> ## Documentation Index > Fetch the complete documentation index at: https://langchain.idochub.dev/llms.txt > Use this file to discover all available pages before exploring further. # AzureChatOpenAI This guide will help you get started with AzureOpenAI [chat models](/oss/python/langchain/models). `AzureChatOpenAI` shares the same underlying base implementation as `ChatOpenAI`, which interfaces with OpenAI services directly. This page serves as a quickstart for authenticating and connecting your Azure OpenAI service to a LangChain chat model. Visit the [ChatOpenAI docs](/oss/python/integrations/chat/openai/) for details on available features, or head to the [API reference](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.azure.AzureChatOpenAI.html). Azure OpenAI has several chat models. You can find information about their latest models and their costs, context windows, and supported input types in the [Azure docs](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models). **Azure OpenAI vs OpenAI** Azure OpenAI refers to OpenAI models hosted on the [Microsoft Azure platform](https://azure.microsoft.com/en-us/products/ai-services/openai-service). OpenAI also provides its own model APIs. To access OpenAI services directly, use the [ChatOpenAI integration](/oss/python/integrations/chat/openai/). ## Overview ### Integration details | Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/azure) | Downloads | Version | | :--------------------------------------------------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------------- | :---: | :----------: | :-----------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------: | | [AzureChatOpenAI](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.azure.AzureChatOpenAI.html) | [langchain-openai](https://python.langchain.com/api_reference/openai/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-openai?style=flat-square\&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-openai?style=flat-square\&label=%20) | ### Model features | [Tool calling](/oss/python/langchain/tools) | [Structured output](/oss/python/langchain/structured-output) | JSON mode | [Image input](/oss/python/how-to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/oss/python/langchain/streaming/) | Native async | [Token usage](/oss/python/how-to/chat_token_usage_tracking/) | [Logprobs](/oss/python/how-to/logprobs/) | | :-----------------------------------------: | :----------------------------------------------------------: | :-------: | :--------------------------------------------------: | :---------: | :---------: | :-------------------------------------------------------: | :----------: | :----------------------------------------------------------: | :--------------------------------------: | | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ## Setup To access AzureOpenAI models you'll need to create an Azure account, create a deployment of an Azure OpenAI model, get the name and endpoint for your deployment, get an Azure OpenAI API key, and install the `langchain-openai` integration package. ### Credentials Head to the [Azure docs](https://learn.microsoft.com/en-us/azure/ai-services/openai/chatgpt-quickstart?tabs=command-line%2Cpython-new\&pivots=programming-language-python) to create your deployment and generate an API key. Once you've done this set the AZURE\_OPENAI\_API\_KEY and AZURE\_OPENAI\_ENDPOINT environment variables: ```python theme={null} import getpass import os if "AZURE_OPENAI_API_KEY" not in os.environ: os.environ["AZURE_OPENAI_API_KEY"] = getpass.getpass( "Enter your AzureOpenAI API key: " ) os.environ["AZURE_OPENAI_ENDPOINT"] = "https://YOUR-ENDPOINT.openai.azure.com/" ``` To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) 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 AzureOpenAI 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. * Replace `azure_deployment` with the name of your deployment, * You can find the latest supported `api_version` here: [learn.microsoft.com/en-us/azure/ai-services/openai/reference](https://learn.microsoft.com/en-us/azure/ai-services/openai/reference). ```python theme={null} from langchain_openai import AzureChatOpenAI llm = AzureChatOpenAI( azure_deployment="gpt-35-turbo", # or your deployment api_version="2023-06-01-preview", # or your api version temperature=0, max_tokens=None, timeout=None, max_retries=2, # other params... ) ``` ## Invocation ```python theme={null} messages = [ ( "system", "You are a helpful assistant that translates English to French. Translate the user sentence.", ), ("human", "I love programming."), ] ai_msg = llm.invoke(messages) ai_msg ``` ```output theme={null} AIMessage(content="J'adore la programmation.", response_metadata={'token_usage': {'completion_tokens': 8, 'prompt_tokens': 31, 'total_tokens': 39}, 'model_name': 'gpt-35-turbo', 'system_fingerprint': None, 'prompt_filter_results': [{'prompt_index': 0, 'content_filter_results': {'hate': {'filtered': False, 'severity': 'safe'}, 'self_harm': {'filtered': False, 'severity': 'safe'}, 'sexual': {'filtered': False, 'severity': 'safe'}, 'violence': {'filtered': False, 'severity': 'safe'}}}], 'finish_reason': 'stop', 'logprobs': None, 'content_filter_results': {'hate': {'filtered': False, 'severity': 'safe'}, 'self_harm': {'filtered': False, 'severity': 'safe'}, 'sexual': {'filtered': False, 'severity': 'safe'}, 'violence': {'filtered': False, 'severity': 'safe'}}}, id='run-bea4b46c-e3e1-4495-9d3a-698370ad963d-0', usage_metadata={'input_tokens': 31, 'output_tokens': 8, 'total_tokens': 39}) ``` ```python theme={null} print(ai_msg.content) ``` ```output theme={null} J'adore la programmation. ``` ## Chaining We can [chain](/oss/python/how-to/sequence/) our model with a prompt template like so: ```python theme={null} from langchain_core.prompts import ChatPromptTemplate prompt = ChatPromptTemplate.from_messages( [ ( "system", "You are a helpful assistant that translates {input_language} to {output_language}.", ), ("human", "{input}"), ] ) chain = prompt | llm chain.invoke( { "input_language": "English", "output_language": "German", "input": "I love programming.", } ) ``` ```output theme={null} AIMessage(content='Ich liebe das Programmieren.', response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 26, 'total_tokens': 32}, 'model_name': 'gpt-35-turbo', 'system_fingerprint': None, 'prompt_filter_results': [{'prompt_index': 0, 'content_filter_results': {'hate': {'filtered': False, 'severity': 'safe'}, 'self_harm': {'filtered': False, 'severity': 'safe'}, 'sexual': {'filtered': False, 'severity': 'safe'}, 'violence': {'filtered': False, 'severity': 'safe'}}}], 'finish_reason': 'stop', 'logprobs': None, 'content_filter_results': {'hate': {'filtered': False, 'severity': 'safe'}, 'self_harm': {'filtered': False, 'severity': 'safe'}, 'sexual': {'filtered': False, 'severity': 'safe'}, 'violence': {'filtered': False, 'severity': 'safe'}}}, id='run-cbc44038-09d3-40d4-9da2-c5910ee636ca-0', usage_metadata={'input_tokens': 26, 'output_tokens': 6, 'total_tokens': 32}) ``` ## Specifying model version Azure OpenAI responses contain `model_name` response metadata property, which is name of the model used to generate the response. However unlike native OpenAI responses, it does not contain the specific version of the model, which is set on the deployment in Azure. E.g. it does not distinguish between `gpt-35-turbo-0125` and `gpt-35-turbo-0301`. This makes it tricky to know which version of the model was used to generate the response, which as result can lead to e.g. wrong total cost calculation with `OpenAICallbackHandler`. To solve this problem, you can pass `model_version` parameter to `AzureChatOpenAI` class, which will be added to the model name in the llm output. This way you can easily distinguish between different versions of the model. ```python theme={null} %pip install -qU langchain-community ``` ```python theme={null} from langchain_community.callbacks import get_openai_callback with get_openai_callback() as cb: llm.invoke(messages) print( f"Total Cost (USD): ${format(cb.total_cost, '.6f')}" ) # without specifying the model version, flat-rate 0.002 USD per 1k input and output tokens is used ``` ```output theme={null} Total Cost (USD): $0.000063 ``` ```python theme={null} llm_0301 = AzureChatOpenAI( azure_deployment="gpt-35-turbo", # or your deployment api_version="2023-06-01-preview", # or your api version model_version="0301", ) with get_openai_callback() as cb: llm_0301.invoke(messages) print(f"Total Cost (USD): ${format(cb.total_cost, '.6f')}") ``` ```output theme={null} Total Cost (USD): $0.000074 ``` ## API reference For detailed documentation of all AzureChatOpenAI features and configurations head to the API reference: [python.langchain.com/api\_reference/openai/chat\_models/langchain\_openai.chat\_models.azure.AzureChatOpenAI.html](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.azure.AzureChatOpenAI.html)