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Showing content from https://python.langchain.com/docs/integrations/text_embedding/azureopenai below:

AzureOpenAIEmbeddings | 🦜️🔗 LangChain

This will help you get started with AzureOpenAI embedding models using LangChain. For detailed documentation on AzureOpenAIEmbeddings features and configuration options, please refer to the API reference.

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

You’ll need to have an Azure OpenAI instance deployed. You can deploy a version on Azure Portal following this guide.

Once you have your instance running, make sure you have the name of your instance and key. You can find the key in the Azure Portal, under the “Keys and Endpoint” section of your instance.

AZURE_OPENAI_ENDPOINT=<YOUR API ENDPOINT>
AZURE_OPENAI_API_KEY=<YOUR_KEY>
AZURE_OPENAI_API_VERSION="2024-02-01"

import getpass
import os

if not os.getenv("AZURE_OPENAI_API_KEY"):
    os.environ["AZURE_OPENAI_API_KEY"] = getpass.getpass(
        "Enter your AzureOpenAI API key: "
    )

To enable automated tracing of your model calls, set your LangSmith API key:

The LangChain AzureOpenAI integration lives in the langchain-openai package:

%pip install -qU langchain-openai

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

from langchain_openai import AzureOpenAIEmbeddings

embeddings = AzureOpenAIEmbeddings(
    model="text-embedding-3-large",
    
    
    
    
)

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.

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

'LangChain is the framework for building context-aware reasoning applications'

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.

You can embed single texts or documents with embed_query:

single_vector = embeddings.embed_query(text)
print(str(single_vector)[:100])  

[-0.0011676070280373096, 0.007125577889382839, -0.014674457721412182, -0.034061674028635025, 0.01128

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])  

[-0.0011966148158535361, 0.007160289213061333, -0.014659193344414234, -0.03403077274560928, 0.011280
[-0.005595256108790636, 0.016757294535636902, -0.011055258102715015, -0.031094247475266457, -0.00363

For detailed documentation on AzureOpenAIEmbeddings features and configuration options, please refer to the API reference.

• Embedding model conceptual guide
• Embedding model how-to guides

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