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#1	[Redis](https://redis.io/) is a scalable, real-time database that can store, search, and analyze vector data.
#2
#3	### Installation
#4	```bash
#5	pip install redis redisvl
#6	```
#7
#8	Redis Stack using Docker:
#9	```bash
#10	docker run -d --name redis-stack -p 6379:6379 -p 8001:8001 redis/redis-stack:latest
#11	```
#12
#13	### Usage
#14
#15	<CodeGroup>
#16	```python Python
#17	import os
#18	from mem0 import Memory
#19
#20	os.environ["OPENAI_API_KEY"] = "sk-xx"
#21
#22	config = {
#23	"vector_store": {
#24	"provider": "redis",
#25	"config": {
#26	"collection_name": "mem0",
#27	"embedding_model_dims": 1536,
#28	"redis_url": "redis://localhost:6379"
#29	}
#30	},
#31	"version": "v1.1"
#32	}
#33
#34	m = Memory.from_config(config)
#35	messages = [
#36	{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
#37	{"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
#38	{"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
#39	{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
#40	]
#41	m.add(messages, user_id="alice", metadata={"category": "movies"})
#42	```
#43
#44	```typescript TypeScript
#45	import { Memory } from 'mem0ai/oss';
#46
#47	const config = {
#48	vectorStore: {
#49	provider: 'redis',
#50	config: {
#51	collectionName: 'memories',
#52	embeddingModelDims: 1536,
#53	redisUrl: 'redis://localhost:6379',
#54	username: 'your-redis-username',
#55	password: 'your-redis-password',
#56	},
#57	},
#58	};
#59
#60	const memory = new Memory(config);
#61	const messages = [
#62	{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
#63	{"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
#64	{"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
#65	{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
#66	]
#67	await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });
#68	```
#69	</CodeGroup>
#70
#71	### Config
#72
#73	Let's see the available parameters for the `redis` config:
#74
#75	<Tabs>
#76	<Tab title="Python">
#77	\| Parameter \| Description \| Default Value \|
#78	\| --- \| --- \| --- \|
#79	\| `collection_name` \| The name of the collection to store the vectors \| `mem0` \|
#80	\| `embedding_model_dims` \| Dimensions of the embedding model \| `1536` \|
#81	\| `redis_url` \| The URL of the Redis server \| `None` \|
#82	</Tab>
#83	<Tab title="TypeScript">
#84	\| Parameter \| Description \| Default Value \|
#85	\| --- \| --- \| --- \|
#86	\| `collectionName` \| The name of the collection to store the vectors \| `mem0` \|
#87	\| `embeddingModelDims` \| Dimensions of the embedding model \| `1536` \|
#88	\| `redisUrl` \| The URL of the Redis server \| `None` \|
#89	\| `username` \| Username for Redis connection \| `None` \|
#90	\| `password` \| Password for Redis connection \| `None` \|
#91	</Tab>
#92	</Tabs>

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