docs/components/vectordbs/dbs/redis.mdx
Redis is a scalable, real-time database that can store, search, and analyze vector data.
pip install redis redisvl
Redis Stack using Docker:
docker run -d --name redis-stack -p 6379:6379 -p 8001:8001 redis/redis-stack:latest
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
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" } });
Let's see the available parameters for the redis config: