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Use Neon as a vector store in Mem0, powered by PostgreSQL and the pgvector extension. Neon is a serverless Postgres platform. Since Mem0 supports Postgres through the pgvector provider, Neon can be used with a standard Postgres connection string.

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Usage

import os

from dotenv import load_dotenv
from mem0 import Memory

load_dotenv()

config = {
    "vector_store": {
        "provider": "pgvector",
        "config": {
            "connection_string": os.environ["DATABASE_URL"],
            "collection_name": "memories",
            "embedding_model_dims": 1536,
            "hnsw": True,
        },
    },
}

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 thriller movies but I love sci-fi movies."},
    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."},
]
m.add(messages, user_id="alice", metadata={"category": "movies"})

results = m.search(
    "What movies should I recommend?",
    filters={"user_id": "alice"},
)

print(results)

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SQL Migration

You don’t need to run any SQL migrations. Mem0 creates the collection table when it initializes the pgvector store.

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Environment

OPENAI_API_KEY=sk-xx...
DATABASE_URL=postgresql://user:[email protected]/neondb?sslmode=require

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Config

ParameterDescriptionDefault Value
connection_stringNeon Postgres connection string.Required
collection_nameName for the vector collection.mem0
embedding_model_dimsEmbedding model dimensions.1536
hnswEnables HNSW indexing.False
sslmodePostgreSQL SSL mode. Use require for Neon.Driver default

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Indexing

The pgvector provider can create an HNSW index for faster vector search.
  • Set hnsw to true to enable a Hierarchical Navigable Small World index.
  • Leave hnsw as false if you want to create or manage indexes yourself.
The pgvector provider uses cosine similarity for vector search. Make sure your embedding dimensions match the configured embedding_model_dims value.

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Best Practices

  1. Index Selection:
    • Use hnsw for faster search performance when memory usage is not a constraint
    • Manage indexes manually if you need a different pgvector index strategy
  2. Connection String:
    • Always use environment variables or even better, a secret manager for sensitive information in the connection string
    • Format: postgresql://user:password@host:port/database