> ## Documentation Index
> Fetch the complete documentation index at: https://nvd-54.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# TogetherEmbeddings 集成

> 使用 LangChain Python 集成 TogetherEmbeddings 向量嵌入模型。

本指南将帮助您开始使用 Together 向量嵌入模型 using LangChain. For detailed documentation on `TogetherEmbeddings` 功能和配置选项的详细文档，请参阅 [API reference](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 embedding 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。

### 凭证

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: ")
```

要启用模型调用的自动追踪，请设置您的 [LangSmith](/langsmith/home) API key:

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
os.environ["LANGSMITH_TRACING"] = "true"
os.environ["LANGSMITH_API_KEY"] = getpass.getpass("请输入您的 LangSmith API 密钥: ")
```

### 安装

LangChain 的 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",
)
```

## 索引与检索

向量嵌入模型常用于检索增强生成 (RAG) 流程中, 既用于索引数据，也用于后续检索数据。 更详细的说明请参阅我们的 [RAG tutorials](/oss/python/langchain/rag).

下面展示如何使用 `embeddings` 对象来索引和检索数据。 在此示例中，我们将在 `InMemoryVectorStore`.

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# 使用示例文本创建向量存储
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'
```

## 直接使用

Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `invoke` operations, respectively.

You can directly call these methods to get embeddings for your own use cases.

### Embed single texts

You can embed single texts or documents with `embed_query`:

```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,
```

### Embed multiple texts

You can embed multiple texts with `embed_documents`:

```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

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

***

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