Source code for langchain_elasticsearch.embeddings

from __future__ import annotations

from typing import TYPE_CHECKING, List, Optional

from elasticsearch import Elasticsearch
from elasticsearch.helpers.vectorstore import EmbeddingService
from langchain_core.embeddings import Embeddings
from langchain_core.utils import get_from_env

if TYPE_CHECKING:
    from elasticsearch.client import MlClient


[docs]class ElasticsearchEmbeddings(Embeddings): """Elasticsearch 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 """ # noqa: E501
[docs] def __init__( self, client: MlClient, model_id: str, *, input_field: str = "text_field", ): """ 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'. """ self.client = client self.model_id = model_id self.input_field = input_field
[docs] @classmethod def from_credentials( cls, model_id: str, *, es_cloud_id: Optional[str] = None, es_api_key: Optional[str] = None, input_field: str = "text_field", ) -> ElasticsearchEmbeddings: """Instantiate 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_elasticserach.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) """ from elasticsearch.client import MlClient es_cloud_id = es_cloud_id or get_from_env("es_cloud_id", "ES_CLOUD_ID") es_api_key = es_api_key or get_from_env("es_api_key", "ES_API_KEY") # Connect to Elasticsearch es_connection = Elasticsearch(cloud_id=es_cloud_id, api_key=es_api_key) client = MlClient(es_connection) return cls(client, model_id, input_field=input_field)
[docs] @classmethod def from_es_connection( cls, model_id: str, es_connection: Elasticsearch, input_field: str = "text_field", ) -> ElasticsearchEmbeddings: """ 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_elasticsearch.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) """ from elasticsearch.client import MlClient # Create an MlClient from the given Elasticsearch connection client = MlClient(es_connection) # Return a new instance of the ElasticsearchEmbeddings class with # the MlClient, model_id, and input_field return cls(client, model_id, input_field=input_field)
def _embedding_func(self, texts: List[str]) -> List[List[float]]: """ 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. """ response = self.client.infer_trained_model( model_id=self.model_id, docs=[{self.input_field: text} for text in texts] ) embeddings = [doc["predicted_value"] for doc in response["inference_results"]] return embeddings
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]: """ 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. """ return self._embedding_func(texts)
[docs] def embed_query(self, text: str) -> List[float]: """ 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. """ return self._embedding_func([text])[0]
[docs]class EmbeddingServiceAdapter(EmbeddingService): """ Adapter for LangChain Embeddings to support the EmbeddingService interface from elasticsearch.helpers.vectorstore. """
[docs] def __init__(self, langchain_embeddings: Embeddings): self._langchain_embeddings = langchain_embeddings
def __eq__(self, other): # type: ignore[no-untyped-def] if isinstance(other, self.__class__): return self.__dict__ == other.__dict__ else: return False
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]: """ 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. """ return self._langchain_embeddings.embed_documents(texts)
[docs] def embed_query(self, text: str) -> List[float]: """ 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. """ return self._langchain_embeddings.embed_query(text)