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This is an automated cherry-pick of pingcap#22604
Signed-off-by: ti-chi-bot <ti-community-prow-bot@tidb.io>
1 parent 2475860 commit 4b73951

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.github/workflows/link.yaml

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- name: Check Links
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uses: lycheeverse/lychee-action@v1.6.1
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with:
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# For parameter description, see https://github.com/lycheeverse/lychee#commandline-parameters
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# Accept 429 for now due to github rate limit.
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# See https://github.com/lycheeverse/lychee/issues/634
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args: -E --exclude-mail -i -n -t 45 -- **/*.md *.md
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=======
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# Don't fail as we want the workflow to continue and run 'Create Issue From File'
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# Excluding releases paths as historic releases may have outdated links.
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fail: false
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failIfEmpty: false
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args: --root-dir $(pwd) --cache --max-cache-age 8d -E -i -n -t 45 --exclude-path '^./releases/' --exclude-path '^./tidb-cloud/releases/' --exclude-path '^./resources/' .
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>>>>>>> 8d8f8fa9cd (Update link checker config (#22604))
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output: out.md
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env:
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GITHUB_TOKEN: ${{secrets.GITHUB_TOKEN}}

.lycheeignore

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https://www\.mysql\.com/products/.*
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https://help\.openai\.com/en/articles/.*
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https://platform\.openai\.com/docs/.*
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https://platform\.openai\.com/api-keys
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https://openai\.com/.*
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https://jwt\.io/
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https://typeorm\.io/.*
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https://docs\.pingcap\.com/tidb/v6\.6/system-variables#tidb_pessimistic_txn_aggressive_locking-new-in-v660
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https://docs\.pingcap\.com/tidb/v7\.6/system-variables#tidb_ddl_version-new-in-v760
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https://developers\.redhat\.com/blog/2021/01/05/building-red-hat-enterprise-linux-9-for-the-x86-64-v2-microarchitecture-level
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https://portal\.azure\.com/.*
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=======
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https://.*github.*/%7B%7B%7B%20.tidb_operator_version%20%7D%7D%7D
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https://.*github.*/%7B%7B%7B.tidb-operator-version%7D%7D%7D
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https://portal\.azure\.com/.*
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https://azuremarketplace\.microsoft\.com/.*
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https://one\.newrelic\.com/.*
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https://tidbcloud\.com/.*
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>>>>>>> 8d8f8fa9cd (Update link checker config (#22604))

tidb-cloud/tidb-cloud-glossary.md

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Unlike [Vector Search](/ai/concepts/vector-search-overview.md), which focuses on semantic similarity, full-text search lets you retrieve documents for exact keywords. In Retrieval-Augmented Generation (RAG) scenarios, you can use full-text search together with vector search to improve the retrieval quality.
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For more information, see [Full-Text Search with SQL](https://docs.pingcap.com/developer/vector-search-full-text-search-sql) and [Full-Text Search with Python](https://docs.pingcap.com/developer/vector-search-full-text-search-python).
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For more information, see [Full-Text Search with SQL](/ai/guides/vector-search-full-text-search-sql.md) and [Full-Text Search with Python](/ai/guides/vector-search-full-text-search-python.md).
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## M
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