tjxddlmZmZmZmZmZmZddlZddlm Z ddl m Z ddl m Z mZmZGdde e ZdS))AnyDictListMappingOptionalTupleN) Embeddingsget_from_dict_or_env) BaseModel ConfigDictmodel_validatorc eZdZUdZdZeed< dZeed< dZeed< dZ e ed < d Z e eed <e d ZedededefdZedeeeffdZ ddeeeefdedeee fdZdeedeee fdZdedee fdZd S)MosaicMLInstructorEmbeddingsaaMosaicML embedding service. To use, you should have the environment variable ``MOSAICML_API_TOKEN`` set with your API token, or pass it as a named parameter to the constructor. Example: .. code-block:: python from langchain_community.llms import MosaicMLInstructorEmbeddings endpoint_url = ( "https://models.hosted-on.mosaicml.hosting/instructor-large/v1/predict" ) mosaic_llm = MosaicMLInstructorEmbeddings( endpoint_url=endpoint_url, mosaicml_api_token="my-api-key" ) zBhttps://models.hosted-on.mosaicml.hosting/instructor-xl/v1/predict endpoint_urlz&Represent the document for retrieval: embed_instructionz  (:#$ cd|jiS)zGet the identifying parameters.r)r)selfs r_identifying_paramsz0MosaicMLInstructorEmbeddings._identifying_params:s 122r!Finputis_retrycd|i}|jdd} tj|j||}n.#tjj$r}t d|d}~wwxYw |jdkrN|s5ddl}| |j | |d St d |j | }t|tr`gd } | D]} | |vr || } nt d |t| t rt| dt r| } n| g} nt d |n6#tjj$r}t d|d|j d}~wwxYw| S)Ninputszapplication/json) Authorizationz Content-Type)headersjsonz$Error raised by inference endpoint: irT)r&z>Error raised by inference API: rate limit exceeded. Response: )dataoutputoutputsz#No key data or output in response: zUnexpected response type: zError raised by inference API: z . Response: )rrequestspostr exceptionsRequestException ValueError status_codetimesleepr_embedtextr+ isinstancedictlistJSONDecodeError) r#r%r&payloadr*responseer5parsed_response output_keyskey output_item embeddingss rr7z#MosaicMLInstructorEmbeddings._embed?s U#!% 79.    I}T%6gVVVHH"3 I I IGAGGHH H I' #s**=KKKJJt/000;;ut;<<< '}'' 'mmooO/400 Q;;; &Co--&5c&: .%OoOOk400/Z APT5U5U/!,JJ"-JJ !Oo!O!OPPP"2   Q!QQ(-QQ   s5.AAAAEB2EF&FFtextscNfd|D}|}|S)zEmbed documents using a MosaicML deployed instructor embedding model. Args: texts: The list of texts to embed. Returns: List of embeddings, one for each text. c"g|] }j|f S)r).0r8r#s r z@MosaicMLInstructorEmbeddings.embed_documents..s!NNNd4d;NNNr!)r7)r#rEinstruction_pairsrDs` rembed_documentsz,MosaicMLInstructorEmbeddings.embed_documents{s8ONNNNNN[[!233 r!r8cP|j|f}||gd}|S)zEmbed a query using a MosaicML deployed instructor embedding model. Args: text: The text to embed. Returns: Embeddings for the text. r)rr7)r#r8instruction_pair embeddings r embed_queryz(MosaicMLInstructorEmbeddings.embed_querys2!2D9KK!1 233A6 r!)F)__name__ __module__ __qualname____doc__rstr__annotations__rrrfloatrrr model_configr classmethodrrr propertyrr$rrboolr7rLrPrHr!rrr s( M#EsEEE.Fs/KE(, ,,,:L_(###$3[$#3WS#X%6333X3 >C::%S/*:6:: d5k ::::x T#Y 4U 3D      U      r!r)typingrrrrrrr/langchain_core.embeddingsr langchain_core.utilsr pydanticr r rrrHr!rr`s<<<<<<<<<<<<<<<<000000555555;;;;;;;;;;JJJJJ9jJJJJJr!