databricks-vector-search

name	databricks-vector-search
description	Patterns for Databricks Vector Search: create endpoints and indexes, query with filters, manage embeddings. Use when building RAG applications, semantic search, or similarity matching. Covers both storage-optimized and standard endpoints.

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

Related Skills

• databricks-model-serving - Deploy agents that use VectorSearchRetrieverTool
• databricks-agent-bricks - Knowledge Assistants use RAG over indexed documents
• databricks-unstructured-pdf-generation - Generate documents to index in Vector Search
• databricks-unity-catalog - Manage the catalogs and tables that back Delta Sync indexes
• databricks-spark-declarative-pipelines - Build Delta tables used as Vector Search sources