Refactor message parameter in Anthropic provider

simplemind/providers/groq.py

@@ -72,6 +72,7 @@ class Groq(BaseProvider):
         response = self.structured_client.chat.completions.create(
             messages=messages,
             response_model=response_model,
+            model=kwargs.pop("llm_model", self.DEFAULT_MODEL),
             **kwargs,
         )
         return response

simplemind/providers/ollama.py

@@ -65,7 +65,12 @@ class Ollama(BaseProvider):
         )
 
     def structured_response(
-        self, prompt: str, response_model: Type[T], *, llm_model: str, **kwargs
+        self,
+        prompt: str,
+        response_model: Type[T],
+        *,
+        llm_model: str | None = None,
+        **kwargs,
     ) -> T:
         """Get a structured response from the Ollama API."""
         messages = [
@@ -80,7 +85,7 @@ class Ollama(BaseProvider):
         )
         return response
 
-    def generate_text(self, prompt: str, *, llm_model: str) -> str:
+    def generate_text(self, prompt: str, *, llm_model: str | None = None) -> str:
         """Generate text using the Ollama API."""
         messages = [
             {"role": "user", "content": prompt},

tests/test_generate_data.py

@@ -0,0 +1,28 @@
+import pytest
+
+from simplemind.providers import Anthropic, Gemini, OpenAI, Groq, Ollama
+from pydantic import BaseModel
+
+
+class ResponseModel(BaseModel):
+    result: int
+
+
+@pytest.mark.parametrize(
+    "provider_cls",
+    [
+        Anthropic,
+        Gemini,
+        OpenAI,
+        Groq,
+        Ollama,
+    ],
+)
+def test_generate_data(provider_cls):
+    provider = provider_cls()
+    prompt = "What is 2+2?"
+
+    data = provider.structured_response(prompt=prompt, response_model=ResponseModel)
+
+    assert isinstance(data, ResponseModel)
+    assert type(data.result) == int