What is databricks-vector-search?

A set of patterns for Databricks Vector Search, a service that finds similar content using numerical representations called embeddings.

Use it to build retrieval-augmented generation (RAG) applications, semantic search, similarity matching, and vector indexes backed by Delta tables.

It helps structure the endpoints, indexes, filters, and data synchronization needed for RAG and semantic search without designing the setup from scratch.

How alessandro9110/Speech-To-Text-With-Databricks configures Claude Co

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Borrowing it

Nothing to install: this file belongs to alessandro9110/Speech-To-Text-With-Databricks. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/alessandro9110/Speech-To-Text-With-Databricks/main/.claude/skills/databricks-vector-search/SKILL.md

Clone the repo

git clone --depth 1 https://github.com/alessandro9110/Speech-To-Text-With-Databricks

Made for: Claude Code.

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README.md

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Per session 49 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 2,591 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00049	$0.02591
Opus 5.5	$0.00020	$0.01036
Sonnet 5.5	$0.00010	$0.00518
Haiku 4.5	$0.00005	$0.00259

Measured 1mo ago against content hash 2b0fa22db10f, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

databricks-vector-search scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.claude/skills/databricks-vector-search/SKILL.md · 353 lines

How it starts

The opening of the file, as written. The whole thing — 353 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Databricks Vector Search

Patterns for creating, managing, and querying vector search indexes for RAG and semantic search applications.

When to Use

Use this skill when:

Building RAG (Retrieval-Augmented Generation) applications
Implementing semantic search or similarity matching
Creating vector indexes from Delta tables
Choosing between storage-optimized and standard endpoints
Querying vector indexes with filters

Overview

Databricks Vector Search provides managed vector similarity search with automatic embedding generation and Delta Lake integration.

Component	Description
Endpoint	Compute resource hosting indexes (Standard or Storage-Optimized)
Index	Vector data structure for similarity search
Delta Sync	Auto-syncs with source Delta table
Direct Access	Manual CRUD operations on vectors

Endpoint Types

Type	Latency	Capacity	Cost	Best For
Standard	~50-100ms	320M vectors (768 dim)	Higher	Real-time, low-latency
Storage-Optimized	~250ms	1B+ vectors (768 dim)	7x lower	Large-scale, cost-sensitive

Index Types

Type	Embeddings	Sync	Use Case
Delta Sync (managed)	Databricks computes	Auto from Delta	Easiest setup
Delta Sync (self-managed)	You provide	Auto from Delta	Custom embeddings
Direct Access	You provide	Manual CRUD	Real-time updates

Quick Start

Create Endpoint

from databricks.sdk import WorkspaceClient

w = WorkspaceClient()

# Create a standard endpoint
endpoint = w.vector_search_endpoints.create_endpoint(
    name="my-vs-endpoint",
    endpoint_type="STANDARD"  # or "STORAGE_OPTIMIZED"
)
# Note: Endpoint creation is asynchronous; check status with get_endpoint()

Create Delta Sync Index (Managed Embeddings)

# Source table must have: primary key column + text column
index = w.vector_search_indexes.create_index(
    name="catalog.schema.my_index",
    endpoint_name="my-vs-endpoint",
    primary_key="id",
    index_type="DELTA_SYNC",
    delta_sync_index_spec={
        "source_table": "catalog.schema.documents",
        "embedding_source_columns": [
            {
                "name": "content",  # Text column to embed
                "embedding_model_endpoint_name": "databricks-gte-large-en"
            }
        ],
        "pipeline_type": "TRIGGERED"  # or "CONTINUOUS"
    }
)

Read the full file on GitHub · 353 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

index-types.md 6.2 KB

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

1mo ago First seen · 353 lines · 49 tokens per session scan A 2b0fa22db10f

Subscribe to this mod's changes

databricks-vector-search is a skill published in the GitHub repository alessandro9110/Speech-To-Text-With-Databricks (4 stars, last pushed 8mo ago), licensed MIT. It adds 49 tokens to every session and 2,591 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-02.

Data and AI RAG and vectors databricks delta-lake generative-ai lakehouse machine speech-to-text

databricks-vector-search next to 6 skills from other repositories
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Speech-To-Text-With-Databricks: Skill for Claude Code