FastEmbed

pip install fastembed

npm install fastembed

import os
from mem0 import Memory

os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM

config = {
    "embedder": {
        "provider": "fastembed",
        "config": {
            "model": "thenlper/gte-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";

// FastEmbed needs no API key. Leave the embedder config empty to use the
// default model (fast-bge-small-en-v1.5), or set `model` to one of the
// supported models listed below.
const memory = new Memory({
  embedder: {
    provider: "fastembed",
    config: {
      model: "fast-bge-small-en-v1.5",
    },
  },
  llm: {
    provider: "openai",
    config: { apiKey: process.env.OPENAI_API_KEY }, // For fact extraction
  },
});

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" });