Add AzureOpenAiAgent (#24058)

* Add AzureOpenAiAgent

* quality

* Update src/transformers/tools/agents.py

Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr>

---------

Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr>

docs/source/en/main_classes/agent.mdx

@@ -38,6 +38,10 @@ We provide three types of agents: [`HfAgent`] uses inference endpoints for opens
 
 [[autodoc]] OpenAiAgent
 
+### AzureOpenAiAgent
+
+[[autodoc]] AzureOpenAiAgent
+
 ### Agent
 
 [[autodoc]] Agent

src/transformers/__init__.py

@@ -619,6 +619,7 @@ _import_structure = {
     ],
     "tools": [
         "Agent",
+        "AzureOpenAiAgent",
         "HfAgent",
         "LocalAgent",
         "OpenAiAgent",
@@ -4410,6 +4411,7 @@ if TYPE_CHECKING:
     # Tools
     from .tools import (
         Agent,
+        AzureOpenAiAgent,
         HfAgent,
         LocalAgent,
         OpenAiAgent,

src/transformers/tools/__init__.py

@@ -24,7 +24,7 @@ from ..utils import (
 
 
 _import_structure = {
-    "agents": ["Agent", "HfAgent", "LocalAgent", "OpenAiAgent"],
+    "agents": ["Agent", "AzureOpenAiAgent", "HfAgent", "LocalAgent", "OpenAiAgent"],
     "base": ["PipelineTool", "RemoteTool", "Tool", "launch_gradio_demo", "load_tool"],
 }
 
@@ -46,7 +46,7 @@ else:
     _import_structure["translation"] = ["TranslationTool"]
 
 if TYPE_CHECKING:
-    from .agents import Agent, HfAgent, LocalAgent, OpenAiAgent
+    from .agents import Agent, AzureOpenAiAgent, HfAgent, LocalAgent, OpenAiAgent
     from .base import PipelineTool, RemoteTool, Tool, launch_gradio_demo, load_tool
 
     try:

src/transformers/tools/agents.py

@@ -453,6 +453,132 @@ class OpenAiAgent(Agent):
         return [answer["text"] for answer in result["choices"]]
 
 
+class AzureOpenAiAgent(Agent):
+    """
+    Agent that uses Azure OpenAI to generate code. See the [official
+    documentation](https://learn.microsoft.com/en-us/azure/cognitive-services/openai/) to learn how to deploy an openAI
+    model on Azure
+
+    <Tip warning={true}>
+
+    The openAI models are used in generation mode, so even for the `chat()` API, it's better to use models like
+    `"text-davinci-003"` over the chat-GPT variant. Proper support for chat-GPT models will come in a next version.
+
+    </Tip>
+
+    Args:
+        deployment_id (`str`):
+            The name of the deployed Azure openAI model to use.
+        api_key (`str`, *optional*):
+            The API key to use. If unset, will look for the environment variable `"AZURE_OPENAI_API_KEY"`.
+        resource_name (`str`, *optional*):
+            The name of your Azure OpenAI Resource. If unset, will look for the environment variable
+            `"AZURE_OPENAI_RESOURCE_NAME"`.
+        api_version (`str`, *optional*, default to `"2022-12-01"`):
+            The API version to use for this agent.
+        is_chat_mode (`bool`, *optional*):
+            Whether you are using a completion model or a chat model (see note above, chat models won't be as
+            efficient). Will default to `gpt` being in the `deployment_id` or not.
+        chat_prompt_template (`str`, *optional*):
+            Pass along your own prompt if you want to override the default template for the `chat` method. Can be the
+            actual prompt template or a repo ID (on the Hugging Face Hub). The prompt should be in a file named
+            `chat_prompt_template.txt` in this repo in this case.
+        run_prompt_template (`str`, *optional*):
+            Pass along your own prompt if you want to override the default template for the `run` method. Can be the
+            actual prompt template or a repo ID (on the Hugging Face Hub). The prompt should be in a file named
+            `run_prompt_template.txt` in this repo in this case.
+        additional_tools ([`Tool`], list of tools or dictionary with tool values, *optional*):
+            Any additional tools to include on top of the default ones. If you pass along a tool with the same name as
+            one of the default tools, that default tool will be overridden.
+
+    Example:
+
+    ```py
+    from transformers import AzureOpenAiAgent
+
+    agent = AzureAiAgent(deployment_id="Davinci-003", api_key=xxx, resource_name=yyy)
+    agent.run("Is the following `text` (in Spanish) positive or negative?", text="¡Este es un API muy agradable!")
+    ```
+    """
+
+    def __init__(
+        self,
+        deployment_id,
+        api_key=None,
+        resource_name=None,
+        api_version="2022-12-01",
+        is_chat_model=None,
+        chat_prompt_template=None,
+        run_prompt_template=None,
+        additional_tools=None,
+    ):
+        if not is_openai_available():
+            raise ImportError("Using `OpenAiAgent` requires `openai`: `pip install openai`.")
+
+        self.deployment_id = deployment_id
+        openai.api_type = "azure"
+        if api_key is None:
+            api_key = os.environ.get("AZURE_OPENAI_API_KEY", None)
+        if api_key is None:
+            raise ValueError(
+                "You need an Azure openAI key to use `AzureOpenAIAgent`. If you have one, set it in your env with "
+                "`os.environ['AZURE_OPENAI_API_KEY'] = xxx."
+            )
+        else:
+            openai.api_key = api_key
+        if resource_name is None:
+            resource_name = os.environ.get("AZURE_OPENAI_RESOURCE_NAME", None)
+        if resource_name is None:
+            raise ValueError(
+                "You need a resource_name to use `AzureOpenAIAgent`. If you have one, set it in your env with "
+                "`os.environ['AZURE_OPENAI_RESOURCE_NAME'] = xxx."
+            )
+        else:
+            openai.api_base = f"https://{resource_name}.openai.azure.com"
+        openai.api_version = api_version
+
+        if is_chat_model is None:
+            is_chat_model = "gpt" in deployment_id.lower()
+        self.is_chat_model = is_chat_model
+
+        super().__init__(
+            chat_prompt_template=chat_prompt_template,
+            run_prompt_template=run_prompt_template,
+            additional_tools=additional_tools,
+        )
+
+    def generate_many(self, prompts, stop):
+        if self.is_chat_model:
+            return [self._chat_generate(prompt, stop) for prompt in prompts]
+        else:
+            return self._completion_generate(prompts, stop)
+
+    def generate_one(self, prompt, stop):
+        if self.is_chat_model:
+            return self._chat_generate(prompt, stop)
+        else:
+            return self._completion_generate([prompt], stop)[0]
+
+    def _chat_generate(self, prompt, stop):
+        result = openai.ChatCompletion.create(
+            engine=self.deployment_id,
+            messages=[{"role": "user", "content": prompt}],
+            temperature=0,
+            stop=stop,
+        )
+        return result["choices"][0]["message"]["content"]
+
+    def _completion_generate(self, prompts, stop):
+        result = openai.Completion.create(
+            engine=self.deployment_id,
+            prompt=prompts,
+            temperature=0,
+            stop=stop,
+            max_tokens=200,
+        )
+        return [answer["text"] for answer in result["choices"]]
+
+
 class HfAgent(Agent):
     """
     Agent that uses an inference endpoint to generate code.