j5`ddlmZmZmZmZddlmZddlmZddl m Z m Z m Z Gdde eZ y))AnyDictListOptional) Embeddings)get_from_dict_or_env) BaseModelFieldmodel_validatorceZdZUdZeddZeed<edZe ed< e ed < dZ e e ed < e d ed edefdZde deefdZdee deeefdZy)ZhipuAIEmbeddingsuZhipuAI embedding model integration. Setup: To use, you should have the ``zhipuai`` python package installed, and the environment variable ``ZHIPU_API_KEY`` set with your API KEY. More instructions about ZhipuAi Embeddings, you can get it from https://open.bigmodel.cn/dev/api#vector .. code-block:: bash pip install -U zhipuai export ZHIPU_API_KEY="your-api-key" Key init args — completion params: model: Optional[str] Name of ZhipuAI model to use. api_key: str Automatically inferred from env var `ZHIPU_API_KEY` if not provided. See full list of supported init args and their descriptions in the params section. Instantiate: .. code-block:: python from langchain_community.embeddings import ZhipuAIEmbeddings embed = ZhipuAIEmbeddings( model="embedding-2", # api_key="...", ) Embed single text: .. code-block:: python input_text = "The meaning of life is 42" embed.embed_query(input_text) .. code-block:: python [-0.003832892, 0.049372625, -0.035413884, -0.019301128, 0.0068899863, 0.01248398, -0.022153955, 0.006623926, 0.00778216, 0.009558191, ...] Embed multiple text: .. code-block:: python input_texts = ["This is a test query1.", "This is a test query2."] embed.embed_documents(input_texts) .. code-block:: python [ [0.0083934665, 0.037985895, -0.06684559, -0.039616987, 0.015481004, -0.023952313, ...], [-0.02713102, -0.005470169, 0.032321047, 0.042484466, 0.023290444, 0.02170547, ...] ] NT)defaultexcludeclientz embedding-2)rmodelapi_key dimensionsbefore)modevaluesreturnct|dd|d< ddlm}||d|d<|S#t$r tdwxYw)z/Validate that auth token exists in environment.rZHIPUAI_API_KEYr)ZhipuAI)rrzUCould not import zhipuai python package.Please install it with `pip install zhipuai`.)rzhipuair ImportError)clsrrs p/var/www/html/Fitness-lenito-AI-main/venv/lib/python3.12/site-packages/langchain_community/embeddings/zhipuai.pyvalidate_environmentz&ZhipuAIEmbeddings.validate_environmentOs_1DUVy  '&vi/@AF8    @  s)>textc0|j|g}|dS)z Embeds a text using the AutoVOT algorithm. Args: text: A text to embed. Returns: Input document's embedded list. r)embed_documents)selfr resps r embed_queryzZhipuAIEmbeddings.embed_query_s##TF+AwtextscJ|j=|jjj|j||j}n1|jjj|j|}|j Dcgc]}|j }}|Scc}w)a0 Embeds a list of text documents using the AutoVOT algorithm. Args: texts: A list of text documents to embed. Returns: A list of embeddings for each document in the input list. Each embedding is represented as a list of float values. )rinputr)rr))rr embeddingscreaterdata embedding)r#r'r$rr*s rr"z!ZhipuAIEmbeddings.embed_documentsls ?? &;;))00jj??1D ;;))00tzz0OD+/9959aakk9 56s B )__name__ __module__ __qualname____doc__r rr__annotations__rstrrrintr classmethodrrrfloatr%r"r&rr r s9vd3FC3}-E3- LN $J $ (# $ 3 $   U T#Y4U 3Dr&r N)typingrrrrlangchain_core.embeddingsrlangchain_core.utilsrpydanticr r r r r8r&rr=s',,0566x :xr&