langchain.embeddings.elasticsearch.ElasticsearchEmbeddings

class langchain.embeddings.elasticsearch.ElasticsearchEmbeddings(client: MlClient, model_id: str, *, input_field: str = 'text_field')[source]

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

Initialize the ElasticsearchEmbeddings instance.

Parameters
  • 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’.

Methods

__init__(client, model_id, *[, input_field])

Initialize the ElasticsearchEmbeddings instance.

aembed_documents(texts)

Asynchronous Embed search docs.

aembed_query(text)

Asynchronous Embed query text.

embed_documents(texts)

Generate embeddings for a list of documents.

embed_query(text)

Generate an embedding for a single query text.

from_credentials(model_id, *[, es_cloud_id, ...])

Instantiate embeddings from Elasticsearch credentials.

from_es_connection(model_id, es_connection)

Instantiate embeddings from an existing Elasticsearch connection.

__init__(client: MlClient, model_id: str, *, input_field: str = 'text_field')[source]

Initialize the ElasticsearchEmbeddings instance.

Parameters
  • 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’.

async aembed_documents(texts: List[str]) List[List[float]]

Asynchronous Embed search docs.

async aembed_query(text: str) List[float]

Asynchronous Embed query text.

embed_documents(texts: List[str]) List[List[float]][source]

Generate embeddings for a list of documents.

Parameters

texts (List[str]) – A list of document text strings to generate embeddings for.

Returns

A list of embeddings, one for each document in the input

list.

Return type

List[List[float]]

embed_query(text: str) List[float][source]

Generate an embedding for a single query text.

Parameters

text (str) – The query text to generate an embedding for.

Returns

The embedding for the input query text.

Return type

List[float]

classmethod from_credentials(model_id: str, *, es_cloud_id: Optional[str] = None, es_user: Optional[str] = None, es_password: Optional[str] = None, input_field: str = 'text_field') ElasticsearchEmbeddings[source]

Instantiate embeddings from Elasticsearch credentials.

Parameters
  • 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

from langchain.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)
classmethod from_es_connection(model_id: str, es_connection: Elasticsearch, input_field: str = 'text_field') ElasticsearchEmbeddings[source]

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

from elasticsearch import Elasticsearch

from langchain.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)

Examples using ElasticsearchEmbeddings