Add ZhipuAI provider

Add ZhipuAI provider integration to the python package.

* Add `aisuite/providers/zhipuai_provider.py` implementing the `ZhipuaiProvider` class using the `zhipuai` library.
* Update `ProviderFactory` in `aisuite/provider.py` to include ZhipuAI as a supported provider.
* Add test cases for `ZhipuaiProvider` in `tests/client/test_client.py` and mock the `ZhipuaiProvider` in the test cases.
* Update `README.md` to mention ZhipuAI as a supported provider and provide an example of using the ZhipuAI provider.

---

For more details, open the [Copilot Workspace session](https://copilot-workspace.githubnext.com/andrewyng/aisuite?shareId=XXXX-XXXX-XXXX-XXXX).

README.md

@@ -7,7 +7,7 @@ Simple, unified interface to multiple Generative AI providers.
 `aisuite` makes it easy for developers to use multiple LLM through a standardized interface. Using an interface similar to OpenAI's, `aisuite` makes it easy to interact with the most popular LLMs and compare the results. It is a thin wrapper around python client libraries, and allows creators to seamlessly swap out and test responses from different LLM providers without changing their code. Today, the library is primarily focussed on chat completions. We will expand it cover more use cases in near future.
 
 Currently supported providers are -
-OpenAI, Anthropic, Azure, Google, AWS, Groq, Mistral, HuggingFace and Ollama.
+OpenAI, Anthropic, Azure, Google, AWS, Groq, Mistral, HuggingFace, Ollama, and ZhipuAI.
 To maximize stability, `aisuite` uses either the HTTP endpoint or the SDK for making calls to the provider.
 
 ## Installation
@@ -108,3 +108,26 @@ We follow a convention-based approach for loading providers, which relies on str
   in providers/openai_provider.py
 
 This convention simplifies the addition of new providers and ensures consistency across provider implementations.
+
+## Example of using the ZhipuAI provider
+
+Set the API key.
+```shell
+export ZHIPUAI_API_KEY="your-zhipuai-api-key"
+```
+
+Use the python client.
+```python
+from zhipuai import ZhipuAI
+client = ZhipuAI(api_key="your-zhipuai-api-key")
+response = client.chat.completions.create(
+    model="glm-4",
+    messages=[
+        {"role": "user", "content": "作为一名营销专家，请为智谱开放平台创作一个吸引人的slogan"},
+        {"role": "assistant", "content": "当然，为了创作一个吸引人的slogan，请告诉我一些关于您产品的信息"},
+        {"role": "user", "content": "智谱AI开放平台"},
+        {"role": "assistant", "content": "智启未来，谱绘无限一智谱AI，让创新触手可及!"},
+        {"role": "user", "content": "创造一个更精准、吸引人的slogan"}
+    ],
+)
+print(response.choices[0].message)

aisuite/providers/zhipuai_provider.py

@@ -0,0 +1,23 @@
+import zhipuai
+from aisuite.provider import Provider
+from aisuite.framework import ChatCompletionResponse
+
+
+class ZhipuaiProvider(Provider):
+    def __init__(self, **config):
+        """
+        Initialize the ZhipuAI provider with the given configuration.
+        Pass the entire configuration dictionary to the ZhipuAI client constructor.
+        """
+        self.client = zhipuai.ZhipuAI(**config)
+
+    def chat_completions_create(self, model, messages, **kwargs):
+        return self.normalize_response(
+            self.client.chat.completions.create(model=model, messages=messages, **kwargs)
+        )
+
+    def normalize_response(self, response):
+        """Normalize the response from the ZhipuAI API to match OpenAI's response format."""
+        normalized_response = ChatCompletionResponse()
+        normalized_response.choices[0].message.content = response.choices[0].message
+        return normalized_response

tests/client/test_client.py

@@ -16,6 +16,7 @@ class TestClient(unittest.TestCase):
     @patch(
         "aisuite.providers.fireworks_provider.FireworksProvider.chat_completions_create"
     )
+    @patch("aisuite.providers.zhipuai_provider.ZhipuaiProvider.chat_completions_create")
     def test_client_chat_completions(
         self,
         mock_fireworks,
@@ -26,6 +27,7 @@ def test_client_chat_completions(
         mock_openai,
         mock_groq,
         mock_mistral,
+        mock_zhipuai,
     ):
         # Mock responses from providers
         mock_openai.return_value = "OpenAI Response"
@@ -36,6 +38,7 @@ def test_client_chat_completions(
         mock_mistral.return_value = "Mistral Response"
         mock_google.return_value = "Google Response"
         mock_fireworks.return_value = "Fireworks Response"
+        mock_zhipuai.return_value = "ZhipuAI Response"
 
         # Provider configurations
         provider_configs = {
@@ -64,6 +67,9 @@ def test_client_chat_completions(
             "fireworks": {
                 "api_key": "fireworks-api-key",
             },
+            "zhipuai": {
+                "api_key": "zhipuai-api-key",
+            },
         }
 
         # Initialize the client
@@ -134,6 +140,14 @@ def test_client_chat_completions(
         self.assertEqual(fireworks_response, "Fireworks Response")
         mock_fireworks.assert_called_once()
 
+        # Test ZhipuAI model
+        zhipuai_model = "zhipuai" + ":" + "glm-4"
+        zhipuai_response = client.chat.completions.create(
+            zhipuai_model, messages=messages
+        )
+        self.assertEqual(zhipuai_response, "ZhipuAI Response")
+        mock_zhipuai.assert_called_once()
+
         # Test that new instances of Completion are not created each time we make an inference call.
         compl_instance = client.chat.completions
         next_compl_instance = client.chat.completions