## Documentation Index

Fetch the complete documentation index at: [/llms.txt](https://docs.mem0.ai/llms.txt)

Use this file to discover all available pages before exploring further.

# Redis

[Redis](https://redis.io/) is a scalable, real-time database that can store, search, and analyze vector data.

### Installation

```
pip install redis redisvl
```

Redis Stack using Docker:

```
docker run -d --name redis-stack -p 6379:6379 -p 8001:8001 redis/redis-stack:latest
```

### Usage

Python

```
import os
from mem0 import Memory

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

config = {
    "vector_store": {
        "provider": "redis",
        "config": {
            "collection_name": "mem0",
            "embedding_model_dims": 1536,
            "redis_url": "redis://localhost:6379"
        }
    },
    "version": "v1.1"
}

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

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

const config = {
  vectorStore: {
    provider: 'redis',
    config: {
      collectionName: 'memories',
      embeddingModelDims: 1536,
      redisUrl: 'redis://localhost:6379',
      username: 'your-redis-username',
      password: 'your-redis-password',
    },
  },
};

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

Let’s see the available parameters for the `redis` config:

- Python
- TypeScript

| Parameter               | Description                                             | Default Value |
|-------------------------|---------------------------------------------------------|---------------|
| `collection_name`      | The name of the collection to store the vectors        | `mem0`        |
| `embedding_model_dims` | Dimensions of the embedding model                       | `1536`        |
| `redis_url`            | The URL of the Redis server                             | `None`        |

| Parameter               | Description                                             | Default Value |
|-------------------------|---------------------------------------------------------|---------------|
| `collectionName`       | The name of the collection to store the vectors        | `mem0`        |
| `embeddingModelDims`   | Dimensions of the embedding model                       | `1536`        |
| `redisUrl`             | The URL of the Redis server                             | `None`        |
| `username`             | Username for Redis connection                           | `None`        |
| `password`             | Password for Redis connection                           | `None`        |