# AzureChatOpenAI > **Class** in `langchain_openai` 📖 [View in docs](https://reference.langchain.com/python/langchain-openai/chat_models/azure/AzureChatOpenAI) Azure OpenAI chat model integration. ## Signature ```python AzureChatOpenAI() ``` ## Description **Setup:** Head to the Azure [OpenAI quickstart guide](https://learn.microsoft.com/en-us/azure/ai-foundry/openai/chatgpt-quickstart?tabs=keyless%2Ctypescript-keyless%2Cpython-new%2Ccommand-line&pivots=programming-language-python) to create your Azure OpenAI deployment. Then install `langchain-openai` and set environment variables `AZURE_OPENAI_API_KEY` and `AZURE_OPENAI_ENDPOINT`: ```bash pip install -U langchain-openai export AZURE_OPENAI_API_KEY="your-api-key" export AZURE_OPENAI_ENDPOINT="https://your-endpoint.openai.azure.com/" ``` Key init args — completion params: azure_deployment: Name of Azure OpenAI deployment to use. temperature: Sampling temperature. max_tokens: Max number of tokens to generate. logprobs: Whether to return logprobs. Key init args — client params: api_version: Azure OpenAI REST API version to use (distinct from the version of the underlying model). [See more on the different versions.](https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#rest-api-versioning) timeout: Timeout for requests. max_retries: Max number of retries. organization: OpenAI organization ID. If not passed in will be read from env var `OPENAI_ORG_ID`. model: The name of the underlying OpenAI model. Used for tracing and token counting. Does not affect completion. E.g. `'gpt-4'`, `'gpt-35-turbo'`, etc. model_version: The version of the underlying OpenAI model. Used for tracing and token counting. Does not affect completion. E.g., `'0125'`, `'0125-preview'`, etc. See full list of supported init args and their descriptions in the params section. **Instantiate:** ```python from langchain_openai import AzureChatOpenAI model = AzureChatOpenAI( azure_deployment="your-deployment", api_version="2024-05-01-preview", temperature=0, max_tokens=None, timeout=None, max_retries=2, # organization="...", # model="gpt-35-turbo", # model_version="0125", # other params... ) ``` !!! note Any param which is not explicitly supported will be passed directly to the `openai.AzureOpenAI.chat.completions.create(...)` API every time to the model is invoked. For example: ```python from langchain_openai import AzureChatOpenAI import openai AzureChatOpenAI(..., logprobs=True).invoke(...) # results in underlying API call of: openai.AzureOpenAI(..).chat.completions.create(..., logprobs=True) # which is also equivalent to: AzureChatOpenAI(...).invoke(..., logprobs=True) ``` **Invoke:** ```python messages = [ ( "system", "You are a helpful translator. Translate the user sentence to French.", ), ("human", "I love programming."), ] model.invoke(messages) ``` ```python AIMessage( content="J'adore programmer.", usage_metadata={ "input_tokens": 28, "output_tokens": 6, "total_tokens": 34, }, response_metadata={ "token_usage": { "completion_tokens": 6, "prompt_tokens": 28, "total_tokens": 34, }, "model_name": "gpt-4", "system_fingerprint": "fp_7ec89fabc6", "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-6d7a5282-0de0-4f27-9cc0-82a9db9a3ce9-0", ) ``` **Stream:** ```python for chunk in model.stream(messages): print(chunk.text, end="") ``` ```python AIMessageChunk(content="", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f") AIMessageChunk(content="J", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f") AIMessageChunk(content="'", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f") AIMessageChunk(content="ad", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f") AIMessageChunk(content="ore", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f") AIMessageChunk(content=" la", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f") AIMessageChunk( content=" programm", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f" ) AIMessageChunk(content="ation", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f") AIMessageChunk(content=".", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f") AIMessageChunk( content="", response_metadata={ "finish_reason": "stop", "model_name": "gpt-4", "system_fingerprint": "fp_811936bd4f", }, id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f", ) ``` ```python stream = model.stream(messages) full = next(stream) for chunk in stream: full += chunk full ``` ```python AIMessageChunk( content="J'adore la programmation.", response_metadata={ "finish_reason": "stop", "model_name": "gpt-4", "system_fingerprint": "fp_811936bd4f", }, id="run-ba60e41c-9258-44b8-8f3a-2f10599643b3", ) ``` **Async:** ```python await model.ainvoke(messages) # stream: # async for chunk in (await model.astream(messages)) # batch: # await model.abatch([messages]) ``` **Tool calling:** ```python from pydantic import BaseModel, Field class GetWeather(BaseModel): '''Get the current weather in a given location''' location: str = Field( ..., description="The city and state, e.g. San Francisco, CA" ) class GetPopulation(BaseModel): '''Get the current population in a given location''' location: str = Field( ..., description="The city and state, e.g. San Francisco, CA" ) model_with_tools = model.bind_tools([GetWeather, GetPopulation]) ai_msg = model_with_tools.invoke( "Which city is hotter today and which is bigger: LA or NY?" ) ai_msg.tool_calls ``` ```python [ { "name": "GetWeather", "args": {"location": "Los Angeles, CA"}, "id": "call_6XswGD5Pqk8Tt5atYr7tfenU", }, { "name": "GetWeather", "args": {"location": "New York, NY"}, "id": "call_ZVL15vA8Y7kXqOy3dtmQgeCi", }, { "name": "GetPopulation", "args": {"location": "Los Angeles, CA"}, "id": "call_49CFW8zqC9W7mh7hbMLSIrXw", }, { "name": "GetPopulation", "args": {"location": "New York, NY"}, "id": "call_6ghfKxV264jEfe1mRIkS3PE7", }, ] ``` **Structured output:** ```python from typing import Optional from pydantic import BaseModel, Field class Joke(BaseModel): '''Joke to tell user.''' setup: str = Field(description="The setup of the joke") punchline: str = Field(description="The punchline to the joke") rating: int | None = Field( description="How funny the joke is, from 1 to 10" ) structured_model = model.with_structured_output(Joke) structured_model.invoke("Tell me a joke about cats") ``` ```python Joke( setup="Why was the cat sitting on the computer?", punchline="To keep an eye on the mouse!", rating=None, ) ``` See `AzureChatOpenAI.with_structured_output()` for more. **JSON mode:** ```python json_model = model.bind(response_format={"type": "json_object"}) ai_msg = json_model.invoke( "Return a JSON object with key 'random_ints' and a value of 10 random ints in [0-99]" ) ai_msg.content ``` ```python '\\n{\\n "random_ints": [23, 87, 45, 12, 78, 34, 56, 90, 11, 67]\\n}' ``` **Image input:** ```python import base64 import httpx from langchain_core.messages import HumanMessage image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" image_data = base64.b64encode(httpx.get(image_url).content).decode("utf-8") message = HumanMessage( content=[ {"type": "text", "text": "describe the weather in this image"}, { "type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_data}"}, }, ] ) ai_msg = model.invoke([message]) ai_msg.content ``` ```python "The weather in the image appears to be quite pleasant. The sky is mostly clear" ``` **Token usage:** ```python ai_msg = model.invoke(messages) ai_msg.usage_metadata ``` ```python {"input_tokens": 28, "output_tokens": 5, "total_tokens": 33} ``` Logprobs: ```python logprobs_model = model.bind(logprobs=True) ai_msg = logprobs_model.invoke(messages) ai_msg.response_metadata["logprobs"] ``` ```python { "content": [ { "token": "J", "bytes": [74], "logprob": -4.9617593e-06, "top_logprobs": [], }, { "token": "'adore", "bytes": [39, 97, 100, 111, 114, 101], "logprob": -0.25202933, "top_logprobs": [], }, { "token": " la", "bytes": [32, 108, 97], "logprob": -0.20141791, "top_logprobs": [], }, { "token": " programmation", "bytes": [ 32, 112, 114, 111, 103, 114, 97, 109, 109, 97, 116, 105, 111, 110, ], "logprob": -1.9361265e-07, "top_logprobs": [], }, { "token": ".", "bytes": [46], "logprob": -1.2233183e-05, "top_logprobs": [], }, ] } ``` Response metadata ```python ai_msg = model.invoke(messages) ai_msg.response_metadata ``` ```python { "token_usage": { "completion_tokens": 6, "prompt_tokens": 28, "total_tokens": 34, }, "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"}, }, } ``` ## Extends - `BaseChatOpenAI` ## Properties - `azure_endpoint` - `deployment_name` - `openai_api_version` - `openai_api_key` - `azure_ad_token` - `azure_ad_token_provider` - `azure_ad_async_token_provider` - `model_version` - `openai_api_type` - `validate_base_url` - `model_name` - `disabled_params` - `max_tokens` - `lc_secrets` - `lc_attributes` ## Methods - [`get_lc_namespace()`](https://reference.langchain.com/python/langchain-openai/chat_models/azure/AzureChatOpenAI/get_lc_namespace) - [`is_lc_serializable()`](https://reference.langchain.com/python/langchain-openai/chat_models/azure/AzureChatOpenAI/is_lc_serializable) - [`validate_environment()`](https://reference.langchain.com/python/langchain-openai/chat_models/azure/AzureChatOpenAI/validate_environment) - [`with_structured_output()`](https://reference.langchain.com/python/langchain-openai/chat_models/azure/AzureChatOpenAI/with_structured_output) --- [View source on GitHub](https://github.com/langchain-ai/langchain/blob/6fb37dba71da807af60aa7b909f71f0625a666bf/libs/partners/openai/langchain_openai/chat_models/azure.py#L37)