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

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Usage

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", user_id="alice")

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Config

Here are the parameters available for configuring Turbopuffer:
ParameterDescriptionDefault Value
collection_nameName of the namespace/collectionmem0
embedding_model_dimsDimensions of the embedding model (must match your chosen embedding model)1536
api_keyTurbopuffer API keyEnvironment variable: TURBOPUFFER_API_KEY
regionTurbopuffer regiongcp-us-central1
distance_metricDistance metric for vector similarity (cosine_distance or euclidean_squared)cosine_distance
batch_sizeBatch size for bulk operations100
extra_paramsAdditional parameters for the Turbopuffer clientNone

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Regions

RegionLocation
gcp-us-central1Iowa, USA (Default)
aws-us-west-2Oregon, USA

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Config Example

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,
        }
    }
}