a bg@sddlZddlmZmZmZmZddlmZddlm Z ddl m Z ddl m Z mZmZddlmZdZd Zed d d d Gddde e ZdS)N)AnyDictListOptional) deprecated) Embeddings)get_from_dict_or_env) BaseModel ConfigDictmodel_validator)Selfz'sentence-transformers/all-mpnet-base-v2)feature-extractionz0.2.2z1.0z3langchain_huggingface.HuggingFaceEndpointEmbeddings)ZsinceZremovalZalternative_importc@seZdZUdZdZeed<dZeed<dZe e ed<dZ e e ed<dZ e e ed<dZ e eed <dZe e ed <ed d d ZeddeeedddZeddedddZee eeedddZee eeedddZe eedddZe eeddd ZdS)!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", ) Nclient async_clientmodelrepo_idr task model_kwargshuggingfacehub_api_tokenZforbid)extraZprotected_namespacesbefore)mode)valuesreturncCst|dd}zddlm}m}|dr6|d|d<n(|drN|d|d<nt|d<t|d<||d|d}||d|d}||d<||d <Wntytd Yn0|S) z?Validate that api key and python package exists in environment.rZHUGGINGFACEHUB_API_TOKENr)AsyncInferenceClientInferenceClientrr)rtokenrrzfCould not import huggingface_hub python package. Please install it with `pip install huggingface_hub`.)rZhuggingface_hubrrget DEFAULT_MODEL ImportError)clsrrrrrrrr|/var/www/html/cobodadashboardai.evdpl.com/venv/lib/python3.9/site-packages/langchain_community/embeddings/huggingface_hub.pyvalidate_environment5s4     z-HuggingFaceHubEmbeddings.validate_environmentafter)rcCs&|jtvr"td|jdtd|S)z#Post init validation for the class.zGot invalid task z, currently only z are supported)r VALID_TASKS ValueError)selfrrr# 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# pz.inputsjsonr)rrpostrr7loadsdecoder(r*Z _model_kwargs responsesrrr#embed_documentsfs  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.r0rrr#r3r4z=HuggingFaceHubEmbeddings.aembed_documents..)r5 parametersr6N)rrr8rr7r9r:r;rrr#aembed_documentsxs   z)HuggingFaceHubEmbeddings.aembed_documents)r2rcCs||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(r2responserrr# embed_querys z$HuggingFaceHubEmbeddings.embed_querycs||gIdHd}|S)zAsync Call to HuggingFaceHub's embedding endpoint for embedding query text. Args: text: The text to embed. Returns: Embeddings for the text. Nr)r?r@rrr# aembed_querys z%HuggingFaceHubEmbeddings.aembed_query)__name__ __module__ __qualname____doc__rr__annotations__rrrstrrrrdictrr Z model_configr classmethodrr$r r)rfloatr=r?rBrCrrrr#rs$    %  r)r7typingrrrrZlangchain_core._apirZlangchain_core.embeddingsrZlangchain_core.utilsrZpydanticr r r Ztyping_extensionsr r r&rrrrr#s