gemini.py•1.59 kB
import os
from typing import Literal
from google import genai
from google.genai import types
from selfmemory.configs.embeddings.base import BaseEmbedderConfig
from selfmemory.embeddings.base import EmbeddingBase
class GoogleGenAIEmbedding(EmbeddingBase):
def __init__(self, config: BaseEmbedderConfig | None = None):
super().__init__(config)
self.config.model = self.config.model or "models/text-embedding-004"
self.config.embedding_dims = (
self.config.embedding_dims or self.config.output_dimensionality or 768
)
api_key = self.config.api_key or os.getenv("GOOGLE_API_KEY")
self.client = genai.Client(api_key=api_key)
def embed(
self, text, memory_action: Literal["add", "search", "update"] | None = None
):
"""
Get the embedding for the given text using Google Generative AI.
Args:
text (str): The text to embed.
memory_action (optional): The type of embedding to use. Must be one of "add", "search", or "update". Defaults to None.
Returns:
list: The embedding vector.
"""
text = text.replace("\n", " ")
# Create config for embedding parameters
config = types.EmbedContentConfig(
output_dimensionality=self.config.embedding_dims
)
# Call the embed_content method with the correct parameters
response = self.client.models.embed_content(
model=self.config.model, contents=text, config=config
)
return response.embeddings[0].values