Apache Cassandra - Mem0

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Apache Cassandra is a highly scalable, distributed NoSQL database designed for handling large amounts of data across many commodity servers with no single point of failure. It supports vector storage for semantic search capabilities in AI applications and can scale to massive datasets with linear performance improvements.

Usage

Python

import os
from mem0 import Memory

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

config = {
    "vector_store": {
        "provider": "cassandra",
        "config": {
            "contact_points": ["127.0.0.1"],
            "port": 9042,
            "username": "cassandra",
            "password": "cassandra",
            "keyspace": "mem0",
            "collection_name": "memories",
        }
    }
}

m = Memory.from_config(config)
m.add(messages, user_id="alice", metadata={"category": "movies"})

TypeScript

import { Memory } from 'mem0ai/oss';

const config = {
  vectorStore: {
    provider: 'cassandra',
    config: {
      contactPoints: ['127.0.0.1'],
      localDataCenter: 'datacenter1',
      port: 9042,
      username: 'cassandra',
      password: 'cassandra',
      keyspace: 'mem0',
      collectionName: 'memories',
    },
  },
};

const memory = new Memory(config);
await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });

Using DataStax Astra DB

For managed Cassandra with DataStax Astra DB:

Python

config = {
    "vector_store": {
        "provider": "cassandra",
        "config": {
            "contact_points": ["dummy"],  # Not used with secure connect bundle
            "username": "token",
            "password": "AstraCS:...",  # Your Astra DB application token
            "keyspace": "mem0",
            "collection_name": "memories",
            "secure_connect_bundle": "/path/to/secure-connect-bundle.zip"
        }
    }
}

TypeScript

const config = {
  vectorStore: {
    provider: 'cassandra',
    config: {
      username: 'token',
      password: 'AstraCS:...',
      keyspace: 'mem0',
      collectionName: 'memories',
      secureConnectBundle: '/path/to/secure-connect-bundle.zip',
    },
  },
};

When using DataStax Astra DB, provide the secure connect bundle path. Contact points and localDataCenter are not needed when a secure connect bundle is provided.

Config

Here are the parameters available for configuring Apache Cassandra:

Setup

Option 1: Local Cassandra Setup using Docker:

# Pull and run Cassandra container
docker run --name mem0-cassandra \
    -p 9042:9042 \
    -e CASSANDRA_CLUSTER_NAME="Mem0Cluster" \
    -d cassandra:latest

# Wait for Cassandra to start (may take 1-2 minutes)
docker exec -it mem0-cassandra cqlsh

# Create keyspace
CREATE KEYSPACE IF NOT EXISTS mem0
WITH replication = {'class': 'SimpleStrategy', 'replication_factor': 1};

Option 2: DataStax Astra DB (Managed Cloud):

1. Sign up at DataStax Astra
2. Create a new database
3. Download the secure connect bundle
4. Generate an application token

For production deployments, use DataStax Astra DB for fully managed Cassandra with automatic scaling, backups, and security.

Option 3: Install Cassandra Locally:

Ubuntu/Debian:

# Add Apache Cassandra repository
echo "deb https://downloads.apache.org/cassandra/debian 40x main" | sudo tee -a /etc/apt/sources.list.d/cassandra.sources.list
curl https://downloads.apache.org/cassandra/KEYS | sudo apt-key add -

# Install Cassandra
sudo apt-get update
sudo apt-get install cassandra

# Start Cassandra
sudo systemctl start cassandra

# Verify installation
nodetool status

macOS:

# Using Homebrew
brew install cassandra

# Start Cassandra
brew services start cassandra

# Connect to CQL shell
cqlsh

Client Installation

Install the driver for your SDK:

Python

pip install cassandra-driver

TypeScript

npm install cassandra-driver

Performance Considerations

• Replication Factor: For production, use replication factor of at least 3
• Consistency Level: Balance between consistency and performance (QUORUM recommended)
• Partitioning: Cassandra automatically distributes data across nodes
• Scaling: Add nodes to linearly increase capacity and performance

Advanced Configuration

Python

from cassandra.policies import DCAwareRoundRobinPolicy

config = {
    "vector_store": {
        "provider": "cassandra",
        "config": {
            "contact_points": ["node1.example.com", "node2.example.com", "node3.example.com"],
            "port": 9042,
            "username": "mem0_user",
            "password": "secure_password",
            "keyspace": "mem0_prod",
            "collection_name": "memories",
            "protocol_version": 4,
            "load_balancing_policy": DCAwareRoundRobinPolicy(local_dc='DC1')
        }
    }
}

TypeScript

const config = {
  vectorStore: {
    provider: 'cassandra',
    config: {
      contactPoints: ['node1.example.com', 'node2.example.com', 'node3.example.com'],
      localDataCenter: 'DC1',
      port: 9042,
      username: 'mem0_user',
      password: 'secure_password',
      keyspace: 'mem0_prod',
      collectionName: 'memories',
      protocolVersion: 4,
    },
  },
};

For production use, configure appropriate replication strategies and consistency levels based on your availability and consistency requirements.