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 is a scalable, real-time database that can store, search, and analyze vector data.
Installation
pip install redis redisvl
docker run -d --name redis-stack -p 6379:6379 -p 8001:8001 redis/redis-stack:latest
Usage
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"})
Config
Let’s see the available parameters for the redis config:
| 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 |
