## Databricks Vector Search

Databricks Vector Search is a serverless similarity search engine that allows you to store a vector representation of your data, including metadata, in a vector database. With Vector Search, you can create auto-updating vector search indexes from Delta tables managed by Unity Catalog and query them with a simple API to return the most similar vectors.

### Usage

#### Python

```python
import os
from mem0 import Memory

config = {
    "vector_store": {
        "provider": "databricks",
        "config": {
            "workspace_url": "https://your-workspace.databricks.com",
            "access_token": "your-access-token",
            "endpoint_name": "your-vector-search-endpoint",
            "catalog": "your_catalog",
            "schema": "your_schema",
            "table_name": "your_table",
            "collection_name": "your_index_name",
            "embedding_dimension": 1536
        }
    }
}

m = Memory.from_config(config)
m.add(messages, user_id="alice", metadata={"category": "movies"})
```

#### TypeScript

```typescript
// Requires the Databricks SQL driver (peer dependency): pnpm add @databricks/sql
import { Memory } from 'mem0ai/oss';

const config = {
  vectorStore: {
    provider: 'databricks',
    config: {
      workspaceUrl: 'https://your-workspace.databricks.com',
      httpPath: '/sql/1.0/warehouses/your-warehouse-id',
      accessToken: 'your-access-token',
      catalog: 'your_catalog',
      schema: 'your_schema',
      tableName: 'your_table',
      collectionName: 'your_index_name',
      embeddingModelDims: 1536,
    },
  },
};

const memory = new Memory(config);
await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });
```

### Config

Here are the parameters available for configuring Databricks Vector Search:

| Parameter | Description | Default Value |
| --- | --- | --- |
| `workspace_url` | The URL of your Databricks workspace | **Required** |
| `access_token` | Personal Access Token for authentication | `None` |
| `client_id` | Service principal client ID (alternative to access_token) | `None` |
| `client_secret` | Service principal client secret (required with client_id) | `None` |
| `endpoint_name` | Name of the Vector Search endpoint | **Required** |
| `catalog` | Unity Catalog catalog name | **Required** |
| `schema` | Unity Catalog schema name | **Required** |
| `table_name` | Source Delta table name | **Required** |
| `collection_name` | Vector search index name | `mem0` |
| `index_type` | Index type: `DELTA_SYNC` or `DIRECT_ACCESS` | `DELTA_SYNC` |
| `embedding_dimension` | Dimension of self-managed embeddings | `1536` |

### Authentication

Databricks Vector Search supports two authentication methods:

#### Service Principal (Recommended for Production)

```python
config = {
    "vector_store": {
        "provider": "databricks",
        "config": {
            "workspace_url": "https://your-workspace.databricks.com",
            "client_id": "your-service-principal-id",
            "client_secret": "your-service-principal-secret",
            "endpoint_name": "your-endpoint",
            "catalog": "your_catalog",
            "schema": "your_schema",
            "table_name": "your_table",
            "collection_name": "your_index_name",
        }
    }
}
```

#### Personal Access Token (for Development)

```python
config = {
    "vector_store": {
        "provider": "databricks",
        "config": {
            "workspace_url": "https://your-workspace.databricks.com",
            "access_token": "your-personal-access-token",
            "endpoint_name": "your-endpoint",
            "catalog": "your_catalog",
            "schema": "your_schema",
            "table_name": "your_table",
            "collection_name": "your_index_name",
        }
    }
}
```

### Important Notes

- **Index Types**: This implementation supports both `DELTA_SYNC` (auto-syncs with source Delta table) and `DIRECT_ACCESS` (manage vectors directly) index types.
- **Unity Catalog**: The source table and index are created under the specified `catalog.schema` namespace.
- **Endpoint Auto-Creation**: If the specified endpoint doesn’t exist, it will be created automatically.
- **Index Auto-Creation**: If the specified index doesn’t exist, it will be created automatically with the provided configuration.