Turbopuffer

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

Turbopuffer is a serverless vector database optimized for low-latency search at scale. It offers cost-effective vector storage with native metadata filtering.

Usage

Python

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"})

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" });

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

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.

Regions

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

Config Example

Python

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,
        }
    }
}

const config = {
  vectorStore: {
    provider: "turbopuffer",
    config: {
      collectionName: "my_memories",
      apiKey: "tpuf_xxxxxxxxxxxx",
      region: "aws-us-west-2",
      distanceMetric: "cosine_distance",
      batchSize: 200,
    },
  },
};