a bg5@sZddlmZmZmZmZddlmZddlmZddl m Z m Z m Z Gddde eZ dS))AnyDictListOptional) Embeddings)get_from_dict_or_env) BaseModelFieldmodel_validatorc@seZdZUdZedddZeed<eddZe ed<e ed <dZ e e ed <e d d eeed ddZe eedddZee eeedddZdS)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)r modelapi_key dimensionsbefore)mode)valuesreturncCsRt|dd|d<z"ddlm}||dd|d<WntyLtdYn0|S)z/Validate that auth token exists in environment.rZZHIPUAI_API_KEYr)ZhipuAI)rrzUCould not import zhipuai python package.Please install it with `pip install zhipuai`.)rZzhipuair ImportError)clsrrrt/var/www/html/cobodadashboardai.evdpl.com/venv/lib/python3.9/site-packages/langchain_community/embeddings/zhipuai.pyvalidate_environmentOs   z&ZhipuAIEmbeddings.validate_environment)textrcCs||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)selfrresprrr embed_query_s zZhipuAIEmbeddings.embed_query)textsrcCsL|jdur$|jjj|j||jd}n|jjj|j|d}dd|jD}|S)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. N)rinputr)rr"cSsg|] }|jqSr)Z embedding).0rrrr z5ZhipuAIEmbeddings.embed_documents..)rr embeddingscreaterdata)rr!rr'rrrrls z!ZhipuAIEmbeddings.embed_documents)__name__ __module__ __qualname____doc__r rr__annotations__rstrrrintr classmethodrrrfloatr rrrrrr s ; r N)typingrrrrZlangchain_core.embeddingsrZlangchain_core.utilsrZpydanticrr r r rrrrs