gemini-code-assist[bot] commented on PR #37393:
URL: https://github.com/apache/beam/pull/37393#issuecomment-3786464730
## Summary of Changes
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pull request and will post my feedback shortly. In the meantime, here's a
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This pull request introduces a new Colab notebook that serves as a
comprehensive guide for integrating vector embeddings with Cloud Spanner using
Apache Beam. It provides practical examples for various data processing and
ingestion patterns, enabling users to efficiently generate, store, and manage
embeddings for applications like product catalogs, leveraging both local and
cloud-based ML models.
### Highlights
* **New Colab Notebook for Spanner Vector Ingestion**: A new Colab notebook,
'spanner_product_catalog_embeddings.ipynb', has been added to demonstrate how
to generate and ingest vector embeddings into Cloud Spanner using Apache Beam.
* **Comprehensive Use Cases**: The notebook covers a wide range of
scenarios, including basic vector ingestion, scaling pipelines with Dataflow,
implementing custom schemas, handling updates to embeddings and metadata,
integrating with existing Spanner data, utilizing Vertex AI for embeddings, and
setting up real-time streaming updates from Pub/Sub.
* **Embedding Generation Options**: It showcases the use of both local
Hugging Face 'sentence-transformers' models and Google's Vertex AI text
embedding models ('text-embedding-005') for generating vector embeddings.
* **Spanner Integration Details**: The examples detail how to configure
'SpannerVectorWriterConfig' and 'SpannerColumnSpecsBuilder' to map 'Chunk'
objects to Spanner table schemas, including handling different write modes
('INSERT', 'UPDATE') and transforming data.
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