Upstash Vector

Upstash Vector is a serverless vector database with built-in embedding models.

Usage with Upstash embeddings

You can enable the built-in embedding models by setting enable_embeddings to True. This allows you to use Upstash’s embedding models for vectorization.

Server-side Upstash embeddings (enable_embeddings) are available in the Python SDK only. The TypeScript SDK always embeds text with your configured embedder before writing to Upstash, so use the external embedding provider setup below.

import os
from mem0 import Memory

os.environ["UPSTASH_VECTOR_REST_URL"] = "..."
os.environ["UPSTASH_VECTOR_REST_TOKEN"] = "..."

config = {
    "vector_store": {
        "provider": "upstash_vector",
        "config": {
            "enable_embeddings": True,
        }
    }
}

m = Memory.from_config(config)
m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})

Setting enable_embeddings to True will bypass any external embedding provider you have configured.

Usage with external embedding providers

import os
from mem0 import Memory

os.environ["OPENAI_API_KEY"] = "..."
os.environ["UPSTASH_VECTOR_REST_URL"] = "..."
os.environ["UPSTASH_VECTOR_REST_TOKEN"] = "..."

config = {
    "vector_store": {
        "provider": "upstash_vector",
    },
    "embedder": {
        "provider": "openai",
        "config": {
            "model": "text-embedding-3-large"
        },
    }
}

m = Memory.from_config(config)
m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})

import { Memory } from "mem0ai/oss";

// Set OPENAI_API_KEY, UPSTASH_VECTOR_REST_URL, and UPSTASH_VECTOR_REST_TOKEN in your environment.
const config = {
  embedder: {
    provider: "openai",
    config: {
      apiKey: process.env.OPENAI_API_KEY,
      model: "text-embedding-3-large",
    },
  },
  vectorStore: {
    provider: "upstash_vector",
    config: {
      collectionName: "memories",
      url: process.env.UPSTASH_VECTOR_REST_URL,
      token: process.env.UPSTASH_VECTOR_REST_TOKEN,
    },
  },
};

const memory = new Memory(config);
await memory.add("Likes to play cricket on weekends", {
  userId: "alice",
  metadata: { category: "hobbies" },
});

Config

Here are the parameters available for configuring Upstash Vector:

Parameter	Description	Default Value
`url`	URL for the Upstash Vector index	`None`
`token`	Token for the Upstash Vector index	`None`
`client`	An `upstash_vector.Index` instance	`None`
`collection_name`	The default namespace used	`""`
`enable_embeddings`	Whether to use Upstash embeddings	`False`

When url and token are not provided, the UPSTASH_VECTOR_REST_URL and UPSTASH_VECTOR_REST_TOKEN environment variables are used.

The TypeScript SDK uses camelCase config keys (collectionName, url, token), where collectionName is required. Pass url and token (or a preconfigured client) explicitly, since the TypeScript SDK does not read them from environment variables. enable_embeddings is not supported in TypeScript.

​Usage with Upstash embeddings

​Usage with external embedding providers

​Config

Usage with Upstash embeddings

Usage with external embedding providers

Config