> ## 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

```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"})
```

### 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`                                      |

### Regions

| Region            | Location            |
| ----------------- | ------------------- |
| `gcp-us-central1` | Iowa, USA (Default) |
| `aws-us-west-2`   | Oregon, USA         |

### Config Example

```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,
        }
    }
}
```