dhT!SSKJr SSKJrJrJr SSKJr SSKJ r \(a SSK J r SSK J r SSKJr \"SS S S 9"S S \55rg)) annotations) TYPE_CHECKINGListOptional) deprecated) get_from_env ElasticsearchMlClient) Embeddingsz0.1.11z,Use class in langchain-elasticsearch packageT) alternativependingc\rSrSrSrSS.SSjjr\SSSSS.SSjj5r\SSS jj5rSS jr SS jr SS jr S r g)ElasticsearchEmbeddingsaElasticsearch embedding models. This class provides an interface to generate embeddings using a model deployed in an Elasticsearch cluster. It requires an Elasticsearch connection object and the model_id of the model deployed in the cluster. In Elasticsearch you need to have an embedding model loaded and deployed. - https://www.elastic.co/guide/en/elasticsearch/reference/current/infer-trained-model.html - https://www.elastic.co/guide/en/machine-learning/current/ml-nlp-deploy-models.html text_field input_fieldc(XlX lX0lg)aG Initialize the ElasticsearchEmbeddings instance. Args: client (MlClient): An Elasticsearch ML client object. model_id (str): The model_id of the model deployed in the Elasticsearch cluster. input_field (str): The name of the key for the input text field in the document. Defaults to 'text_field'. N)clientmodel_idr)selfrrrs d/var/www/html/shao/venv/lib/python3.13/site-packages/langchain_community/embeddings/elasticsearch.py__init__ ElasticsearchEmbeddings.__init__s"  &N) es_cloud_ides_user es_passwordrcSSKJn SSKJn U=(d [ SS5nU=(d [ SS5nU=(d [ S S 5nU"X#U4S 9nU"U5n U"XUS 9$![a [ S5ef=f) aInstantiate embeddings from Elasticsearch credentials. Args: model_id (str): The model_id of the model deployed in the Elasticsearch cluster. input_field (str): The name of the key for the input text field in the document. Defaults to 'text_field'. es_cloud_id: (str, optional): The Elasticsearch cloud ID to connect to. es_user: (str, optional): Elasticsearch username. es_password: (str, optional): Elasticsearch password. Example: .. code-block:: python from langchain_community.embeddings import ElasticsearchEmbeddings # Define the model ID and input field name (if different from default) model_id = "your_model_id" # Optional, only if different from 'text_field' input_field = "your_input_field" # Credentials can be passed in two ways. Either set the env vars # ES_CLOUD_ID, ES_USER, ES_PASSWORD and they will be automatically # pulled in, or pass them in directly as kwargs. embeddings = ElasticsearchEmbeddings.from_credentials( model_id, input_field=input_field, # es_cloud_id="foo", # es_user="bar", # es_password="baz", ) documents = [ "This is an example document.", "Another example document to generate embeddings for.", ] embeddings_generator.embed_documents(documents) rr r zPelasticsearch package not found, please install with 'pip install elasticsearch'r ES_CLOUD_IDrES_USERr ES_PASSWORD)cloud_id basic_authr) elasticsearchr elasticsearch.clientr ImportErrorr) clsrrrr rr r es_connectionrs rfrom_credentials(ElasticsearchEmbeddings.from_credentials3s`  3 5"O\-%O ?\)Y?!O\-%O & {-C -(6== !  s A$$A:c,SSKJn U"U5nU"XQUS9$)a Instantiate embeddings from an existing Elasticsearch connection. This method provides a way to create an instance of the ElasticsearchEmbeddings class using an existing Elasticsearch connection. The connection object is used to create an MlClient, which is then used to initialize the ElasticsearchEmbeddings instance. Args: model_id (str): The model_id of the model deployed in the Elasticsearch cluster. es_connection (elasticsearch.Elasticsearch): An existing Elasticsearch connection object. input_field (str, optional): The name of the key for the input text field in the document. Defaults to 'text_field'. Returns: ElasticsearchEmbeddings: An instance of the ElasticsearchEmbeddings class. Example: .. code-block:: python from elasticsearch import Elasticsearch from langchain_community.embeddings import ElasticsearchEmbeddings # Define the model ID and input field name (if different from default) model_id = "your_model_id" # Optional, only if different from 'text_field' input_field = "your_input_field" # Create Elasticsearch connection es_connection = Elasticsearch( hosts=["localhost:9200"], http_auth=("user", "password") ) # Instantiate ElasticsearchEmbeddings using the existing connection embeddings = ElasticsearchEmbeddings.from_es_connection( model_id, es_connection, input_field=input_field, ) documents = [ "This is an example document.", "Another example document to generate embeddings for.", ] embeddings_generator.embed_documents(documents) rr r)r(r )r*rr+rr rs rfrom_es_connection*ElasticsearchEmbeddings.from_es_connectionws#p 2-(6==rcURRURUVs/sHo RU0PM snS9nUSVs/sHoDSPM nnU$s snfs snf)z Generate embeddings for the given texts using the Elasticsearch model. Args: texts (List[str]): A list of text strings to generate embeddings for. Returns: List[List[float]]: A list of embeddings, one for each text in the input list. )rdocsinference_resultspredicted_value)rinfer_trained_modelrr)rtextstextresponsedoc embeddingss r_embedding_func'ElasticsearchEmbeddings._embedding_funcss;;22]]u)Uut+;+;T*Bu)U3 9AAT8UV8U+,8U V *VWs A  Ac$URU5$)z Generate embeddings for a list of documents. Args: texts (List[str]): A list of document text strings to generate embeddings for. Returns: List[List[float]]: A list of embeddings, one for each document in the input list. r;)rr6s rembed_documents'ElasticsearchEmbeddings.embed_documentss##E**rc,URU/5S$)z Generate an embedding for a single query text. Args: text (str): The query text to generate an embedding for. Returns: List[float]: The embedding for the input query text. rr>)rr7s r embed_query#ElasticsearchEmbeddings.embed_querys##TF+A..r)rrr)rr rstrrrD) rrDr Optional[str]rrEr rErrDreturnr)r)rrDr+r rrDrFr)r6z List[str]rFzList[List[float]])r7rDrFz List[float]) __name__ __module__ __qualname____firstlineno____doc__r classmethodr,r/r;r?rB__static_attributes__rrrrs  ( '''  '* &*!%%)'A>A># A>  A> # A>A> !A>A>F ( >>>>%>> >> ! >>>>@$ + /rrN) __future__rtypingrrrlangchain_core._apirlangchain_core.utilsrr'r r(r langchain_core.embeddingsr rrNrrrTsL"00*-+-0  HRVP/jP/P/r