!pip-q install langchain-elasticsearch

from langchain_elasticsearchimport ElasticsearchEmbeddings

# Define the model ID
model_id="your_model_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, embeddinginenumerate(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}")

# Create Elasticsearch connection
from elasticsearchimport 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, embeddinginenumerate(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}")