> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mem0.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Redis

> Use Redis as a real-time vector database in Mem0 for fast vector search using Redis Stack and redisvl.

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

### Installation

```bash theme={null}
pip install redis redisvl
```

Redis Stack using Docker:

```bash theme={null}
docker run -d --name redis-stack -p 6379:6379 -p 8001:8001 redis/redis-stack:latest
```

### Usage

<CodeGroup>
  ```python Python theme={null}
  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 TypeScript theme={null}
  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" } });
  ```
</CodeGroup>

### Config

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

<Tabs>
  <Tab title="Python">
    | 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`        |
  </Tab>

  <Tab title="TypeScript">
    | 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`        |
  </Tab>
</Tabs>