> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-opensw-1782332329-96d87c7.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# AIMlAPIEmbeddings integration

> Integrate with the AIMlAPIEmbeddings embedding model using LangChain Python.

This guide helps you get started with AI/ML API embedding models using LangChain.

## Overview

### Integration details

| Class               | Package                                                                         | Local | JS support |                                              Downloads                                             |                                             Version                                             |
| :------------------ | :------------------------------------------------------------------------------ | :---: | :--------: | :------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: |
| `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) |

## Setup

To access AI/ML API embedding models you'll need to create an account, get an API key, and install the `langchain-aimlapi` integration package.

### Credentials

Head to [aimlapi.com](https://aimlapi.com/app/?utm_source=langchain\&utm_medium=github\&utm_campaign=integration) to sign up and generate an API key. Once you've done this set the `AIMLAPI_API_KEY` environment variable:

```python theme={null}
import getpass
import os

if not os.getenv("AIMLAPI_API_KEY"):
    os.environ["AIMLAPI_API_KEY"] = getpass.getpass("Enter your AI/ML API key: ")
```

To enable automated tracing of your model calls, set your [LangSmith](/langsmith/observability) API key:

```python theme={null}
os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
os.environ["LANGSMITH_TRACING"] = "true"
```

### Installation

The LangChain AI/ML API integration lives in the `langchain-aimlapi` package:

```python theme={null}
pip install -qU langchain-aimlapi
```

## Instantiation

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

```python theme={null}
from langchain_aimlapi import AIMLAPIEmbeddings

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

## Indexing and retrieval

Embedding models are often used in retrieval-augmented generation (RAG) flows. Below is how to index and retrieve data using the `embeddings` object we initialized above with `InMemoryVectorStore`.

```python theme={null}
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={null}
'LangChain is the framework for building context-aware reasoning applications'
```

## Direct usage

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

### Embed single text

```python theme={null}
single_vector = embeddings.embed_query(text)
print(str(single_vector)[:100])
```

### Embed multiple texts

```python theme={null}
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])
```

***

***

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

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