Apache Cassandra - Mem0

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

<CodeGroup> ```python 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) 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"})


```typescript TypeScript
import { Memory } from 'mem0ai/oss';

// Set OPENAI_API_KEY in your environment for the default embedder

const config = {
  vectorStore: {
    provider: 'cassandra',
    config: {
      contactPoints: ['127.0.0.1'],
      localDataCenter: 'datacenter1', // required with contactPoints; "datacenter1" is the default for a single-node cluster
      port: 9042,
      username: 'cassandra',
      password: 'cassandra',
      keyspace: 'mem0',
      collectionName: 'memories',
    },
  },
};

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" } });

</CodeGroup>

Using DataStax Astra DB

For managed Cassandra with DataStax Astra DB:

<CodeGroup> ```python 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:...', // Your Astra DB application token
      keyspace: 'mem0',
      collectionName: 'memories',
      secureConnectBundle: '/path/to/secure-connect-bundle.zip',
    },
  },
};

</CodeGroup> <Note> 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. </Note>

Config

Here are the parameters available for configuring Apache Cassandra:

Parameter	Description	Default Value
`contact_points`	List of contact point IP addresses	Required
`port`	Cassandra port	`9042`
`username`	Database username	`None`
`password`	Database password	`None`
`keyspace`	Keyspace name	`"mem0"`
`collection_name`	Table name for storing vectors	`"memories"`
`embedding_model_dims`	Dimensions of embedding vectors	`1536`
`secure_connect_bundle`	Path to Astra DB secure connect bundle	`None`
`protocol_version`	CQL protocol version	`4`
`load_balancing_policy`	Custom load balancing policy	`None`

<Note> The TypeScript SDK uses camelCase keys: `contactPoints`, `collectionName`, `embeddingModelDims`, `secureConnectBundle`, `protocolVersion`, and `loadBalancingPolicy`. It also requires `localDataCenter` (for example, `datacenter1`) when you connect with `contactPoints` instead of a secure connect bundle. The Node.js driver needs this to route queries; it has no default. </Note>

Setup

Option 1: Local Cassandra Setup using Docker:

bash

# 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):

Sign up at DataStax Astra
Create a new database
Download the secure connect bundle
Generate an application token

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

Option 3: Install Cassandra Locally:

Ubuntu/Debian:

bash

# 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:

bash

# Using Homebrew
brew install cassandra

# Start Cassandra
brew services start cassandra

# Connect to CQL shell
cqlsh

Client Installation

Install the driver for your SDK:

<CodeGroup> ```bash Python pip install cassandra-driver ```

bash

npm install cassandra-driver

</CodeGroup>

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

<CodeGroup> ```python 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 TypeScript
// The Node.js driver routes to localDataCenter by default, so set it to your
// primary DC for datacenter-aware routing. Pass loadBalancingPolicy only when
// you need a custom policy from the cassandra-driver package.
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,
    },
  },
};

</CodeGroup> <Warning> For production use, configure appropriate replication strategies and consistency levels based on your availability and consistency requirements. </Warning>