> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mem0.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Turbopuffer

> Use Turbopuffer as a serverless vector database in Mem0 for low-latency search at scale with native metadata filtering.

[Turbopuffer](https://turbopuffer.com) is a serverless vector database optimized for low-latency search at scale. It offers cost-effective vector storage with native metadata filtering.

### Usage

<CodeGroup>
  ```python Python theme={null}
  import os
  from mem0 import Memory

  os.environ["OPENAI_API_KEY"] = "sk-xx"
  os.environ["TURBOPUFFER_API_KEY"] = "tpuf_xxxxxxxxxxxx"

  config = {
      "vector_store": {
          "provider": "turbopuffer",
          "config": {
              "collection_name": "movie_preferences",
              "embedding_model_dims": 1536,
              "region": "gcp-us-central1",
          }
      }
  }

  m = Memory.from_config(config)

  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."}
  ]

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

  # Search memories
  results = m.search(query="sci-fi recommendations", filters={"user_id": "alice"})
  ```

  ```typescript TypeScript theme={null}
  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:

| Parameter              | Description                                                                      | Default Value                               |
| ---------------------- | -------------------------------------------------------------------------------- | ------------------------------------------- |
| `collection_name`      | Name of the namespace/collection                                                 | `mem0`                                      |
| `embedding_model_dims` | Dimensions of the embedding model (must match your chosen embedding model)       | `1536`                                      |
| `api_key`              | Turbopuffer API key                                                              | Environment variable: `TURBOPUFFER_API_KEY` |
| `region`               | Turbopuffer region                                                               | `gcp-us-central1`                           |
| `distance_metric`      | Distance metric for vector similarity (`cosine_distance` or `euclidean_squared`) | `cosine_distance`                           |
| `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            |
| ----------------- | ------------------- |
| `gcp-us-central1` | Iowa, USA (Default) |
| `aws-us-west-2`   | Oregon, USA         |

### Config Example

<CodeGroup>
  ```python Python theme={null}
  config = {
      "vector_store": {
          "provider": "turbopuffer",
          "config": {
              "collection_name": "my_memories",
              "embedding_model_dims": 1536,
              "api_key": "tpuf_xxxxxxxxxxxx",
              "region": "aws-us-west-2",
              "distance_metric": "cosine_distance",
              "batch_size": 200,
          }
      }
  }
  ```

  ```typescript TypeScript theme={null}
  const config = {
    vectorStore: {
      provider: "turbopuffer",
      config: {
        collectionName: "my_memories",
        apiKey: "tpuf_xxxxxxxxxxxx",
        region: "aws-us-west-2",
        distanceMetric: "cosine_distance",
        batchSize: 200,
      },
    },
  };
  ```
</CodeGroup>