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@ti-chi-bot ti-chi-bot bot added the do-not-merge/work-in-progress Indicates that a PR should not merge because it is a work in progress. label Aug 3, 2025
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@ti-chi-bot ti-chi-bot bot added missing-translation-status This PR does not have translation status info. size/XXL Denotes a PR that changes 1000+ lines, ignoring generated files. labels Aug 3, 2025
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Summary of Changes

Hello @breezewish, 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 introduces new documentation for TiDB Cloud's 'Auto Embedding' feature, designed to simplify vector search by automatically converting text into vector embeddings. The changes include an overview of the feature and specific guides for integrating various embedding models, making it easier for users to leverage semantic search capabilities.

Highlights

  • New Documentation for Auto Embedding: I've added comprehensive documentation for the new 'Auto Embedding' feature in TiDB Cloud, which allows users to perform vector search directly on text data without manual vector generation.
  • Overview of Auto Embedding: A new overview page (tidb-cloud/vector-search-auto-embedding-overview.md) has been introduced, explaining the core concepts, workflow, and benefits of Auto Embedding, including quick start examples and its compatibility with vector indexes.
  • Specific Embedding Model Guides: Detailed documentation pages have been added for various embedding models supported by TiDB Cloud, including Amazon Titan, Cohere, and Gemini. These guides provide information on available models, SQL usage examples, and configuration options for both TiDB Cloud-hosted and Bring Your Own Key (BYOK) models.
  • Table of Contents Update: The TOC-tidb-cloud.md file has been updated to include a new 'Auto Embedding' section under 'Vector Search', linking to the newly added overview and individual model documentation pages.
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Code Review

This pull request adds new documentation for the "Auto Embedding" feature. The changes include updating the Table of Contents and adding several new documentation pages for the feature overview and specific embedding models (Amazon Titan, Cohere, Gemini).

The new documentation is well-structured and detailed. I've found a few minor issues related to grammar, typos, and wording that could improve clarity and consistency. These are mostly low-severity suggestions.

I've referenced the following points from the style guide in my review:

  • Clarity and Simplicity (line 14): Suggested more direct and professional wording.
  • Correct English grammar, spelling, and punctuation (line 20): Pointed out typos and grammatical mistakes.
  • Consistent terminology (line 25): Recommended consistent capitalization for "documentation".


> **Note:**
>
> You must using `VEC_COSINE_DISTANCE` or `VEC_L2_DISTANCE` when defining the vector index, while using `VEC_EMBED_COSINE_DISTANCE` or `VEC_EMBED_L2_DISTANCE` when performing vector search queries.

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medium

There's a grammatical error here.1 It should be "You must use" instead of "You must using".

Style Guide References

Suggested change
> You must using `VEC_COSINE_DISTANCE` or `VEC_L2_DISTANCE` when defining the vector index, while using `VEC_EMBED_COSINE_DISTANCE` or `VEC_EMBED_L2_DISTANCE` when performing vector search queries.
> You must use `VEC_COSINE_DISTANCE` or `VEC_L2_DISTANCE` when defining the vector index, while using `VEC_EMBED_COSINE_DISTANCE` or `VEC_EMBED_L2_DISTANCE` when performing vector search queries.

Footnotes

  1. The style guide requires correct English grammar, spelling, and punctuation. (link)


## Auto Embedding + Vector Index

Auto embedding is compatible with [Vector Index](/vector-search/vector-search-index.md) for better query performance. Define a vector index over the generated vector column, and it will just work:

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low

The phrase "it will just work" is a bit informal for technical documentation. I suggest a more professional alternative to maintain clarity and a consistent tone.1

Style Guide References

Suggested change
Auto embedding is compatible with [Vector Index](/vector-search/vector-search-index.md) for better query performance. Define a vector index over the generated vector column, and it will just work:
Auto embedding is compatible with [Vector Index](/vector-search/vector-search-index.md) for better query performance. Define a vector index over the generated vector column, and it is used automatically:

Footnotes

  1. The style guide prioritizes clarity and simplicity in documentation. (link)

breezewish and others added 3 commits August 4, 2025 00:26
Signed-off-by: Wish <[email protected]>
Signed-off-by: Wish <[email protected]>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
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