o Zh@sjdZddlmZmZmZddlZddlmZddlm Z m Z m Z ddl m Z mZmZGddde eZdS) z#This file is for LLMRails Embedding)DictListOptionalN) Embeddings)convert_to_secret_strget_from_dict_or_envpre_init) BaseModel ConfigDict SecretStrc@seZdZUdZdZeed< dZee ed< e ddZ e de d e fd d Zd eed eeefd dZded eefddZdS)LLMRailsEmbeddingsaLLMRails embedding models. To use, you should have the environment variable ``LLM_RAILS_API_KEY`` set with your API key or pass it as a named parameter to the constructor. Model can be one of ["embedding-english-v1","embedding-multi-v1"] Example: .. code-block:: python from langchain_community.embeddings import LLMRailsEmbeddings cohere = LLMRailsEmbeddings( model="embedding-english-v1", api_key="my-api-key" ) zembedding-english-v1modelNapi_keyZforbid)extravaluesreturncCstt|dd}||d<|S)z,Validate that api key exists in environment.rZLLM_RAILS_API_KEY)rr)clsrrr_/var/www/html/lang_env/lib/python3.10/site-packages/langchain_community/embeddings/llm_rails.pyvalidate_environment's  z'LLMRailsEmbeddings.validate_environmenttextscCs:tjdd|ji||jddd}dd|dDS) zCall out to Cohere's embedding endpoint. Args: texts: The list of texts to embed. Returns: List of embeddings, one for each text. z&https://api.llmrails.com/v1/embeddingsz X-API-KEY)inputr <)headersjsontimeoutcSsg|]}|dqS)Z embeddingr).0itemrrr ?sz6LLMRailsEmbeddings.embed_documents..data)requestspostrZget_secret_valuer r)selfrresponserrrembed_documents0s   z"LLMRailsEmbeddings.embed_documentstextcCs||gdS)zCall out to Cohere's embedding endpoint. Args: text: The text to embed. Returns: Embeddings for the text. r)r$)r"r%rrr embed_queryAs zLLMRailsEmbeddings.embed_query)__name__ __module__ __qualname____doc__r str__annotations__rrr r Z model_configrrrrfloatr$r&rrrrr s  r )r*typingrrrr Zlangchain_core.embeddingsrZlangchain_core.utilsrrrZpydanticr r r r rrrrs