadata.
• Integration Tests: Includes a new integration test suite (spanner_it_test.py) that uses a Spanner emulator to validate various write configurations and schema mappings.

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[claudevdm]

/gemini review

[claudevdm]

R: @damccorm

[claudevdm]

The tests use spanner emulator. I am not sure what will happen in the xlang dataflow test suite, but hopefully the tests will be skipped? Or maybe I need to skip tests if runner is dataflow runner manually.

[github-actions]

Stopping reviewer notifications for this pull request: review requested by someone other than the bot, ceding control. If you'd like to restart, comment assign set of reviewers

[gemini-code-assist]

Code Review

This pull request introduces a SpannerVectorWriter for RAG pipelines, allowing embeddings and metadata to be written to Google Cloud Spanner. The implementation is well-structured, featuring a flexible SpannerColumnSpecsBuilder for schema definition and comprehensive integration tests using a Spanner emulator. My review includes suggestions to improve code efficiency in duplicate checking, enhance robustness in error handling within the test helpers, and adhere to Python's standard import practices in the test file. Overall, this is a solid contribution that adds valuable functionality.

[damccorm]

Thanks - had a few nits, but otherwise this LGTM

[damccorm]

Thanks!