55 lines
2.3 KiB
Python
55 lines
2.3 KiB
Python
import os
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from typing import Literal, Optional
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from vertexai.language_models import TextEmbeddingInput, TextEmbeddingModel
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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 VertexAIEmbedding(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 "text-embedding-004"
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self.config.embedding_dims = self.config.embedding_dims or 256
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self.embedding_types = {
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"add": self.config.memory_add_embedding_type or "RETRIEVAL_DOCUMENT",
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"update": self.config.memory_update_embedding_type or "RETRIEVAL_DOCUMENT",
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"search": self.config.memory_search_embedding_type or "RETRIEVAL_QUERY"
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}
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credentials_path = self.config.vertex_credentials_json
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if credentials_path:
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os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = credentials_path
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elif not os.getenv("GOOGLE_APPLICATION_CREDENTIALS"):
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raise ValueError(
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"Google application credentials JSON is not provided. Please provide a valid JSON path or set the 'GOOGLE_APPLICATION_CREDENTIALS' environment variable."
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)
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self.model = TextEmbeddingModel.from_pretrained(self.config.model)
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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 Vertex 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. Must be one of "add", "search", or "update". Defaults to None.
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Returns:
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list: The embedding vector.
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"""
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embedding_type = "SEMANTIC_SIMILARITY"
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if memory_action is not None:
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if memory_action not in self.embedding_types:
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raise ValueError(f"Invalid memory action: {memory_action}")
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embedding_type = self.embedding_types[memory_action]
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text_input = TextEmbeddingInput(text=text, task_type=embedding_type)
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embeddings = self.model.get_embeddings(texts=[text_input], output_dimensionality=self.config.embedding_dims)
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return embeddings[0].values
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