> ## 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.

# TogetherEmbeddings 集成

> 使用 LangChain Python 集成 TogetherEmbeddings 嵌入模型。

这将帮助您开始使用 LangChain 的 Together 嵌入模型。有关 `TogetherEmbeddings` 功能和配置选项的详细文档，请参阅 [API 参考](https://reference.langchain.com/python/langchain-together/embeddings/TogetherEmbeddings)。

## 概述

### 集成详情

<ItemTable category="embeddings" item="Together" />

## 设置

要访问 Together 嵌入模型，您需要创建一个 Together 账户，获取 API 密钥，并安装 `langchain-together` 集成包。

### 凭据

前往 [https://api.together.xyz/](https://api.together.xyz/) 注册 Together 并生成 API 密钥。完成后，设置 TOGETHER\_API\_KEY 环境变量：

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import getpass
import os

if not os.getenv("TOGETHER_API_KEY"):
    os.environ["TOGETHER_API_KEY"] = getpass.getpass("Enter your Together API key: ")
```

要为您的模型调用启用自动追踪，请设置您的 [LangSmith](/langsmith/home) API 密钥：

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
os.environ["LANGSMITH_TRACING"] = "true"
os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
```

### 安装

LangChain Together 集成位于 `langchain-together` 包中：

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
pip install -qU langchain-together
```

## 实例化

现在我们可以实例化我们的模型对象并生成聊天补全：

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

embeddings = TogetherEmbeddings(
    model="togethercomputer/m2-bert-80M-8k-retrieval",
)
```

## 索引和检索

嵌入模型通常用于检索增强生成 (RAG) 流程，既作为数据索引的一部分，也用于稍后的检索。更多详细说明，请参阅我们的 [RAG 教程](/oss/python/langchain/rag)。

下面，查看如何使用上面初始化的 `embeddings` 对象来索引和检索数据。在此示例中，我们将索引和检索 `InMemoryVectorStore` 中的示例文档。

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# Create a vector store with a sample text
from langchain_core.vectorstores import InMemoryVectorStore

text = "LangChain is the framework for building context-aware reasoning applications"

vectorstore = InMemoryVectorStore.from_texts(
    [text],
    embedding=embeddings,
)

# Use the vectorstore as a retriever
retriever = vectorstore.as_retriever()

# Retrieve the most similar text
retrieved_documents = retriever.invoke("What is LangChain?")

# show the retrieved document's content
retrieved_documents[0].page_content
```

```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
'LangChain is the framework for building context-aware reasoning applications'
```

## 直接用法

在底层，vectorstore 和 retriever 实现分别调用 `embeddings.embed_documents(...)` 和 `embeddings.embed_query(...)` 为 `from_texts` 和检索 `invoke` 操作中使用的文本创建嵌入。

您可以直接调用这些方法以获取您自己用例的嵌入。

### 嵌入单个文本

您可以使用 `embed_query` 嵌入单个文本或文档：

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
single_vector = embeddings.embed_query(text)
print(str(single_vector)[:100])  # Show the first 100 characters of the vector
```

```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
[0.3812227, -0.052848946, -0.10564975, 0.03480297, 0.2878488, 0.0084609175, 0.11605915, 0.05303011,
```

### 嵌入多个文本

您可以使用 `embed_documents` 嵌入多个文本：

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
text2 = (
    "LangGraph is a library for building stateful, multi-actor applications with LLMs"
)
two_vectors = embeddings.embed_documents([text, text2])
for vector in two_vectors:
    print(str(vector)[:100])  # Show the first 100 characters of the vector
```

```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
[0.3812227, -0.052848946, -0.10564975, 0.03480297, 0.2878488, 0.0084609175, 0.11605915, 0.05303011,
[0.066308185, -0.032866564, 0.115751594, 0.19082588, 0.14017, -0.26976448, -0.056340694, -0.26923394
```

***

## API 参考

有关 `TogetherEmbeddings` 功能和配置选项的详细文档，请参阅 [API 参考](https://reference.langchain.com/python/langchain-together/embeddings/TogetherEmbeddings)。

***

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