40 lines
1.4 KiB
Python
40 lines
1.4 KiB
Python
import os
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from typing import Literal, Optional
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import google.genai as genai
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from mem0.configs.embeddings.base import BaseEmbedderConfig
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from mem0.embeddings.base import EmbeddingBase
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class GoogleGenAIEmbedding(EmbeddingBase):
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def __init__(self, config: Optional[BaseEmbedderConfig] = None):
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super().__init__(config)
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self.config.model = self.config.model or "models/text-embedding-004"
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self.config.embedding_dims = self.config.embedding_dims or self.config.output_dimensionality or 768
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api_key = self.config.api_key or os.getenv("GOOGLE_API_KEY")
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if api_key:
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self.client = genai.Client(api_key="api_key")
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else:
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self.client = genai.Client()
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def embed(self, text, memory_action: Optional[Literal["add", "search", "update"]] = None):
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"""
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Get the embedding for the given text using Google Generative AI.
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Args:
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text (str): The text to embed.
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memory_action (optional): The type of embedding to use. (Currently not used by Gemini for task_type)
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Returns:
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list: The embedding vector.
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"""
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text = text.replace("\n", " ")
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response = self.client.models.embed_content(
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model=self.config.model, content=text, output_dimensionality=self.config.embedding_dims
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)
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return response["embedding"]
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