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feat(vector-stores): add Databricks provider to TypeScript OSS SDK (#5824)

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Co-authored-by: kartik-mem0 <kartik.labhshetwar@mem0.ai>
This commit is contained in:
Rod Boev
2026-07-11 07:05:47 -04:00
committed by GitHub
parent c9af55986e
commit 17836748d7
15 changed files with 4399 additions and 92 deletions
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docs/components/vectordbs/dbs/databricks.mdx
+62 -1
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@@ -6,7 +6,8 @@ description: "Use Databricks Vector Search as a serverless vector store in Mem0
### Usage
```python
<CodeGroup>
```python Python
import os
from mem0 import Memory
@@ -36,10 +37,44 @@ messages = [
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',
// SQL warehouse HTTP path, used for index writes (required)
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);
const messages = [
{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
{"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
{"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
]
await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });
```
</CodeGroup>
### Config
Here are the parameters available for configuring Databricks Vector Search:
<Tabs>
<Tab title="Python">
| Parameter | Description | Default Value |
| --- | --- | --- |
| `workspace_url` | The URL of your Databricks workspace | **Required** |
@@ -60,6 +95,32 @@ Here are the parameters available for configuring Databricks Vector Search:
| `pipeline_type` | Sync pipeline type: `TRIGGERED` or `CONTINUOUS` | `TRIGGERED` |
| `warehouse_name` | Databricks SQL warehouse name (if using SQL warehouse) | `None` |
| `query_type` | Query type: `ANN` or `HYBRID` | `ANN` |
</Tab>
<Tab title="TypeScript">
| Parameter | Description | Default Value |
| --- | --- | --- |
| `workspaceUrl` | The URL of your Databricks workspace (or pass `host`) | **Required** |
| `httpPath` | SQL warehouse HTTP path, used for index writes | **Required** |
| `accessToken` | Personal Access Token for authentication | `None` |
| `clientId` | Service principal client ID (alternative to `accessToken`) | `None` |
| `clientSecret` | Service principal client secret (required with `clientId`) | `None` |
| `endpointName` | Name of the Vector Search endpoint | `mem0_vector_search` |
| `endpointType` | Type of endpoint (`STANDARD` or `STORAGE_OPTIMIZED`) | `STANDARD` |
| `pipelineType` | Delta Sync pipeline type: `TRIGGERED` or `CONTINUOUS` | `TRIGGERED` |
| `queryType` | Query type: `ANN` or `HYBRID` | `ANN` |
| `catalog` | Unity Catalog catalog name | `main` |
| `schema` | Unity Catalog schema name | `default` |
| `collectionName` | Vector Search index name | `mem0` |
| `tableName` | Source Delta table name | falls back to `collectionName` |
| `embeddingModelDims` | Dimension of self-managed embeddings | `1536` |
| `syncPollIntervalMs` | Poll interval while waiting for a `TRIGGERED` sync | `1000` |
| `syncTimeoutMs` | Timeout while waiting for an index sync | `300000` |
<Note>
The TypeScript provider uses `DELTA_SYNC` indexes with self-managed embeddings: pass vectors directly. `DIRECT_ACCESS` indexes, Databricks-computed embeddings (`embedding_model_endpoint_name`), and Azure AD auth are Python-only today. It writes to the index through a SQL warehouse, so `httpPath` is required, and `@databricks/sql` must be installed as a peer dependency.
</Note>
</Tab>
</Tabs>
### Authentication