U h@sddlZddlmZmZmZmZddlmZddlm Z ddl m Z m Z ddl mZdZdZed d d d Gd dde e ZdS)N)AnyDictListOptional) deprecated) Embeddings) BaseModelroot_validator)get_from_dict_or_envz'sentence-transformers/all-mpnet-base-v2)feature-extractionz0.2.2z1.0z3langchain_huggingface.HuggingFaceEndpointEmbeddings)ZsinceZremovalZalternative_importc@seZdZUdZeed<eed<dZeeed<dZ eeed<dZ eeed<dZ ee ed <dZ eeed <Gd d d Zed deedddZedd deedddZeeeeedddZeeeeedddZeeedddZeeedddZdS) HuggingFaceHubEmbeddingsawHuggingFaceHub embedding models. To use, you should have the ``huggingface_hub`` python package installed, and the environment variable ``HUGGINGFACEHUB_API_TOKEN`` set with your API token, or pass it as a named parameter to the constructor. Example: .. code-block:: python from langchain_community.embeddings import HuggingFaceHubEmbeddings model = "sentence-transformers/all-mpnet-base-v2" hf = HuggingFaceHubEmbeddings( model=model, task="feature-extraction", huggingfacehub_api_token="my-api-key", ) client async_clientNmodelrepo_idr task model_kwargshuggingfacehub_api_tokenc@seZdZdZdS)zHuggingFaceHubEmbeddings.ConfigZforbidN)__name__ __module__ __qualname__extrarrR/tmp/pip-unpacked-wheel-9gdii04g/langchain_community/embeddings/huggingface_hub.pyConfig2srT)pre)valuesreturncCst|dd}zddlm}m}|dr6|d|d<n(|drN|d|d<nt|d<t|d<||d|d}||d|d}||d<||d <Wntk rtd YnX|S) z?Validate that api key and python package exists in environment.rZHUGGINGFACEHUB_API_TOKENr)AsyncInferenceClientInferenceClientrr)rtokenr rzfCould not import huggingface_hub python package. Please install it with `pip install huggingface_hub`.)r Zhuggingface_hubrrget DEFAULT_MODEL ImportError)clsrrrrr rrrrvalidate_environment5s8    z-HuggingFaceHubEmbeddings.validate_environmentF)rZskip_on_failurecCs*|dtkr&td|ddtd|S)z#Post init validation for the class.rzGot invalid task z, currently only z are supported) VALID_TASKS ValueError)r$rrrr post_init[s  z"HuggingFaceHubEmbeddings.post_init)textsrcCs@dd|D}|jpi}|jjd|i||jd}t|S)zCall out to HuggingFaceHub's embedding endpoint for embedding search docs. Args: texts: The list of texts to embed. Returns: List of embeddings, one for each text. cSsg|]}|ddqS  replace.0textrrr osz.inputsjsonr)rr postrr5loadsdecodeselfr)Z _model_kwargs responsesrrrembed_documentses   z(HuggingFaceHubEmbeddings.embed_documentscsDdd|D}|jpi}|jj||d|jdIdH}t|S)zAsync Call to HuggingFaceHub's embedding endpoint for embedding search docs. Args: texts: The list of texts to embed. Returns: List of embeddings, one for each text. cSsg|]}|ddqSr*r-r/rrrr2sz=HuggingFaceHubEmbeddings.aembed_documents..)r3 parametersr4N)rrr6rr5r7r8r9rrraembed_documentsws   z)HuggingFaceHubEmbeddings.aembed_documents)r1rcCs||gd}|S)zCall out to HuggingFaceHub's embedding endpoint for embedding query text. Args: text: The text to embed. Returns: Embeddings for the text. r)r<r:r1responserrr embed_querys z$HuggingFaceHubEmbeddings.embed_querycs||gIdHd}|S)zAsync Call to HuggingFaceHub's embedding endpoint for embedding query text. Args: text: The text to embed. Returns: Embeddings for the text. Nr)r>r?rrr aembed_querys z%HuggingFaceHubEmbeddings.aembed_query)rrr__doc__r__annotations__rrstrrrrdictrrr rr%r(rfloatr<r>rArBrrrrr s" %   r )r5typingrrrrZlangchain_core._apirZlangchain_core.embeddingsrZlangchain_core.pydantic_v1rr Zlangchain_core.utilsr r"r&r rrrrs