Elasticsearch에서 호스팅되는 embedding model을 사용하여 embedding을 생성하는 방법에 대한 안내 ElasticsearchEmbeddings class를 인스턴스화하는 가장 쉬운 방법은
  • Elastic Cloud를 사용하는 경우 from_credentials constructor 사용
  • 또는 모든 Elasticsearch cluster와 함께 from_es_connection constructor 사용
!pip -q install langchain-elasticsearch

from langchain_elasticsearch import ElasticsearchEmbeddings

# Define the model ID
model_id = "your_model_id"

​
from_credentials로 테스트하기

이 방법은 Elastic Cloud cloud_id가 필요합니다 
# 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}")

​
기존 Elasticsearch client connection으로 테스트하기

이 방법은 모든 Elasticsearch deployment와 함께 사용할 수 있습니다 
# 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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