MISTRAL_API_KEY environment variable to use the model as given below in the example.
Usage
import os
from mem0 import Memory
os.environ["OPENAI_API_KEY"] = "your-api-key" # used for embedding model
os.environ["MISTRAL_API_KEY"] = "your-api-key"
config = {
"llm": {
"provider": "litellm",
"config": {
"model": "open-mixtral-8x7b",
"temperature": 0.1,
"max_tokens": 2000,
}
}
}
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"})
import { Memory } from 'mem0ai/oss';
const config = {
llm: {
provider: 'mistral',
config: {
apiKey: process.env.MISTRAL_API_KEY || '',
model: 'mistral-tiny-latest', // Or 'mistral-small-latest', 'mistral-medium-latest', etc.
temperature: 0.1,
maxTokens: 2000,
},
},
};
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
All available parameters for thelitellm config are present in Master List of All Params in Config.