Zǻi: ddlmZddlmZmZmZddlmZddlm Z ddl m Z m Z  ddl mZmZerddl mZmZGdd eZy#eef$rdxZZY#wxYw) ) annotations) TYPE_CHECKINGAnyOptional)Embedder)EmbeddingsGenerationError)RateLimitHandlerrate_limit_handler)TextEmbeddingInputTextEmbeddingModelNcXeZdZdZ d dfd Ze d ddZxZS)VertexAIEmbeddingsz 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". czt tdt| |tj||_y)NzxCould not import Vertex AI Python client. Please install it with `pip install "neo4j-graphrag[google]"`.)r ImportErrorsuper__init__from_pretrainedmodel)selfrr __class__s `/opt/lhia/marcimex/agent/venv/lib/python3.12/site-packages/neo4j_graphrag/embeddings/vertexai.pyrzVertexAIEmbeddings.__init__*s@  %R  +,'77> c  t||g}|jj|fi|}t|djS#t $r}t d||d}~wwxYw)a Generate embeddings for a given query using a Vertex AI text embedding model. Args: text (str): The text to generate an embedding for. task_type (str): The type of the text embedding task. Defaults to "RETRIEVAL_QUERY". See https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/text-embeddings-api#tasktype for a full list. **kwargs (Any): Additional keyword arguments to pass to the Vertex AI client's get_embeddings method. rz,Failed to generate embedding with VertexAI: N)r rget_embeddingslistvalues Exceptionr)rtext task_typekwargsinputs embeddingses r embed_queryzVertexAIEmbeddings.embed_query7sv #436F3226DVDJ 1 ,,- - +>qcB  sAA A! AA!)ztext-embedding-004N)rstrr zOptional[RateLimitHandler]returnNone)RETRIEVAL_QUERY)rr%rr%r rr&z list[float])__name__ __module__ __qualname____doc__rr r$ __classcell__)rs@rrr!sh*9= ? ?7 ?  ?*;$'GJ rr) __future__rtypingrrrneo4j_graphrag.embeddings.baserneo4j_graphrag.exceptionsrneo4j_graphrag.utils.rate_limitr r vertexai.language_modelsr r rAttributeErrorrrrr6sW#//3?P3O O,, ^$3.22+3sA AA