pgvector

Use pgvector as a vector store in Mem0 for PostgreSQL-based vector similarity search with open-source simplicity.

pgvector is an open-source vector similarity search extension for Postgres. After connecting to Postgres, run CREATE EXTENSION IF NOT EXISTS vector; to create the vector extension.

Usage

Python

import os
from mem0 import Memory

os.environ["OPENAI_API_KEY"] = "sk-xx"

config = {
    "vector_store": {
        "provider": "pgvector",
        "config": {
            "user": "test",
            "password": "123",
            "host": "127.0.0.1",
            "port": "5432",
        },
    }
}

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"})

import { Memory } from "mem0ai/oss";

const config = {
  vectorStore: {
    provider: "pgvector",
    config: {
      collectionName: "memories",
      embeddingModelDims: 1536,
      connectionString: "postgresql://test:123@localhost:5432/vector_store",
      diskann: false, // Optional, requires pgvectorscale extension
      hnsw: false, // Optional, for HNSW indexing
    },
  },
};

const memory = new Memory(config);
const 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." },
];

await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });

Config

Here are the parameters available for configuring pgvector:

Parameter	SDK	Description	Default Value
connectionString	TypeScript OSS	PostgreSQL connection string for direct connections. When set, Mem0 connects to the target database directly and skips the bootstrap postgres database flow.	None
ssl	TypeScript OSS	SSL option passed directly to pg, either true or an SSL config object, for both connectionString and split-field connections.	None
dbname	TypeScript OSS	Split-field database name. This is only used when connectionString is absent.	vector_store
collectionName	TypeScript OSS	Collection name.	memories
embeddingModelDims	TypeScript OSS	Dimensions of the embedding model.	Required
user	TypeScript OSS + Python	Database user for split-field connections.	None
password	TypeScript OSS + Python	Database password for split-field connections.	None
host	TypeScript OSS + Python	Database host for split-field connections.	None
port	TypeScript OSS + Python	Database port for split-field connections.	None
diskann	TypeScript OSS + Python	Whether to use DiskANN for vector similarity search, requires pgvectorscale.	False
hnsw	TypeScript OSS + Python	Whether to use HNSW for vector similarity search.	TypeScript OSS: False, Python: True
connection_string	Python only	PostgreSQL connection string, overrides individual connection parameters.	None
sslmode	Python only	SSL mode for PostgreSQL connections, such as require, prefer, or disable.	None
connection_pool	Python only	psycopg connection pool object, overrides connection string and individual connection parameters.	None

TypeScript OSS: Use connectionString plus optional ssl for managed Postgres setups. If you omit connectionString, Mem0 falls back to split fields and uses dbname, user, password, host, port, and optional ssl.

Python: The Python SDK uses snake_case keys such as connection_string, sslmode, collection_name, and embedding_model_dims.

Python connection priority:

1. connection_pool (highest priority)
2. connection_string
3. Individual connection parameters (user, password, host, port, sslmode)