Upstash Vector - Mem0

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.

<Note> 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. </Note>

python

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"})

<Note> Setting `enable_embeddings` to `True` will bypass any external embedding provider you have configured. </Note>

Usage with external embedding providers

<CodeGroup> ```python Python 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"})


```typescript TypeScript
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" },
});

</CodeGroup>

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`

<Note> When `url` and `token` are not provided, the `UPSTASH_VECTOR_REST_URL` and `UPSTASH_VECTOR_REST_TOKEN` environment variables are used. </Note> <Note> 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. </Note>