NgP|ddlmZmZmZmZmZddlZddlmZddl m Z m Z ddl m Z mZdZdZGdd e eZdS) )AnyDictListMappingOptionalN) Embeddings)get_from_dict_or_envpre_init) BaseModel ConfigDictz#sentence-transformers/clip-ViT-B-32iceZdZUdZeZeed< dZe ed< dZ eed< dZ eed< d Z e eed < d Ze eed < eZeed < ed dZededefdZedeeeffdZdeedeeefdZdeedeeefdZdedeefdZ d S)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_namespacesvaluesreturnc2t|dd}||d<|S)z?Validate that api key and python package exists in environment.rDEEPINFRA_API_TOKEN)r )clsrrs d/var/www/html/ai-engine/env/lib/python3.11/site-packages/langchain_community/embeddings/deepinfra.pyvalidate_environmentz(DeepInfraEmbeddings.validate_environment;s/3 )+@  )<$% cd|jiS)zGet the identifying parameters.r)r)selfs r_identifying_paramsz'DeepInfraEmbeddings._identifying_paramsDsDM**r!inputc|jpi}d|jdd} tjd|j|||jd|}n.#tjj$r}td|d}~wwxYw|j dkrtd |j d |j  | }|d }n6#tjj $r}td |d |j d}~wwxYw|S)Nzbearer zapplication/json) Authorizationz Content-Typez'https://api.deepinfra.com/v1/inference/)inputsr)headersjsonz$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._embedIsY)/R Bt'?AA.    I-I$-II %DNTTmTCC "3 I I IGAGGHH H I ?c ! !*???CHH.   A<JJ"2   L!LL#(LL   s/)AA-A((A-B88C+ C&&C+textscg}fd|DfdtdtjD}|D]}||z }|S)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. c&g|] }j|Sr)r).0r3r#s r z7DeepInfraEmbeddings.embed_documents..vs'QQQ4 6>>>QQQr!c4g|]}||jzSr)r)r=iinstruction_pairsr#s rr>z7DeepInfraEmbeddings.embed_documents..xs:    a!do"55 6   r!r)rangelenrr9)r#r:r,chunkschunkrAs` @rembed_documentsz#DeepInfraEmbeddings.embed_documentsis QQQQ5QQQ     1c"344doFF    - -E $++e,, ,JJr!r3cT|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_querys8#4r]s55555555555555000000????????********8@@@@@)Z@@@@@r!