tj ddlmZmZmZmZddlmZddlmZddl m Z m Z ddl m Z Gdde eeeeefZGdd e eZd S) )AnyDictListOptional) Embeddings)pre_init) BaseModel ConfigDict)ContentHandlerBaseceZdZdZdS)EmbeddingsContentHandlerzContent handler for LLM class.N)__name__ __module__ __qualname____doc__o/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/langchain_community/embeddings/sagemaker_endpoint.pyr r s((((rr c neZdZUdZ dZeed<dZeed< dZ eed< dZ e eed< e ed< dZ e eed < dZe eed < ed d d ZededefdZdeedeeefdZ ddeededeeefdZdedeefdZdS)SagemakerEndpointEmbeddingsaCustom Sagemaker Inference Endpoints. To use, you must supply the endpoint name from your deployed Sagemaker model & the region where it is deployed. To authenticate, the AWS client uses the following methods to automatically load credentials: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html If a specific credential profile should be used, you must pass the name of the profile from the ~/.aws/credentials file that is to be used. Make sure the credentials / roles used have the required policies to access the Sagemaker endpoint. See: https://docs.aws.amazon.com/IAM/latest/UserGuide/access_policies.html Nclient endpoint_name region_namecredentials_profile_namecontent_handler model_kwargsendpoint_kwargsTforbidr)arbitrary_types_allowedextraprotected_namespacesvaluesreturnc||d|S ddl} |d||d}n|}|d|d|d<n%#t$r}t d ||d}~wwxYwn#t $rt d wxYw|S) z.Dont do anything if client provided externallyrNrr) profile_namezsagemaker-runtimer)rzCould not load credentials to authenticate with AWS client. Please check that credentials in the specified profile name are valid. zRCould not import boto3 python package. Please install it with `pip install boto3`.)getboto3Sessionr Exception ValueError ImportError)clsr#r(sessiones rvalidate_environmentz0SagemakerEndpointEmbeddings.validate_environmentus# ::h   +MX  LLL 45A#mm%+,F%G,GG $mmooG#*>>'VM5J$2$$x      3/033   !   >    s/BAA98B9 BBBBB9textscttd|}|jpi}|jpi}|j||}|jj}|jj} |jj d|j |||d|}n$#t$r}td|d}~wwxYw|j |dS)z3Call out to SageMaker Inference embedding endpoint.c.|ddS)N  )replace)xs rz=SagemakerEndpointEmbeddings._embedding_func..s199T3#7#7r) EndpointNameBody ContentTypeAcceptz$Error raised by inference endpoint: Nr:r)listmaprrrtransform_input content_typeacceptsrinvoke_endpointrr*r+transform_output) selfr1 _model_kwargs_endpoint_kwargsbodyr@rAresponser/s r_embedding_funcz+SagemakerEndpointEmbeddings._embedding_funcsS77??@@)/R /52#33E=II+8 &. I2t{2!/(  # HH I I IGAGGHH H I#44Xf5EFFFs%B B# BB#@ chunk_sizecg}|t|krt|n|}tdt||D]7}|||||z}||8|S)aCompute doc embeddings using a SageMaker Inference Endpoint. Args: texts: The list of texts to embed. chunk_size: The chunk size defines how many input texts will be grouped together as request. If None, will use the chunk size specified by the class. Returns: List of embeddings, one for each text. r)lenrangerIextend)rDr1rKresults _chunk_sizeirHs rembed_documentsz+SagemakerEndpointEmbeddings.embed_documentss$.U$;$;c%jjj q#e**k22 % %A++E!a+o2E,FGGH NN8 $ $ $ $rtextc:||gdS)zCompute query embeddings using a SageMaker inference endpoint. Args: text: The text to embed. Returns: Embeddings for the text. r)rI)rDrTs r embed_queryz'SagemakerEndpointEmbeddings.embed_querys##TF++A..r)rJ)rrrrrr__annotations__rstrrrrr rrrr model_configrr0rfloatrIintrSrVrrrrrs"<FCM3,KP.2hsm222.--- &$(L(4.'''1&*OXd^*** : $H2L"$"4"""X"HGT#YG4U 3DGGGG435#Y,/ d5k , / /U / / / / / /rrN)typingrrrrlangchain_core.embeddingsrlangchain_core.utilsrpydanticr r +langchain_community.llms.sagemaker_endpointr rXrZr rrrrras,,,,,,,,,,,,000000))))))********JJJJJJ)))))1$s)T$u+=N2NO)))D/D/D/D/D/)ZD/D/D/D/D/r