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[WIP] tidbcloud: Add auto embedding docs #21499
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Signed-off-by: Wish <[email protected]>
[APPROVALNOTIFIER] This PR is NOT APPROVED This pull-request has been approved by: The full list of commands accepted by this bot can be found here.
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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".
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> **Note:** | ||
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> 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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There's a grammatical error here.1 It should be "You must use" instead of "You must using".
Style Guide References
> 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
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## Auto Embedding + Vector Index | ||
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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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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
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
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>
@breezewish: The following test failed, say
Full PR test history. Your PR dashboard. Instructions for interacting with me using PR comments are available here. If you have questions or suggestions related to my behavior, please file an issue against the kubernetes-sigs/prow repository. I understand the commands that are listed here. |
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