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" } )

Query Index

results = w.vector_search_indexes.query_index( index_name="catalog.schema.my_index", columns=["id", "content", "metadata"], query_text="What is machine learning?", num_results=5 )

for doc in results.result.data_array: score = doc[-1] # Similarity score is last column print(f"Score: {score}, Content: {doc[1][:100]}...")

Common Patterns

Create Storage-Optimized Endpoint

For large-scale, cost-effective deployments

endpoint = w.vector_search_endpoints.create_endpoint( name="my-storage-endpoint", endpoint_type="STORAGE_OPTIMIZED" )

Delta Sync with Self-Managed Embeddings

Source table must have: primary key + embedding vector 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_vector_columns": [ { "name": "embedding", # Pre-computed embedding column "embedding_dimension": 768 } ], "pipeline_type": "TRIGGERED" } )

Direct Access Index

import json

Create index for manual CRUD

index = w.vector_search_indexes.create_index( name="catalog.schema.direct_index", endpoint_name="my-vs-endpoint", primary_key="id", index_type="DIRECT_ACCESS", direct_access_index_spec={ "embedding_vector_columns": [ {"name": "embedding", "embedding_dimension": 768} ], "schema_json": json.dumps({ "id": "string", "text": "string", "embedding": "array<float>", "metadata": "string" }) } )

Upsert data

w.vector_search_indexes.upsert_data_vector_index( index_name="catalog.schema.direct_index", inputs_json=json.dumps([ {"id": "1", "text": "Hello", "embedding": [0.1, 0.2, ...], "metadata": "doc1"}, {"id": "2", "text": "World", "embedding": [0.3, 0.4, ...], "metadata": "doc2"}, ]) )

Delete data

w.vector_search_indexes.delete_data_vector_index( index_name="catalog.schema.direct_index", primary_keys=["1", "2"] )

Query with Embedding Vector

When you have pre-computed query embedding

results = w.vector_search_indexes.query_index( index_name="catalog.schema.my_index", columns=["id", "text"], query_vector=[0.1, 0.2, 0.3, ...], # Your 768-dim vector num_results=10 )

Hybrid Search (Semantic + Keyword)

Combines vector similarity with keyword matching

results = w.vector_search_indexes.query_index( index_name="catalog.schema.my_index", columns=["id", "content"], query_text="machine learning algorithms", query_type="hybrid", # Enable hybrid search num_results=10 )

Filtering

Standard Endpoint Filters (Dictionary)

filters_json uses dictionary format

results = w.vector_search_indexes.query_index( index_name="catalog.schema.my_index", columns=["id", "content"], query_text="machine learning", num_results=10, filters_json='{"category": "ai", "status": ["active", "pending"]}' )

Storage-Optimized Filters (SQL-like)

filter_string uses SQL-like syntax

results = w.vector_search_indexes.query_index( index_name="catalog.schema.my_index", columns=["id", "content"], query_text="machine learning", num_results=10, filter_string="category = 'ai' AND status IN ('active', 'pending')" )

More filter examples

filter_string="price > 100 AND price < 500" filter_string="department LIKE 'eng%'" filter_string="created_at >= '2024-01-01'"

Trigger Index Sync

For TRIGGERED pipeline type, manually sync

w.vector_search_indexes.sync_index( index_name="catalog.schema.my_index" )

Scan All Index Entries

Retrieve all vectors (for debugging/export)

scan_result = w.vector_search_indexes.scan_index( index_name="catalog.schema.my_index", num_results=100 )

Reference Files

• index-types.md - Detailed comparison of index types and creation patterns

CLI Quick Reference

List endpoints

databricks vector-search endpoints list

Create endpoint

databricks vector-search endpoints create
--name my-endpoint
--endpoint-type STANDARD

List indexes on endpoint

databricks vector-search indexes list-indexes
--endpoint-name my-endpoint

Get index status

databricks vector-search indexes get-index
--index-name catalog.schema.my_index

Sync index (for TRIGGERED)

databricks vector-search indexes sync-index
--index-name catalog.schema.my_index

Delete index

databricks vector-search indexes delete-index
--index-name catalog.schema.my_index

Common Issues

Issue Solution

Index sync slow Use Storage-Optimized endpoints (20x faster indexing)

Query latency high Use Standard endpoint for <100ms latency

filters_json not working Storage-Optimized uses filter_string (SQL syntax)

Embedding dimension mismatch Ensure query and index dimensions match

Index not updating Check pipeline_type; use sync_index() for TRIGGERED

Out of capacity Upgrade to Storage-Optimized (1B+ vectors)

Embedding Models

Databricks provides built-in embedding models:

Model Dimensions Use Case

databricks-gte-large-en

1024 English text, high quality

databricks-bge-large-en

1024 English text, general

Use with managed embeddings

embedding_source_columns=[ { "name": "content", "embedding_model_endpoint_name": "databricks-gte-large-en" } ]

MCP Tools

The following MCP tools are available for managing Vector Search infrastructure. These are management tools for creating and configuring endpoints/indexes. For agent-runtime querying, use the Databricks managed Vector Search MCP server or VectorSearchRetrieverTool .

Endpoint Management

Tool Description

create_vs_endpoint

Create a Vector Search endpoint (STANDARD or STORAGE_OPTIMIZED)

get_vs_endpoint

Get endpoint status and details

list_vs_endpoints

List all endpoints in the workspace

delete_vs_endpoint

Delete an endpoint (indexes must be deleted first)

Index Management

Tool Description

create_vs_index

Create a Delta Sync or Direct Access index

get_vs_index

Get index status and configuration

list_vs_indexes

List all indexes on an endpoint

delete_vs_index

Delete an index

sync_vs_index

Trigger sync for TRIGGERED pipeline indexes

Query and Data

Tool Description

query_vs_index

Query index with text, vector, or hybrid search (for testing)

upsert_vs_data

Upsert vectors into a Direct Access index

delete_vs_data

Delete vectors from a Direct Access index

scan_vs_index

Scan/export index entries (for debugging)

Notes

• Storage-Optimized is newer - Better for most use cases unless you need <100ms latency

• Delta Sync recommended - Easier than Direct Access for most scenarios

• Hybrid search - Available for both Delta Sync and Direct Access indexes

• Management vs runtime - MCP tools above handle lifecycle management; for agent tool-calling at runtime, use the Databricks managed Vector Search MCP server