40 lines
1.5 KiB
Python
40 lines
1.5 KiB
Python
import os
|
|
from typing import Literal, Optional
|
|
|
|
from google import genai
|
|
from google.genai import types
|
|
|
|
from mem0.configs.embeddings.base import BaseEmbedderConfig
|
|
from mem0.embeddings.base import EmbeddingBase
|
|
|
|
|
|
class GoogleGenAIEmbedding(EmbeddingBase):
|
|
def __init__(self, config: Optional[BaseEmbedderConfig] = 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: Optional[Literal["add", "search", "update"]] = 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
|