Google AI

To use Google AI embedding models, set the GOOGLE_API_KEY environment variables. You can obtain the Gemini API key from here.

Usage

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

config = { "embedder": { "provider": "gemini", "config": { "model": "models/gemini-embedding-001", } } }

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")

```typescript TypeScript
import { Memory } from 'mem0ai/oss';

const config = {
  embedder: {
      provider: "google",
      config: {
        apiKey: process.env["GOOGLE_API_KEY"],
        model: "gemini-embedding-001",
        embeddingDims: 1536,
      },
    },
};

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 Gemini embedder: <Tabs> <Tab title="Python">