Usage
import os
from mem0 import Memory
os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM
config = {
"embedder": {
"provider": "ollama",
"config": {
"model": "mxbai-embed-large"
}
}
}
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="john")
import { Memory } from 'mem0ai/oss';
const config = {
embedder: {
provider: 'ollama',
config: {
model: 'nomic-embed-text:latest', // or any other Ollama embedding model
url: 'http://localhost:11434', // Ollama server URL
},
},
};
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: "john" });
Config
Here are the parameters available for configuring Ollama embedder:- Python
- TypeScript
| Parameter | Description | Default Value |
|---|---|---|
model | The name of the Ollama model to use | nomic-embed-text |
embedding_dims | Dimensions of the embedding model | 512 |
ollama_base_url | Base URL for ollama connection | None |
| Parameter | Description | Default Value |
|---|---|---|
model | The name of the Ollama model to use | nomic-embed-text:latest |
url | Base URL for Ollama server | http://localhost:11434 |
embeddingDims | Dimensions of the embedding model | 768 |