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

# Nebius 集成

> 使用 LangChain Python 与 Nebius 集成。

所有与 Nebius AI Studio 相关的功能

> [Nebius AI Studio](https://studio.nebius.ai/) 为各种用例提供对广泛的最先进大型语言模型和嵌入模型的 API 访问权限。

## 安装与设置

Nebius 集成可以通过 pip 安装：

<CodeGroup>
  ```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  pip install langchain-nebius
  ```

  ```bash uv theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  uv add langchain-nebius
  ```
</CodeGroup>

要使用 Nebius AI Studio，您需要一个 API 密钥，您可以从 [Nebius AI Studio](https://studio.nebius.ai/) 获取。API 密钥可以作为初始化参数 `api_key` 传递，或设置为环境变量 `NEBIUS_API_KEY`。

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import os
os.environ["NEBIUS_API_KEY"] = "YOUR-NEBIUS-API-KEY"
```

### 可用模型

支持的完整模型列表可在 [Nebius AI Studio 文档](https://studio.nebius.com/) 中找到。

## 聊天模型

### ChatNebius

`ChatNebius` 类允许您与 Nebius AI Studio 的聊天模型进行交互。

查看 [使用示例](/oss/python/integrations/chat/nebius)。

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

# Initialize the chat model
chat = ChatNebius(
    model="Qwen/Qwen3-30B-A3B-fast",  # Choose from available models
    temperature=0.6,
    top_p=0.95
)
```

## 嵌入模型

### NebiusEmbeddings

`NebiusEmbeddings` 类允许您使用 Nebius AI Studio 的嵌入模型生成向量嵌入。

查看 [使用示例](/oss/python/integrations/embeddings/nebius)。

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

# Initialize embeddings
embeddings = NebiusEmbeddings(
    model="BAAI/bge-en-icl"  # Default embedding model
)
```

## 检索器

### NebiusRetriever

`NebiusRetriever` 利用来自 Nebius AI Studio 的嵌入实现高效的相似性搜索。它利用高质量的嵌入模型来实现文档的语义搜索。

查看 [使用示例](/oss/python/integrations/retrievers/nebius)。

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_core.documents import Document
from langchain_nebius import NebiusEmbeddings, NebiusRetriever

# Create sample documents
docs = [
    Document(page_content="Paris is the capital of France"),
    Document(page_content="Berlin is the capital of Germany"),
]

# Initialize embeddings
embeddings = NebiusEmbeddings()

# Create retriever
retriever = NebiusRetriever(
    embeddings=embeddings,
    docs=docs,
    k=2  # Number of documents to return
)
```

## 工具

### NebiusRetrievalTool

`NebiusRetrievalTool` 允许您基于 NebiusRetriever 为代理创建工具。

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_nebius import NebiusEmbeddings, NebiusRetriever, NebiusRetrievalTool
from langchain_core.documents import Document

# Create sample documents
docs = [
    Document(page_content="Paris is the capital of France and has the Eiffel Tower"),
    Document(page_content="Berlin is the capital of Germany and has the Brandenburg Gate"),
]

# Create embeddings and retriever
embeddings = NebiusEmbeddings()
retriever = NebiusRetriever(embeddings=embeddings, docs=docs)

# Create retrieval tool
tool = NebiusRetrievalTool(
    retriever=retriever,
    name="nebius_search",
    description="Search for information about European capitals"
)
```

***

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