U h@sfdZddlmZmZmZddlZddlmZddlm Z m Z ddl m Z m Z mZGddde eZdS) z#This file is for LLMRails Embedding)DictListOptionalN) Embeddings) BaseModel SecretStr)convert_to_secret_strget_from_dict_or_envpre_initc@seZdZUdZdZeed<dZee ed<GdddZ e e e dd d Z eeeeed d d ZeeedddZdS)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_keyc@seZdZdZdS)zLLMRailsEmbeddings.ConfigZforbidN)__name__ __module__ __qualname__extrarrL/tmp/pip-unpacked-wheel-9gdii04g/langchain_community/embeddings/llm_rails.pyConfig#sr)valuesreturncCstt|dd}||d<|S)z,Validate that api key exists in environment.r ZLLM_RAILS_API_KEY)rr )clsrr rrrvalidate_environment&s  z'LLMRailsEmbeddings.validate_environment)textsrcCs: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)requestspostr Zget_secret_valuer r)selfrresponserrrembed_documents/s   z"LLMRailsEmbeddings.embed_documents)textrcCs||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_query@s zLLMRailsEmbeddings.embed_query)rrr__doc__r str__annotations__r rrrr rrrfloatr'r)rrrrr s  r )r*typingrrrr#Zlangchain_core.embeddingsrZlangchain_core.pydantic_v1rrZlangchain_core.utilsrr r r rrrrs