Databricks Vector Search

Databricks Vector Search

FIRST: Use the parent databricks-core skill for CLI basics, authentication, and profile selection.

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.

Endpoint Types

Index Types

Quick Start

Create Endpoint

pythonCopy code
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)

pythonCopy code
# 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

pythonCopy code
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

pythonCopy code
# 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

pythonCopy code
# 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

pythonCopy code
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

pythonCopy code
# 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)

Hybrid search combines vector similarity (ANN) with BM25 keyword scoring. Use it when queries contain exact terms that must match — SKUs, error codes, proper nouns, or technical terminology — where pure semantic search might miss keyword-specific results. See references/search-modes.md for detailed guidance on choosing between ANN and hybrid search.

pythonCopy code
# Combines vector similarity with keyword matching
results = w.vector_search_indexes.query_index(
    index_name="catalog.schema.my_index",
    columns=["id", "content"],
    query_text="SPARK-12345 executor memory error",
    query_type="HYBRID",
    num_results=10
)

Filtering

Standard Endpoint Filters (Dictionary)

pythonCopy code
# 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)

Storage-Optimized endpoints use SQL-like filter syntax via the databricks-vectorsearch package's filters parameter (accepts a string):

pythonCopy code
from databricks.vector_search.client import VectorSearchClient

vsc = VectorSearchClient()
index = vsc.get_index(endpoint_name="my-storage-endpoint", index_name="catalog.schema.my_index")

# SQL-like filter syntax for storage-optimized endpoints
results = index.similarity_search(
    query_text="machine learning",
    columns=["id", "content"],
    num_results=10,
    filters="category = 'ai' AND status IN ('active', 'pending')"
)

# More filter examples
# filters="price > 100 AND price < 500"
# filters="department LIKE 'eng%'"
# filters="created_at >= '2024-01-01'"

Trigger Index Sync

pythonCopy code
# For TRIGGERED pipeline type, manually sync
w.vector_search_indexes.sync_index(
    index_name="catalog.schema.my_index"
)

Scan All Index Entries

pythonCopy code
# 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

CLI Quick Reference

bashCopy code
# List endpoints
databricks vector-search-endpoints list-endpoints

# Create endpoint (positional args: NAME ENDPOINT_TYPE)
databricks vector-search-endpoints create-endpoint my-endpoint STANDARD

# List indexes on endpoint (positional arg: ENDPOINT_NAME)
databricks vector-search-indexes list-indexes my-endpoint

# Get index status (positional arg: INDEX_NAME)
databricks vector-search-indexes get-index catalog.schema.my_index

# Sync index (positional arg: INDEX_NAME)
databricks vector-search-indexes sync-index catalog.schema.my_index

# Delete index (positional arg: INDEX_NAME)
databricks vector-search-indexes delete-index catalog.schema.my_index

Common Issues

Embedding Models

Databricks provides built-in embedding models:

pythonCopy code
# Use with managed embeddings
embedding_source_columns=[
    {
        "name": "content",
        "embedding_model_endpoint_name": "databricks-gte-large-en"
    }
]

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
• columns_to_sync matters — only synced columns are available in query results; include all columns you need
• Filter syntax differs by endpoint — Standard uses dict-format filters, Storage-Optimized uses SQL-like string filters. Use the databricks-vectorsearch package's filters parameter which accepts both formats
• Management vs runtime — CLI and SDK handle lifecycle management; for agent tool-calling at runtime, use VectorSearchRetrieverTool

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-pipelines - Build Delta tables used as Vector Search sources