Add Azure OpenAI support (#282)

* Added support for connecting to Azure OpenAI via AzureOpenAIServerModel

docs/source/en/guided_tour.md

@@ -27,10 +27,11 @@ To initialize a minimal agent, you need at least these two arguments:
     - [`TransformersModel`] takes a pre-initialized `transformers` pipeline to run inference on your local machine using `transformers`.
     - [`HfApiModel`] leverages a `huggingface_hub.InferenceClient` under the hood.
     - [`LiteLLMModel`] lets you call 100+ different models through [LiteLLM](https://docs.litellm.ai/)!
+    - [`AzureOpenAIServerModel`] allows you to use OpenAI models deployed in [Azure](https://azure.microsoft.com/en-us/products/ai-services/openai-service).
 
 - `tools`, a list of `Tools` that the agent can use to solve the task. It can be an empty list. You can also add the default toolbox on top of your `tools` list by defining the optional argument `add_base_tools=True`.
 
-Once you have these two arguments, `tools` and `model`,  you can create an agent and run it. You can use any LLM you'd like, either through [Hugging Face API](https://huggingface.co/docs/api-inference/en/index), [transformers](https://github.com/huggingface/transformers/), [ollama](https://ollama.com/), or [LiteLLM](https://www.litellm.ai/).
+Once you have these two arguments, `tools` and `model`,  you can create an agent and run it. You can use any LLM you'd like, either through [Hugging Face API](https://huggingface.co/docs/api-inference/en/index), [transformers](https://github.com/huggingface/transformers/), [ollama](https://ollama.com/), [LiteLLM](https://www.litellm.ai/), or [Azure OpenAI](https://azure.microsoft.com/en-us/products/ai-services/openai-service).
 
 <hfoptions id="Pick a LLM">
 <hfoption id="Hugging Face API">
@@ -103,6 +104,49 @@ agent.run(
     "Could you give me the 118th number in the Fibonacci sequence?",
 )
 ```
+</hfoption>
+<hfoption id="Azure OpenAI">
+
+To connect to Azure OpenAI, you can either use `AzureOpenAIServerModel` directly, or use `LiteLLMModel` and configure it accordingly.
+
+To initialize an instance of `AzureOpenAIServerModel`, you need to pass your model deployment name and then either pass the `azure_endpoint`, `api_key`, and `api_version` arguments, or set the environment variables `AZURE_OPENAI_ENDPOINT`, `AZURE_OPENAI_API_KEY`, and `OPENAI_API_VERSION`.
+
+```python
+# !pip install smolagents[openai]
+from smolagents import CodeAgent, AzureOpenAIServerModel
+
+model = AzureOpenAIServerModel(model_id="gpt-4o-mini")
+agent = CodeAgent(tools=[], model=model, add_base_tools=True)
+
+agent.run(
+    "Could you give me the 118th number in the Fibonacci sequence?",
+)
+```
+
+Similarly, you can configure `LiteLLMModel` to connect to Azure OpenAI as follows:
+
+- pass your model deployment name as `model_id`, and make sure to prefix it with `azure/`
+- make sure to set the environment variable `AZURE_API_VERSION`
+- either pass the `api_base` and `api_key` arguments, or set the environment variables `AZURE_API_KEY`, and `AZURE_API_BASE`
+
+```python
+import os
+from smolagents import CodeAgent, LiteLLMModel
+
+AZURE_OPENAI_CHAT_DEPLOYMENT_NAME="gpt-35-turbo-16k-deployment" # example of deployment name
+
+os.environ["AZURE_API_KEY"] = "" # api_key
+os.environ["AZURE_API_BASE"] = "" # "https://example-endpoint.openai.azure.com"
+os.environ["AZURE_API_VERSION"] = "" # "2024-10-01-preview"
+
+model = LiteLLMModel(model_id="azure/" + AZURE_OPENAI_CHAT_DEPLOYMENT_NAME)
+agent = CodeAgent(tools=[], model=model, add_base_tools=True)
+
+agent.run(
+   "Could you give me the 118th number in the Fibonacci sequence?",
+)
+```
+
 </hfoption>
 </hfoptions>
 

docs/source/en/reference/agents.md

@@ -148,7 +148,7 @@ print(model(messages))
 
 [[autodoc]] LiteLLMModel
 
-### OpenAiServerModel
+### OpenAIServerModel
 
 This class lets you call any OpenAIServer compatible model.
 Here's how you can set it (you can customise the `api_base` url to point to another server):
@@ -160,4 +160,29 @@ model = OpenAIServerModel(
     api_base="https://api.openai.com/v1",
     api_key=os.environ["OPENAI_API_KEY"],
 )
-```
+```
+
+[[autodoc]] OpenAIServerModel
+
+### AzureOpenAIServerModel
+
+`AzureOpenAIServerModel` allows you to connect to any Azure OpenAI deployment. 
+
+Below you can find an example of how to set it up, note that you can omit the `azure_endpoint`, `api_key`, and `api_version` arguments, provided you've set the corresponding environment variables -- `AZURE_OPENAI_ENDPOINT`, `AZURE_OPENAI_API_KEY`, and `OPENAI_API_VERSION`.
+
+Pay attention to the lack of an `AZURE_` prefix for `OPENAI_API_VERSION`, this is due to the way the underlying [openai](https://github.com/openai/openai-python) package is designed. 
+
+```py
+import os
+
+from smolagents import AzureOpenAIServerModel
+
+model = AzureOpenAIServerModel(
+    model_id = os.environ.get("AZURE_OPENAI_MODEL"),
+    azure_endpoint=os.environ.get("AZURE_OPENAI_ENDPOINT"),
+    api_key=os.environ.get("AZURE_OPENAI_API_KEY"),
+    api_version=os.environ.get("OPENAI_API_VERSION")    
+)
+```
+
+[[autodoc]] AzureOpenAIServerModel

src/smolagents/models.py

@@ -620,6 +620,45 @@ def __call__(
         return message
 
 
+class AzureOpenAIServerModel(OpenAIServerModel):
+    """This model connects to an Azure OpenAI deployment.
+
+    Parameters:
+        model_id (`str`):
+            The model deployment name to use when connecting (e.g. "gpt-4o-mini").
+        azure_endpoint (`str`, *optional*):
+            The Azure endpoint, including the resource, e.g. `https://example-resource.azure.openai.com/`. If not provided, it will be inferred from the `AZURE_OPENAI_ENDPOINT` environment variable.
+        api_key (`str`, *optional*):
+            The API key to use for authentication. If not provided, it will be inferred from the `AZURE_OPENAI_API_KEY` environment variable.
+        api_version (`str`, *optional*):
+            The API version to use. If not provided, it will be inferred from the `OPENAI_API_VERSION` environment variable.
+        custom_role_conversions (`Dict[str, str]`, *optional*):
+            Custom role conversion mapping to convert message roles in others.
+            Useful for specific models that do not support specific message roles like "system".
+        **kwargs:
+            Additional keyword arguments to pass to the Azure OpenAI API.
+    """
+
+    def __init__(
+        self,
+        model_id: str,
+        azure_endpoint: Optional[str] = None,
+        api_key: Optional[str] = None,
+        api_version: Optional[str] = None,
+        custom_role_conversions: Optional[Dict[str, str]] = None,
+        **kwargs,
+    ):
+        # read the api key manually, to avoid super().__init__() trying to use the wrong api_key (OPENAI_API_KEY)
+        if api_key is None:
+            api_key = os.environ.get("AZURE_OPENAI_API_KEY")
+
+        super().__init__(model_id=model_id, api_key=api_key, custom_role_conversions=custom_role_conversions, **kwargs)
+        # if we've reached this point, it means the openai package is available (checked in baseclass) so go ahead and import it
+        import openai
+
+        self.client = openai.AzureOpenAI(api_key=api_key, api_version=api_version, azure_endpoint=azure_endpoint)
+
+
 __all__ = [
     "MessageRole",
     "tool_role_conversions",
@@ -629,5 +668,6 @@ def __call__(
     "HfApiModel",
     "LiteLLMModel",
     "OpenAIServerModel",
+    "AzureOpenAIServerModel",
     "ChatMessage",
 ]