> ## Documentation Index
> Fetch the complete documentation index at: https://cndoc-langchain.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Elasticsearch 集成

> 使用 LangChain Python 与 Elasticsearch 嵌入模型集成。

如何使用 Elasticsearch 中托管的嵌入模型生成嵌入向量的演练

实例化 `ElasticsearchEmbeddings` 类最简单的方法是

* 如果您使用 Elastic Cloud，则使用 `from_credentials` 构造函数
* 或者使用 `from_es_connection` 构造函数连接任何 Elasticsearch 集群

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
!pip -q install langchain-elasticsearch
```

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_elasticsearch import ElasticsearchEmbeddings
```

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# 定义模型 ID
model_id = "your_model_id"
```

## 使用 `from_credentials` 进行测试

这需要一个 Elastic Cloud `cloud_id`

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# 使用凭据实例化 ElasticsearchEmbeddings
embeddings = ElasticsearchEmbeddings.from_credentials(
    model_id,
    es_cloud_id="your_cloud_id",
    es_user="your_user",
    es_password="your_password",
)
```

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# 为多个文档创建嵌入向量
documents = [
    "This is an example document.",
    "Another example document to generate embeddings for.",
]
document_embeddings = embeddings.embed_documents(documents)
```

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# 打印文档嵌入向量
for i, embedding in enumerate(document_embeddings):
    print(f"Embedding for document {i + 1}: {embedding}")
```

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# 为单个查询创建嵌入向量
query = "This is a single query."
query_embedding = embeddings.embed_query(query)
```

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# 打印查询嵌入向量
print(f"Embedding for query: {query_embedding}")
```

## 使用现有的 Elasticsearch 客户端连接进行测试

这可以用于任何 Elasticsearch 部署

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# 创建 Elasticsearch 连接
from elasticsearch import Elasticsearch

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

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# 使用 es_connection 实例化 ElasticsearchEmbeddings
embeddings = ElasticsearchEmbeddings.from_es_connection(
    model_id,
    es_connection,
)
```

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# 为多个文档创建嵌入向量
documents = [
    "This is an example document.",
    "Another example document to generate embeddings for.",
]
document_embeddings = embeddings.embed_documents(documents)
```

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# 打印文档嵌入向量
for i, embedding in enumerate(document_embeddings):
    print(f"Embedding for document {i + 1}: {embedding}")
```

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# 为单个查询创建嵌入向量
query = "This is a single query."
query_embedding = embeddings.embed_query(query)
```

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# 打印查询嵌入向量
print(f"Embedding for query: {query_embedding}")
```

***

<div className="source-links">
  <Callout icon="terminal-2">
    [将这些文档连接](/use-these-docs)到 Claude、VSCode 等，通过 MCP 获取实时答案。
  </Callout>

  <Callout icon="edit">
    [在 GitHub 上编辑此页面](https://github.com/langchain-ai/docs/edit/main/src/oss/python/integrations/embeddings/elasticsearch.mdx) 或 [提交问题](https://github.com/langchain-ai/docs/issues/new/choose)。
  </Callout>
</div>