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DatabricksKnown publisherdatabricks/databricks-agent-skills/plugins/databricks/antigravity/skills/databricks-vector-search/SKILL.md

databricks-vector-search

Skill387 starsChanged 17 days agoLicence not identifiedView on GitHub

Databricks AI Search (formerly Vector Search) endpoints and indexes for RAG and semantic search. Use when asked to: create Standard or Storage-Optimized endpoints and Delta Sync or Direct Access indexes with AISearchClient; query with ANN, hybrid, or full-text search and filters; sync, monitor, or migrate indexes; set a target QPS, triage 429s, or load-test an endpoint; wire an index into an agent as a retriever tool. NOT for: no-code document Q&A (databricks-genie-agents); serving-endpoint CRUD (databricks-model-serving); custom ResponsesAgent authoring and deployment (databricks-ml-training).

  • Installs packages

3 other projects use a version of this, 3 of them edited.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

7 rules. 1 is common, 3 are distinctive, and 3 are very specific to this project.

  • 1

    Common

    Most of the 3 edited versions we read keep these.

  • 3

    Distinctive

    This author's own choices. Worth a look.

  • 3

    Very specific

    Tied to their project's names, files or tools. Probably skip.

The licence could not be identified — read it at the source. We count its rules but don't quote them. Read them on GitHub.

“Common” is measured: kept by at least 6 in 10 of the 3 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

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What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

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