U h:@sjddlmZmZmZmZmZddlZddlmZddl m Z ddl m Z m Z dZdZGdd d e eZdS) )AnyDictListMappingOptionalN) Embeddings) BaseModel)get_from_dict_or_envpre_initz#sentence-transformers/clip-ViT-B-32ic@seZdZUdZeZeed<dZe ed<dZ eed<dZ eed<d Z e eed <d Ze eed <eZeed <Gd ddZeeedddZeeeefdddZeeeeedddZeeeeedddZeeedddZd 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_sizec@seZdZdZdS)zDeepInfraEmbeddings.ConfigZforbidN)__name__ __module__ __qualname__extrarrL/tmp/pip-unpacked-wheel-9gdii04g/langchain_community/embeddings/deepinfra.pyConfig9sr)valuesreturncCst|dd}||d<|S)z?Validate that api key and python package exists in environment.rZDEEPINFRA_API_TOKEN)r )clsrrrrrvalidate_environment<sz(DeepInfraEmbeddings.validate_environment)rcCs d|jiS)zGet the identifying parameters.r )r selfrrr_identifying_paramsEsz'DeepInfraEmbeddings._identifying_params)inputrc Cs|jpi}d|jdd}z(tjd|j|||jd|d}Wn4tjjk rx}ztd|W5d}~XYnX|j dkrtd |j |j fz| }|d }Wn<tjj k r}ztd |d |j W5d}~XYnX|S) Nzbearer zapplication/json) Authorizationz Content-Typez'https://api.deepinfra.com/v1/inference/)inputsr )headersjsonz$Error raised by inference endpoint: z/Error raised by inference API HTTP code: %s, %s embeddingszError raised by inference API: z . Response: ) rrrequestspostr r exceptionsRequestException ValueError status_codetextr%JSONDecodeError)rr!Z _model_kwargsr$resetr'rrr_embedJs4        zDeepInfraEmbeddings._embed)textsrcsTg}fdd|DfddtdtjD}|D]}||7}q<|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. csg|]}j|qSr)r).0r.rrr wsz7DeepInfraEmbeddings.embed_documents..csg|]}||jqSr)r)r5iZinstruction_pairsrrrr6ysr)rangelenrr3)rr4r'chunkschunkrr8rembed_documentsjs  z#DeepInfraEmbeddings.embed_documents)r.rcCs"|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)rr3)rr.Zinstruction_pairZ embeddingrrr embed_querys zDeepInfraEmbeddings.embed_query) rrr__doc__DEFAULT_MODEL_IDr str__annotations__r boolrrrrdictrMAX_BATCH_SIZErintrr rrpropertyrrr rfloatr3r=r>rrrrr s       r )typingrrrrrr(Zlangchain_core.embeddingsrZlangchain_core.pydantic_v1rZlangchain_core.utilsr r r@rEr rrrrs