Ig70ddlmZmZmZmZmZmZmZmZddl Z ddl m Z ddl mZddlmZmZddlmZmZddlmZdd d d d d didddd dd dddddddddd dddiddddd d dd d iddd!ddd d"dd#iddd$d%d&d d'd d(iddd)dd*d+d,dd-iddd.dd/d ddd0iddd1dd2d+d3dd1id4dd5dd2d+d'dd5id4dd6dd2d+ddd6id4dd7d d2d+d8dd9id4dd:d d;d+ddd=id4dgZGd?d@eee j0ZGdAdBeZy)C)AnyDictIterableListOptionalSequenceTypeUnionN) OnnxProvider)OnnxOutputContext)define_cache_dir normalize) OnnxTextModelTextEmbeddingWorker)TextEmbeddingBasezBAAI/bge-base-eniz}Text embeddings, Unimodal (text), English, 512 input tokens truncation, Prefixes for queries/documents: necessary, 2023 year.mitgzG?urlzGhttps://storage.googleapis.com/qdrant-fastembed/fast-bge-base-en.tar.gzzmodel_optimized.onnx)modeldim descriptionlicense size_in_GBsources model_filezBAAI/bge-base-en-v1.5zText embeddings, Unimodal (text), English, 512 input tokens truncation, Prefixes for queries/documents: not so necessary, 2023 year.gzG?zLhttps://storage.googleapis.com/qdrant-fastembed/fast-bge-base-en-v1.5.tar.gzzqdrant/bge-base-en-v1.5-onnx-q)rhfzBAAI/bge-large-en-v1.5ig333333?rzqdrant/bge-large-en-v1.5-onnxz model.onnxzBAAI/bge-small-enigp= ף?zHhttps://storage.googleapis.com/qdrant-fastembed/BAAI-bge-small-en.tar.gzBAAI/bge-small-en-v1.5gx&?zqdrant/bge-small-en-v1.5-onnx-qzBAAI/bge-small-zh-v1.5izText embeddings, Unimodal (text), Chinese, 512 input tokens truncation, Prefixes for queries/documents: not so necessary, 2023 year.g ףp= ?zMhttps://storage.googleapis.com/qdrant-fastembed/fast-bge-small-zh-v1.5.tar.gzz;sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2zText embeddings, Unimodal (text), Multilingual (~50 languages), 512 input tokens truncation, Prefixes for queries/documents: not necessary, 2019 year.z apache-2.0g)\(?z3qdrant/paraphrase-multilingual-MiniLM-L12-v2-onnx-Qzthenlper/gte-largezText embeddings, Unimodal (text), English, 512 input tokens truncation, Prefixes for queries/documents: not necessary, 2023 year.zqdrant/gte-large-onnxz"mixedbread-ai/mxbai-embed-large-v1z}Text embeddings, Unimodal (text), English, 512 input tokens truncation, Prefixes for queries/documents: necessary, 2024 year.g{Gz?zonnx/model.onnxz#snowflake/snowflake-arctic-embed-xsz"snowflake/snowflake-arctic-embed-sz"snowflake/snowflake-arctic-embed-mgQ?z"Snowflake/snowflake-arctic-embed-mz'snowflake/snowflake-arctic-embed-m-longz~Text embeddings, Unimodal (text), English, 2048 input tokens truncation, Prefixes for queries/documents: necessary, 2024 year.gHzG?z"snowflake/snowflake-arctic-embed-lgRQ?ceZdZdZedeeeeffdZ ddede ede e de e e ded e ee d ed e e ffd Z dd eeeefde de e deej&fdZededfdZdeeej&fdeeej&ffdZdedeej&fdZddZxZS)OnnxTextEmbeddingz+Implementation of the Flag Embedding model.returnctS)z Lists the supported models. 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Mutually exclusive with `providers` Defaults to False. device_ids (Optional[List[int]], optional): The list of device ids to use for data parallel processing in workers. Should be used with `cuda=True`, mutually exclusive with `providers`. Defaults to None. lazy_load (bool, optional): Whether to load the model during class initialization or on demand. Should be set to True when using multiple-gpu and parallel encoding. Defaults to False. device_id (Optional[int], optional): The device id to use for loading the model in the worker process. Raises: ValueError: If the model_name is not in the format / e.g. BAAI/bge-base-en. Nr)local_files_only)super__init__r*r-r,r+r._get_model_descriptionmodel_descriptionr r(download_model_local_files_only _model_dirload_onnx_model) selfr'r(r)r*r+r,r-r.kwargs __class__s r$r2zOnnxTextEmbedding.__init__s@ YB6B""%   &DN __ (!__Q/DN!DN!%! 1, data-parallel encoding will be used, recommended for offline encoding of large datasets. 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