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.

import os
from mem0 import Memory

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

config = {
    "vector_store": {
        "provider": "upstash_vector",
        "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"})

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.