Azure AI integrations

Integrate with Azure AI using LangChain Python.

This page covers all LangChain integrations with Microsoft Azure and its related projects.

Integration packages for Azure AI, Dynamic Sessions, SQL Server are maintained in the langchain-azure repository.

Chat models

We recommend developers start with the (langchain-azure-ai) to access all the models available in Azure AI Foundry.

Azure AI chat completions

Access models like Azure OpenAI, DeepSeek R1, Cohere, Phi and Mistral using the AzureAIOpenAIApiChatModel class.

pip

pip install -U langchain-azure-ai

uv add langchain-azure-ai

Configure your endpoint. You can use a project endpoint with DefaultAzureCredential, or set an API key directly.

export AZURE_AI_PROJECT_ENDPOINT=your-project-endpoint

from langchain_azure_ai.chat_models import AzureAIOpenAIApiChatModel
from azure.identity import DefaultAzureCredential

llm = AzureAIOpenAIApiChatModel(
    model="gpt-5.5",
    credential=DefaultAzureCredential(),
)

llm.invoke('Tell me a joke and include some emojis')

Embedding models

Azure AI model inference for embeddings

pip

pip install -U langchain-azure-ai

uv add langchain-azure-ai

Configure your endpoint. You can use a project endpoint with DefaultAzureCredential, or set an API key directly.

export AZURE_AI_PROJECT_ENDPOINT=your-project-endpoint

from langchain_azure_ai.embeddings import AzureAIOpenAIApiEmbeddingsModel
from azure.identity import DefaultAzureCredential

embed_model = AzureAIOpenAIApiEmbeddingsModel(
    model="text-embedding-ada-002",
    credential=DefaultAzureCredential(),
)

Vector stores

Azure CosmosDB NoSQL vector search

Azure CosmosDB NoSQL is a fully managed, globally distributed, serverless document database for modern applications. It stores data in flexible JSON documents and uses a SQL-like query language. This provides high performance, low latency, and automatic, elastic scalability. It also features integrated vector search capabilities for AI workloads like generative AI and RAG. This allows you to store, index, and query vector embeddings alongside your operational data in the same database. You can combine vector similarity search with traditional keyword-based search for relevant results and choose from various indexing methods for optimal performance. This unified approach simplifies application architecture and ensures data consistency.

We need to install the langchain-azure-cosmosdb and azure-cosmos packages to use this vector store.

pip

pip install -qU langchain-azure-cosmosdb azure-cosmos

uv add langchain-azure-cosmosdb azure-cosmos

from langchain_azure_cosmosdb import AzureCosmosDBNoSqlVectorSearch

vector_search = AzureCosmosDBNoSqlVectorSearch.from_documents(
    documents=docs,
    embedding=openai_embeddings,
    cosmos_client=cosmos_client,
    database_name=database_name,
    container_name=container_name,
    vector_embedding_policy=vector_embedding_policy,
    full_text_policy=full_text_policy,
    indexing_policy=indexing_policy,
    cosmos_container_properties=cosmos_container_properties,
    cosmos_database_properties={},
    full_text_search_enabled=True,
)

See a usage example.

Azure CosmosDB mongo vCore vector search

Azure CosmosDB Mongo vCore architecture makes it easy to create a database with full native MongoDB support. You can apply your MongoDB experience and continue to use your favorite MongoDB drivers, SDKs, and tools by pointing your application to the API for MongoDB (vCore) cluster’s connection string.

We need to install the pymongo package to use this vector store.

pip

pip install -qU pymongo

uv add pymongo

from langchain_azure_ai.vectorstores.azure_cosmos_db_mongo_vcore import (
    AzureCosmosDBMongoVCoreVectorSearch,
)

vectorstore = AzureCosmosDBMongoVCoreVectorSearch.from_documents(
    docs,
    openai_embeddings,
    collection=collection,
    index_name=INDEX_NAME,
)

See a usage example.

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