AsyncElasticsearchEmbeddings

Setup:

Install langchain_elasticsearch and start Elasticsearch locally using the start-local script.

pip install -qU langchain_elasticsearch
curl -fsSL https://elastic.co/start-local | sh

This will create an elastic-start-local folder. To start Elasticsearch and Kibana:

cd elastic-start-local
./start.sh

Elasticsearch will be available at http://localhost:9200. The password for the elastic user and API key are stored in the .env file in the elastic-start-local folder.

Initialize the ElasticsearchEmbeddings instance.

Instantiate:

from langchain_elasticsearch import ElasticsearchEmbeddings

embeddings = ElasticsearchEmbeddings(
    model_id="your_model_id",
    es_url="http://localhost:9200"
)

Instantiate with API key (URL):

from langchain_elasticsearch import ElasticsearchEmbeddings

embeddings = ElasticsearchEmbeddings(
    model_id="your_model_id",
    es_url="http://localhost:9200",
    es_api_key="your-api-key"
)

Instantiate with username/password (URL):

from langchain_elasticsearch import ElasticsearchEmbeddings

embeddings = ElasticsearchEmbeddings(
    model_id="your_model_id",
    es_url="http://localhost:9200",
    es_user="elastic",
    es_password="password"
)

If you want to use a cloud hosted Elasticsearch instance, you can pass in the es_cloud_id argument instead of the es_url argument.

Instantiate from cloud (with username/password):

from langchain_elasticsearch import ElasticsearchEmbeddings

embeddings = ElasticsearchEmbeddings(
    model_id="your_model_id",
    es_cloud_id="<cloud_id>",
    es_user="elastic",
    es_password="<password>"
)

Instantiate from cloud (with API key):

from langchain_elasticsearch import ElasticsearchEmbeddings

embeddings = ElasticsearchEmbeddings(
    model_id="your_model_id",
    es_cloud_id="<cloud_id>",
    es_api_key="your-api-key"
)

You can also connect to an existing Elasticsearch instance by passing in a pre-existing Elasticsearch connection via the client argument.

Instantiate from existing connection:

from langchain_elasticsearch import ElasticsearchEmbeddings
from elasticsearch import Elasticsearch

client = Elasticsearch("http://localhost:9200")
embeddings = ElasticsearchEmbeddings(
    model_id="your_model_id",
    client=client
)

Generate embeddings:

documents = [
    "This is an example document.",
    "Another example document to generate embeddings for.",
]
embeddings_list = embeddings.embed_documents(documents)

Generate query embedding:

query_embedding = embeddings.embed_query("What is this about?")

For synchronous applications, use the ElasticsearchEmbeddings class. For asynchronous applications, use the AsyncElasticsearchEmbeddings class.