Merge branch 'huggingface:main' into g-eoj/mlx-model-support

.github/workflows/tests.yml

@@ -87,3 +87,8 @@ jobs:
         run: |
           uv run pytest ./tests/test_utils.py
         if: ${{ success() || failure() }}
+
+      - name: Function type hints utils tests
+        run: |
+          uv run pytest ./tests/test_function_type_hints_utils.py
+        if: ${{ success() || failure() }}

README.md

@@ -37,13 +37,13 @@ limitations under the License.
 🧑‍💻 **First-class support for Code Agents**, i.e. agents that write their actions in code (as opposed to "agents being used to write code"). To make it secure, we support executing in sandboxed environments via [E2B](https://e2b.dev/).
  - On top of this [`CodeAgent`](https://huggingface.co/docs/smolagents/reference/agents#smolagents.CodeAgent) class, we still support the standard [`ToolCallingAgent`](https://huggingface.co/docs/smolagents/reference/agents#smolagents.ToolCallingAgent) that writes actions as JSON/text blobs.
 
-🤗 **Hub integrations**: you can share and load tools to/from the Hub, and more is to come!
+🤗 **Hub integrations**: you can share and load Gradio Spaces as tools to/from the Hub, and more is to come!
 
 🌐 **Support for any LLM**: it supports models hosted on the Hub loaded in their `transformers` version or through our inference API, but also supports models from OpenAI, Anthropic and many others via our [LiteLLM](https://www.litellm.ai/) integration.
 
 Full documentation can be found [here](https://huggingface.co/docs/smolagents/index).
 
-> [!NOTE]  
+> [!NOTE]
 > Check the our [launch blog post](https://huggingface.co/blog/smolagents) to learn more about `smolagents`!
 
 ## Quick demo
@@ -54,7 +54,7 @@ pip install smolagents
 ```
 Then define your agent, give it the tools it needs and run it!
 ```py
-from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel
+from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel,
 
 agent = CodeAgent(tools=[DuckDuckGoSearchTool()], model=HfApiModel())
 

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/index.md

@@ -29,7 +29,7 @@ This library offers:
 
 🧑‍💻 **First-class support for Code Agents**, i.e. agents that write their actions in code (as opposed to "agents being used to write code"), [read more here](tutorials/secure_code_execution).
 
-🤗 **Hub integrations**: you can share and load tools to/from the Hub, and more is to come!
+🤗 **Hub integrations**: you can share and load Gradio Spaces as tools to/from the Hub, and more is to come!
 
 <div class="mt-10">
   <div class="w-full flex flex-col space-y-4 md:space-y-0 md:grid md:grid-cols-2 md:gap-y-4 md:gap-x-5">

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