To use Gemini embedding models, set the GOOGLE_API_KEY environment variables. You can obtain the Gemini API key from here.
import os
from Zentry import Memory
os.environ["GOOGLE_API_KEY"] = "key"
os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM
config = {
"embedder": {
"provider": "gemini",
"config": {
"model": "models/text-embedding-004",
}
}
}
m = Memory.from_config(config)
messages = [
{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
{"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
{"role": "user", "content": "Iām 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")
Here are the parameters available for configuring Gemini embedder:
| Parameter | Description | Default Value |
|---|
model | The name of the embedding model to use | models/text-embedding-004 |
embedding_dims | Dimensions of the embedding model | 768 |
api_key | The Gemini API key | None |