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.
Overview
Integration details
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 to sign up to OpenAI and generate an API key. Once you’ve done this set the OPENAI_API_KEY environment variable:
import getpass
import os
if not os.getenv("OPENAI_API_KEY"):
os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter your OpenAI API key: ")
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:
pip install -qU langchain-openai
Instantiation
Now we can instantiate our model object and generate chat completions:
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
)
Azure OpenAI v1 API supportAs of langchain-openai>=1.0.1, OpenAIEmbeddings can be used directly with Azure OpenAI endpoints using the new v1 API, including support for Microsoft Entra ID authentication. See the Using with Azure OpenAI section below for details. 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.
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.
# 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
'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:
single_vector = embeddings.embed_query(text)
print(str(single_vector)[:100]) # Show the first 100 characters of the vector
[-0.019276829436421394, 0.0037708976306021214, -0.03294256329536438, 0.0037671267054975033, 0.008175
Embed multiple texts
You can embed multiple texts with embed_documents:
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
[-0.019260549917817116, 0.0037612367887049913, -0.03291035071015358, 0.003757466096431017, 0.0082049
[-0.010181212797760963, 0.023419594392180443, -0.04215526953339577, -0.001532090245746076, -0.023573
Using with Azure OpenAI
Azure OpenAI v1 API supportAs of langchain-openai>=1.0.1, OpenAIEmbeddings can be used directly with Azure OpenAI endpoints using the new v1 API. 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. 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:
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 authentication with automatic token refresh. Pass a token provider callable to the api_key parameter:
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"])
Installation requirementsTo use Microsoft Entra ID authentication, install the Azure Identity library:pip install azure-identity
api_key parameter when using
asynchronous functions. You must import DefaultAzureCredential from azure.identity.aio:
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"])
When using an async callable for the API key, you must use async methods (aembed_query, aembed_documents). Sync methods will raise an error.
API reference
For detailed documentation on OpenAIEmbeddings features and configuration options, please refer to the API reference.
