Source code for neo4j_graphrag.embeddings.vertexai

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from __future__ import annotations

from typing import Any

from neo4j_graphrag.embeddings.base import Embedder

try:
    import vertexai
except ImportError:
    vertexai = None


[docs] class VertexAIEmbeddings(Embedder): """ Vertex AI embeddings class. This class uses the Vertex AI Python client to generate vector embeddings for text data. Args: model (str): The name of the Vertex AI text embedding model to use. Defaults to "text-embedding-004". """ def __init__(self, model: str = "text-embedding-004") -> None: if vertexai is None: raise ImportError( "Could not import Vertex AI Python client. " "Please install it with `pip install google-cloud-aiplatform`." ) self.vertexai_model = ( vertexai.language_models.TextEmbeddingModel.from_pretrained(model) )
[docs] def embed_query( self, text: str, task_type: str = "RETRIEVAL_QUERY", **kwargs: Any ) -> list[float]: """ Generate embeddings for a given query using a Vertex AI text embedding model. Args: text (str): The text to generate an embedding for. task_type (str): The type of the text embedding task. Defaults to "RETRIEVAL_QUERY". See https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/text-embeddings-api#tasktype for a full list. **kwargs (Any): Additional keyword arguments to pass to the Vertex AI client's get_embeddings method. """ inputs = [vertexai.language_models.TextEmbeddingInput(text, task_type)] embeddings = self.vertexai_model.get_embeddings(inputs, **kwargs) return embeddings[0].values # type: ignore