o Zh@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@s.eZdZUdZdZeed<dZeed<dZe e ed< dZ e e ed< dZ e e ed< dZ e eed < dZe e ed <ed d d ZeddededefddZedddefddZdee deeefddZdee deeefddZde deefddZde deefdd 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)modevaluesreturncCst|dd}zCddlm}m}|dr|d|d<n|dr'|d|d<nt|d<t|d<||d|d}||d|d}||d<||d <W|StyTtd w) 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)clsrrrrrrrre/var/www/html/lang_env/lib/python3.10/site-packages/langchain_community/embeddings/huggingface_hub.pyvalidate_environment5s6    z-HuggingFaceHubEmbeddings.validate_environmentaftercCs&|jtvrtd|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_inittextscCs@dd|D}|jp i}|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. cSg|]}|ddqS  replace.0textrrr# pz.inputsjsonr)rrpostrr8loadsdecoder(r*Z _model_kwargs responsesrrr#embed_documentsfs  z(HuggingFaceHubEmbeddings.embed_documentscsFdd|D}|jp i}|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. cSr+r,r/r1rrr#r4r5z=HuggingFaceHubEmbeddings.aembed_documents..)r6 parametersr7N)rrr9rr8r:r;r<rrr#aembed_documentsxs   z)HuggingFaceHubEmbeddings.aembed_documentsr3cCs||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(r3responserrr# embed_querys z$HuggingFaceHubEmbeddings.embed_querycs||gIdHd}|S)zAsync Call to HuggingFaceHub's embedding endpoint for embedding query text. Args: text: The text to embed. Returns: Embeddings for the text. Nr)r@rArrr# aembed_querys z%HuggingFaceHubEmbeddings.aembed_query)__name__ __module__ __qualname____doc__rr__annotations__rrrstrrrrdictrr Z model_configr classmethodrr$r r)rfloatr>r@rCrDrrrr#rs.    %  r)r8typingrrrrZlangchain_core._apirZlangchain_core.embeddingsrZlangchain_core.utilsrZpydanticr r r Ztyping_extensionsr r r&rrrrr#s