databricks-vector-search

Files (5)

• SKILL.md
• end-to-end-rag.md
• index-types.md
• search-modes.md
• troubleshooting-and-operations.md

SKILL.md (first lines)

# 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** | 20-50ms | 320M vectors (768 dim) | Higher | Real-time, low-latency |
| **Storage-Optimized** | 300-500ms | 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

```python
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)

```python
# 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

```python
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

```python
# 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

```python
# 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

From ThomazRossito/data-agents, MIT.

More from ThomazRossito/data-agents