7|hPtddlmZmZmZmZmZddlZddlmZddl m Z m Z ddl m Z mZdZdZGdd e eZy) )AnyDictListMappingOptionalN) Embeddings)get_from_dict_or_envpre_init) BaseModel ConfigDictz#sentence-transformers/clip-ViT-B-32ic6eZdZUdZeZeed< dZe ed< dZ eed< dZ eed< d Z e eed < d Ze eed < eZeed < ed dZededefdZedeeeffdZdeedeeefdZdeedeeefdZdedeefdZ y )DeepInfraEmbeddingsaDeep Infra's embedding inference service. To use, you should have the environment variable ``DEEPINFRA_API_TOKEN`` set with your API token, or pass it as a named parameter to the constructor. There are multiple embeddings models available, see https://deepinfra.com/models?type=embeddings. Example: .. code-block:: python from langchain_community.embeddings import DeepInfraEmbeddings deepinfra_emb = DeepInfraEmbeddings( model_id="sentence-transformers/clip-ViT-B-32", deepinfra_api_token="my-api-key" ) r1 = deepinfra_emb.embed_documents( [ "Alpha is the first letter of Greek alphabet", "Beta is the second letter of Greek alphabet", ] ) r2 = deepinfra_emb.embed_query( "What is the second letter of Greek alphabet" ) model_idF normalizez passage: embed_instructionzquery: query_instructionN model_kwargsdeepinfra_api_token batch_sizeforbid)extraprotected_namespacesvaluesreturnc*t|dd}||d<|S)z?Validate that api key and python package exists in environment.rDEEPINFRA_API_TOKEN)r )clsrrs g/var/www/html/test/engine/venv/lib/python3.12/site-packages/langchain_community/embeddings/deepinfra.pyvalidate_environmentz(DeepInfraEmbeddings.validate_environment;s*3 )+@ )<$% cd|jiS)zGet the identifying parameters.r)r)selfs r_identifying_paramsz'DeepInfraEmbeddings._identifying_paramsDsDMM**r!inputc.|jxsi}d|jdd} tjd|j|||j d|}|jdk7r%td |jd |j |j}|d }|S#tj j$r}td|d}~wwxYw#tj j$r }td |d |jd}~wwxYw)Nzbearer zapplication/json) Authorizationz Content-Typez'https://api.deepinfra.com/v1/inference/)inputsr)headersjsonz$Error raised by inference endpoint: z)Error raised by inference API HTTP code: z, embeddingszError raised by inference API: z . Response: ) rrrequestspostrr exceptionsRequestException ValueError status_codetextr*JSONDecodeError)r#r% _model_kwargsr)resetr,s r_embedzDeepInfraEmbeddings._embedIs$))/R  't'?'?&@A.   I--9$--I %DNNTmTC ??c !??CHH.   A<J !""33 ICA3GH H I""22 1!M#((L  s/4B$ C$CCCD4DDtextscg}|Dcgc]}|j|}}tdt||jDcgc]}||||jz}}|D]}||j |z }|Scc}wcc}w)aQEmbed documents using a Deep Infra deployed embedding model. For larger batches, the input list of texts is chunked into smaller batches to avoid exceeding the maximum request size. Args: texts: The list of texts to embed. Returns: List of embeddings, one for each text. r)rrangelenrr9)r#r:r,r3instruction_pairsichunkschunks rembed_documentsz#DeepInfraEmbeddings.embed_documentsis KPQ4 6 67v>QQ1c"34dooF  a!doo"5 6   -E $++e, ,J -R s A:A?r3cP|j|}|j|gd}|S)zEmbed a query using a Deep Infra deployed embedding model. Args: text: The text to embed. Returns: Embeddings for the text. r)rr9)r#r3instruction_pair embeddings r embed_queryzDeepInfraEmbeddings.embed_querys7#445dV<KK!1 23A6 r!)!__name__ __module__ __qualname____doc__DEFAULT_MODEL_IDrstr__annotations__rboolrrrrdictrMAX_BATCH_SIZErintr model_configr rr propertyrrr$rfloatr9rBrFrr!rrr s8%Hc$"It6(s(.&s&.#'L(4.'")-#-D$J$,H2FL $4+WS#X%6++DI$tE{*;@T#Y4U 3D0  U r!r)typingrrrrrr-langchain_core.embeddingsrlangchain_core.utilsr r pydanticr r rKrPrrr!rrYs2550?*8@)Z@r!