feat(vector-stores): add Turbopuffer provider to TypeScript OSS SDK (#5801)

Co-authored-by: kartik-mem0 <kartik.labhshetwar@mem0.ai>
This commit is contained in:
Rod Boev
2026-07-06 22:57:00 +05:30
committed by GitHub
co-authored by kartik-mem0
parent 2bc2f763d9
commit 2cc060fd76
7 changed files with 725 additions and 2 deletions
+54 -2
View File
@@ -6,7 +6,8 @@ description: "Use Turbopuffer as a serverless vector database in Mem0 for low-la
### Usage
```python
<CodeGroup>
```python Python
import os
from mem0 import Memory
@@ -39,6 +40,36 @@ m.add(messages, user_id="alice", metadata={"category": "movies"})
results = m.search(query="sci-fi recommendations", filters={"user_id": "alice"})
```
```typescript TypeScript
import { Memory } from "mem0ai/oss";
// Set TURBOPUFFER_API_KEY in your environment, or pass it as config.apiKey below.
const config = {
vectorStore: {
provider: "turbopuffer",
config: {
collectionName: "movie_preferences",
region: "gcp-us-central1",
},
},
};
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 thrillers but I love sci-fi." },
{ role: "assistant", content: "Got it! I'll suggest sci-fi movies instead." },
];
await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });
// Search memories
const results = await memory.search("sci-fi recommendations", { userId: "alice" });
```
</CodeGroup>
### Config
Here are the parameters available for configuring Turbopuffer:
@@ -53,6 +84,10 @@ Here are the parameters available for configuring Turbopuffer:
| `batch_size` | Batch size for bulk operations | `100` |
| `extra_params` | Additional parameters for the Turbopuffer client | `None` |
<Note>
**TypeScript (Node.js) config keys** are camelCase: `collectionName`, `apiKey`, `region`, `distanceMetric`, and `batchSize`. The TypeScript SDK infers the vector dimension from your embedder, so `embeddingModelDims` is not required.
</Note>
### Regions
| Region | Location |
@@ -62,7 +97,8 @@ Here are the parameters available for configuring Turbopuffer:
### Config Example
```python
<CodeGroup>
```python Python
config = {
"vector_store": {
"provider": "turbopuffer",
@@ -77,3 +113,19 @@ config = {
}
}
```
```typescript TypeScript
const config = {
vectorStore: {
provider: "turbopuffer",
config: {
collectionName: "my_memories",
apiKey: "tpuf_xxxxxxxxxxxx",
region: "aws-us-west-2",
distanceMetric: "cosine_distance",
batchSize: 200,
},
},
};
```
</CodeGroup>