> ## 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.

# AIMlAPIEmbeddings 集成

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

This guide helps you get started with AI/ML API 向量嵌入模型 using LangChain.

## 概述

### 集成详情

| 类                   | 包                                                                               |  本地 | JS 支持 |                                                 下载量                                                |                                                版本                                               |
| :------------------ | :------------------------------------------------------------------------------ | :-: | :---: | :------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: |
| `AIMLAPIEmbeddings` | [`langchain-aimlapi`](https://reference.langchain.com/python/langchain-aimlapi) |  ❌  |   ❌   | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-aimlapi?style=flat-square\&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-aimlapi?style=flat-square\&label=%20) |

## 设置

要访问 AI/ML API embedding 模型，您需要创建一个 账户，获取 API 密钥，并安装 `langchain-aimlapi` 集成包。

### 凭证

前往 [aimlapi.com](https://aimlapi.com/app/?utm_source=langchain\&utm_medium=github\&utm_campaign=integration) to sign up 并生成 API 密钥。 完成后设置 `AIMLAPI_API_KEY` 环境变量：

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

if not os.getenv("AIMLAPI_API_KEY"):
    os.environ["AIMLAPI_API_KEY"] = getpass.getpass("Enter your AI/ML 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_API_KEY"] = getpass.getpass("请输入您的 LangSmith API 密钥: ")
os.environ["LANGSMITH_TRACING"] = "true"
```

### 安装

LangChain 的 AI/ML API 集成位于 `langchain-aimlapi` 包中：

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

## 实例化

Now we can instantiate our embeddings model and perform embedding operations:

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

embeddings = AIMLAPIEmbeddings(
    model="text-embedding-ada-002",
)
```

## 索引与检索

向量嵌入模型常用于检索增强生成 (RAG) 流程中. Below is how to index and retrieve data using the `embeddings` object we initialized above with `InMemoryVectorStore`.

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_core.vectorstores import InMemoryVectorStore

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

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

retriever = vectorstore.as_retriever()

retrieved_documents = retriever.invoke("What is LangChain?")
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

You can directly call `embed_query` and `embed_documents` for custom embedding scenarios.

### Embed single text

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
single_vector = embeddings.embed_query(text)
print(str(single_vector)[:100])
```

### Embed multiple texts

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
text2 = "LangGraph is a library for building stateful, multi-actor applications with LLMs"

vectors = embeddings.embed_documents([text, text2])
for vector in vectors:
    print(str(vector)[:100])
```

***

***

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