Elasticsearch integration

Integrate with the Elasticsearch embedding model using LangChain Python.

Walkthrough of how to generate embeddings using a hosted embedding model in Elasticsearch

The easiest way to instantiate the ElasticsearchEmbeddings class it either

• using the from_credentials constructor if you are using Elastic Cloud
• or using the from_es_connection constructor with any Elasticsearch cluster

!pip -q install langchain-elasticsearch

from langchain_elasticsearch import ElasticsearchEmbeddings

# Define the model ID
model_id = "your_model_id"

Testing with from_credentials

This required an Elastic Cloud cloud_id

# Instantiate ElasticsearchEmbeddings using credentials
embeddings = ElasticsearchEmbeddings.from_credentials(
    model_id,
    es_cloud_id="your_cloud_id",
    es_user="your_user",
    es_password="your_password",
)

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

# Print document embeddings
for i, embedding in enumerate(document_embeddings):
    print(f"Embedding for document {i + 1}: {embedding}")

# Create an embedding for a single query
query = "This is a single query."
query_embedding = embeddings.embed_query(query)

# Print query embedding
print(f"Embedding for query: {query_embedding}")

Testing with existing Elasticsearch client connection

This can be used with any Elasticsearch deployment

# Create Elasticsearch connection
from elasticsearch import Elasticsearch

es_connection = Elasticsearch(
    hosts=["https://es_cluster_url:port"], basic_auth=("user", "password")
)

# Instantiate ElasticsearchEmbeddings using es_connection
embeddings = ElasticsearchEmbeddings.from_es_connection(
    model_id,
    es_connection,
)

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

# Print document embeddings
for i, embedding in enumerate(document_embeddings):
    print(f"Embedding for document {i + 1}: {embedding}")

# Create an embedding for a single query
query = "This is a single query."
query_embedding = embeddings.embed_query(query)

# Print query embedding
print(f"Embedding for query: {query_embedding}")

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