> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-opensw-1781706951-9d0138e.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# OpenAIEmbeddings integration

> Integrate with the OpenAIEmbeddings embedding model using LangChain Python.

This will help you get started with OpenAI embedding models using LangChain. For detailed documentation on `OpenAIEmbeddings` features and configuration options, please refer to the [API reference](https://reference.langchain.com/python/langchain-openai/embeddings/base/OpenAIEmbeddings).

## Overview

### Integration details

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

## Setup

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

### Credentials

Head to [platform.openai.com](https://platform.openai.com) to sign up to OpenAI and generate an API key. Once you’ve done this set the OPENAI\_API\_KEY environment variable:

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

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

If you're routing requests through a proxy or service emulator, you can set the base URL via env var instead of passing `base_url`. Resolution order (first match wins):

1. Explicit `base_url` (or `openai_api_base`) kwarg.
2. `OPENAI_API_BASE` — read by LangChain at init.
3. `OPENAI_BASE_URL` — read by the underlying `openai` SDK client.

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

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

### Installation

The LangChain OpenAI integration lives in the `langchain-openai` package:

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

## Instantiation

Now we can instantiate our model object and generate chat completions:

```python theme={null}
from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(
    model="text-embedding-3-large",
    # With the `text-embedding-3` class
    # of models, you can specify the size
    # of the embeddings you want returned.
    # dimensions=1024
)
```

<Info>
  **Azure OpenAI v1 API support**

  As of `langchain-openai>=1.0.1`, `OpenAIEmbeddings` can be used directly with Azure OpenAI endpoints using the new [v1 API](https://learn.microsoft.com/en-us/azure/ai-foundry/openai/api-version-lifecycle?tabs=python#next-generation-api-1), including support for Microsoft Entra ID authentication. See the [Using with Azure OpenAI](#using-with-azure-openai) section below for details.
</Info>

## Indexing and retrieval

Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/oss/python/langchain/rag).

Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`.

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

## Direct usage

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={null}
single_vector = embeddings.embed_query(text)
print(str(single_vector)[:100])  # Show the first 100 characters of the vector
```

```text theme={null}
[-0.019276829436421394, 0.0037708976306021214, -0.03294256329536438, 0.0037671267054975033, 0.008175
```

### Embed multiple texts

You can embed multiple texts with `embed_documents`:

```python theme={null}
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={null}
[-0.019260549917817116, 0.0037612367887049913, -0.03291035071015358, 0.003757466096431017, 0.0082049
[-0.010181212797760963, 0.023419594392180443, -0.04215526953339577, -0.001532090245746076, -0.023573
```

## Using with Azure OpenAI

<Info>
  **Azure OpenAI v1 API support**

  As of `langchain-openai>=1.0.1`, `OpenAIEmbeddings` can be used directly with Azure OpenAI endpoints using the new [v1 API](https://learn.microsoft.com/en-us/azure/ai-foundry/openai/api-version-lifecycle?tabs=python#next-generation-api-1). This provides a unified way to use OpenAI embeddings whether hosted on OpenAI or Azure.

  For the traditional Azure-specific implementation, continue to use [`AzureOpenAIEmbeddings`](/oss/python/integrations/embeddings/azure_openai).
</Info>

### Using Azure OpenAI v1 API with API Key

To use `OpenAIEmbeddings` with Azure OpenAI, set the `base_url` to your Azure endpoint with `/openai/v1/` appended:

```python theme={null}
from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(
    model="text-embedding-3-large",  # Your Azure deployment name
    base_url="https://{your-resource-name}.openai.azure.com/openai/v1/",
    api_key="your-azure-api-key"
)

# Use as normal
vector = embeddings.embed_query("Hello world")
```

### Using Azure OpenAI with Microsoft entra ID

The v1 API adds native support for [Microsoft Entra ID](https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/managed-identity) authentication with automatic token refresh. Pass a token provider callable to the `api_key` parameter:

```python theme={null}
from azure.identity import DefaultAzureCredential, get_bearer_token_provider
from langchain_openai import OpenAIEmbeddings

# Create a token provider that handles automatic refresh
token_provider = get_bearer_token_provider(
    DefaultAzureCredential(),
    "https://cognitiveservices.azure.com/.default"
)

embeddings = OpenAIEmbeddings(
    model="text-embedding-3-large",  # Your Azure deployment name
    base_url="https://{your-resource-name}.openai.azure.com/openai/v1/",
    api_key=token_provider  # Callable that handles token refresh
)

# Use as normal
vectors = embeddings.embed_documents(["Hello", "World"])
```

<Tip>
  **Installation requirements**

  To use Microsoft Entra ID authentication, install the Azure Identity library:

  ```bash theme={null}
  pip install azure-identity
  ```
</Tip>

You can also pass a token provider callable to the `api_key` parameter when using
asynchronous functions. You must import DefaultAzureCredential from `azure.identity.aio`:

```python theme={null}
from azure.identity.aio import DefaultAzureCredential
from langchain_openai import OpenAIEmbeddings

credential = DefaultAzureCredential()

embeddings_async = OpenAIEmbeddings(
    model="text-embedding-3-large",
    api_key=credential
)

# Use async methods when using async callable
vectors = await embeddings_async.aembed_documents(["Hello", "World"])

```

<Note>
  When using an async callable for the API key, you must use async methods (`aembed_query`, `aembed_documents`). Sync methods will raise an error.
</Note>

***

## API reference

For detailed documentation on `OpenAIEmbeddings` features and configuration options, please refer to the [API reference](https://reference.langchain.com/python/langchain-openai/embeddings/base/OpenAIEmbeddings).

***

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