Refactor code structure and imports in simplemind package

simplemind/providers/__init__.py

@@ -1,3 +1,4 @@
 from .openai import OpenAI
+from .anthropic import Anthropic
 
-providers = [OpenAI]
+providers = [OpenAI, Anthropic]

simplemind/providers/anthropic.py

@@ -0,0 +1,60 @@
+import anthropic
+import instructor
+
+# from ..models import Conversation, Message
+from ..settings import settings
+
+PROVIDER_NAME = "anthropic"
+DEFAULT_MODEL = "claude-3-5-sonnet-20241022"
+DEFAULT_MAX_TOKENS = 1000
+
+
+class Anthropic:
+    __name__ = PROVIDER_NAME
+    DEFAULT_MODEL = DEFAULT_MODEL
+
+    def __init__(self, api_key: str = None):
+        self.api_key = api_key or settings.ANTHROPIC_API_KEY
+
+    @property
+    def client(self):
+        """The raw Anthropic client."""
+        return anthropic.Anthropic(api_key=self.api_key)
+
+    @property
+    def structured_client(self):
+        """A client patched with Instructor."""
+        return instructor.from_anthropic(anthropic.Anthropic(api_key=self.api_key))
+
+    def send_conversation(self, conversation: "Conversation"):
+        """Send a conversation to the Anthropic API."""
+        from ..models import Message
+
+        messages = [
+            {"role": msg.role, "content": msg.text} for msg in conversation.messages
+        ]
+
+        response = self.client.messages.create(
+            model=conversation.llm_model or DEFAULT_MODEL,
+            messages=messages,
+            max_tokens=DEFAULT_MAX_TOKENS,
+        )
+
+        # Get the response content from the OpenAI response
+        # assistant_message = response.choices[0].message
+        assistant_message = response.content[0].text
+
+        # Create and return a properly formatted Message instance
+        return Message(
+            role="assistant",
+            text=assistant_message,
+            raw=response,
+            llm_model=conversation.llm_model or DEFAULT_MODEL,
+            llm_provider=PROVIDER_NAME,
+        )
+
+    def structured_response(self, model, response_model, **kwargs):
+        response = self.structured_client.messages.create(
+            model=model, response_model=response_model, **kwargs
+        )
+        return response

t.py

@@ -1,7 +1,7 @@
 import simplemind as sm
 
 conversation = sm.create_conversation()
-conversation.add_message(role="user", text="Hello, how are you?")
-r = conversation.send(llm_model="gpt-4o-mini", llm_provider="openai")
+conversation.add_message(role="user", text="Hello, how are you? Do you like poetry?")
+r = conversation.send(llm_provider="anthropic")
 
-print(r)
+print(r.text)