> ## 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.

# SambanovaEmbeddings 集成

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

本指南将帮助您开始使用 SambaNova 向量嵌入模型 using LangChain. For detailed documentation on `SambaNovaEmbeddings` 功能和配置选项的详细文档，请参阅 [API reference](https://docs.sambanova.ai/cloud/docs/get-started/overview).

**[SambaNova](https://sambanova.ai/)'s** [SambaCloud](https://cloud.sambanova.ai/) is a platform for performing inference with open-source models

## 概述

### 集成详情

|                             提供商                            |                                    包                                   |
| :--------------------------------------------------------: | :--------------------------------------------------------------------: |
| [SambaNova](/oss/python/integrations/providers/sambanova/) | [`langchain-sambanova`](/oss/python/integrations/providers/sambanova/) |

## 设置

要访问 `SambaNovaEmbeddings` models you will need to create a [SambaCloud](http://cloud.sambanova.ai?utm_source=langchain\&utm_medium=external\&utm_campaign=cloud_signup) account, get an API key, install the `langchain_sambanova` 集成包。

```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
pip install langchain-sambanova
```

### 凭证

Get an API Key from [cloud.sambanova.ai](http://cloud.sambanova.ai/apis?utm_source=langchain\&utm_medium=external\&utm_campaign=cloud_signup).完成后设置 SAMBANOVA\_API\_KEY 环境变量：

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

if not os.getenv("SAMBANOVA_API_KEY"):
    os.environ["SAMBANOVA_API_KEY"] = getpass.getpass("Enter your SambaNova 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 SambaNova 集成位于 `langchain-sambanova` 包中：

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

## 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_sambanova import SambaNovaEmbeddings

embeddings = SambaNovaEmbeddings(
    model="E5-Mistral-7B-Instruct",
)
```

## 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
```

## 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
```

### 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
```

***

## API 参考

For detailed documentation on `SambaNovaEmbeddings` 功能和配置选项的详细文档，请参阅 [SambaNova Developer Guide](https://docs.sambanova.ai/cloud/docs/get-started/overview).

***

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