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Elasticsearch

逐步解說如何使用 Elasticsearch 中託管的嵌入模型產生嵌入

要實例化 ElasticsearchEmbeddings 類別，最簡單的方法是

  • 如果您使用 Elastic Cloud，則使用 from_credentials 建構子
  • 或使用任何 Elasticsearch 叢集的 from_es_connection 建構子
!pip -q install langchain-elasticsearch
from langchain_elasticsearch import ElasticsearchEmbeddings
# Define the model ID
model_id = "your_model_id"

使用 from_credentials 測試​

這需要一個 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}")

使用現有的 Elasticsearch 客戶端連線進行測試​

這可以用於任何 Elasticsearch 部署

# 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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