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

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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.

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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"})

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Config

Here are the parameters available for configuring Upstash Vector:
ParameterDescriptionDefault Value
urlURL for the Upstash Vector indexNone
tokenToken for the Upstash Vector indexNone
clientAn upstash_vector.Index instanceNone
collection_nameThe default namespace used""
enable_embeddingsWhether to use Upstash embeddingsFalse
When url and token are not provided, the UPSTASH_VECTOR_REST_URL and UPSTASH_VECTOR_REST_TOKEN environment variables are used.