> ## 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.

# Voyage AI 集成

> 使用 LangChain Python 与 Voyage AI 嵌入模型集成。

> [Voyage AI](https://www.voyageai.com/) 提供尖端的嵌入/向量化模型。

让我们加载 Voyage AI Embedding 类。（使用 `pip install langchain-voyageai` 安装 LangChain 合作伙伴包）

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

Voyage AI 利用 API 密钥来监控使用情况和管理权限。要获取您的密钥，请在我们 [主页](https://www.voyageai.com) 上创建账户。然后，使用您的 API 密钥创建一个 VoyageEmbeddings 模型。您可以使用以下任何模型：（[来源](https://docs.voyageai.com/docs/embeddings)）：

* `voyage-4-large`
* `voyage-4`
* `voyage-4-lite`
* `voyage-context-3`
* `voyage-3.5`
* `voyage-3.5-lite`
* `voyage-3-large`
* `voyage-3`
* `voyage-3-lite`
* `voyage-large-2`
* `voyage-code-2`
* `voyage-2`
* `voyage-law-2`
* `voyage-large-2-instruct`
* `voyage-finance-2`
* `voyage-multilingual-2`

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
embeddings = VoyageAIEmbeddings(
    voyage_api_key="[ Your Voyage API key ]", model="voyage-law-2"
)
```

准备文档并使用 `embed_documents` 获取它们的嵌入向量。

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
documents = [
    "Caching embeddings enables the storage or temporary caching of embeddings, eliminating the necessity to recompute them each time.",
    "An LLMChain is a chain that composes basic LLM functionality. It consists of a PromptTemplate and a language model (either an LLM or chat model). It formats the prompt template using the input key values provided (and also memory key values, if available), passes the formatted string to LLM and returns the LLM output.",
    "A Runnable represents a generic unit of work that can be invoked, batched, streamed, and/or transformed.",
]
```

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
documents_embds = embeddings.embed_documents(documents)
```

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
documents_embds[0][:5]
```

```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
[0.0562174916267395,
 0.018221192061901093,
 0.0025736060924828053,
 -0.009720131754875183,
 0.04108370840549469]
```

同样，使用 `embed_query` 来嵌入查询。

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
query = "What's an LLMChain?"
```

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
query_embd = embeddings.embed_query(query)
```

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
query_embd[:5]
```

```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
[-0.0052348352037370205,
 -0.040072452276945114,
 0.0033957737032324076,
 0.01763271726667881,
 -0.019235141575336456]
```

## 极简检索系统

嵌入的主要特性是，两个嵌入之间的余弦相似度捕捉了对应原始段落的语义相关性。这使我们能够使用嵌入来进行语义检索/搜索。

我们可以基于余弦相似度在文档嵌入中找到几个最接近的嵌入，并使用 LangChain 中的 `KNNRetriever` 类检索相应的文档。

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_community.retrievers import KNNRetriever

retriever = KNNRetriever.from_texts(documents, embeddings)

# retrieve the most relevant documents
result = retriever.invoke(query)
top1_retrieved_doc = result[0].page_content  # return the top1 retrieved result

print(top1_retrieved_doc)
```

```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
An LLMChain is a chain that composes basic LLM functionality. It consists of a PromptTemplate and a language model (either an LLM or chat model). It formats the prompt template using the input key values provided (and also memory key values, if available), passes the formatted string to LLM and returns the LLM output.
```

***

<div className="source-links">
  <Callout icon="edit">
    [Edit this page on GitHub](https://github.com/langchain-ai/docs/edit/main/src/i18n\zh-CN\oss\python\integrations\embeddings\voyageai.mdx) or [file an issue](https://github.com/langchain-ai/docs/issues/new/choose).
  </Callout>

  <Callout icon="terminal-2">
    [Connect these docs](/use-these-docs) to Claude, VSCode, and more via MCP for real-time answers.
  </Callout>
</div>