fix openai

simplemind/providers/openai.py

@@ -1,8 +1,7 @@
 from functools import cached_property
-from typing import TYPE_CHECKING, Iterator, Type, TypeVar
+from typing import TYPE_CHECKING, Callable, Iterator, Type, TypeVar
 
 import instructor
-from google.generativeai.responder import Callable
 from pydantic import BaseModel
 
 from ..logging import logger
@@ -117,61 +116,64 @@ class OpenAI(BaseProvider):
         """A OpenAI client with Instructor."""
         return instructor.from_openai(self.client)
 
-
-@logger
-def send_conversation(
-    self,
-    conversation: "Conversation",
-    tools: list[Callable | BaseTool] | None = None,
-    **kwargs,
-) -> "Message":
-    """Send a conversation to the OpenAI API."""
-    from ..models import Message
-
-    # Format messages from conversation
-    formatted_messages = [
-        {"role": msg.role, "content": msg.text} for msg in conversation.messages
-    ]
-
-    # Set up tools if provided
-    converted_tools = self.make_tools(tools)
-    tools_config = (
-        [t.get_input_schema() for t in converted_tools] if tools else None
-    )
-
-    # Merge all kwargs
-    request_kwargs = {
-        **self.DEFAULT_KWARGS,
+    @logger
+    def send_conversation(
+        self,
+        conversation: "Conversation",
+        tools: list[Callable | BaseTool] | None = None,
         **kwargs,
-        "model": conversation.llm_model or self.DEFAULT_MODEL,
-        "messages": formatted_messages,
-    }
+    ) -> "Message":
+        """Send a conversation to the OpenAI API."""
+        from ..models import Message
 
-    if tools_config:
-        request_kwargs["tools"] = tools_config
+        # Format messages from conversation
+        formatted_messages = [
+            {"role": msg.role, "content": msg.text}
+            for msg in conversation.messages
+        ]
 
-    # Make initial API call
-    response = self.client.chat.completions.create(**request_kwargs)
+        # Set up tools if provided
+        converted_tools = self.make_tools(tools)
+        tools_config = (
+            [t.get_input_schema() for t in converted_tools] if tools else None
+        )
 
-    # Handle tool responses if needed
-    while response.choices[0].message.tool_calls:
-        # Handle each tool call
-        for tool in converted_tools:
-            tool.handle(response, formatted_messages)
-            if tool.is_executed():
-                # Make another API call with the updated messages
-                response = self.client.chat.completions.create(**request_kwargs)
-                tool.reset_result()
+        # Merge all kwargs
+        request_kwargs = {
+            **self.DEFAULT_KWARGS,
+            **kwargs,
+            "model": conversation.llm_model or self.DEFAULT_MODEL,
+            "messages": formatted_messages,
+        }
 
-    final_message = response.choices[0].message.content
+        if tools_config:
+            request_kwargs["tools"] = tools_config
 
-    return Message(
-        role="assistant",
-        text=final_message or "",
-        raw=response,
-        llm_model=conversation.llm_model or self.DEFAULT_MODEL,
-        llm_provider=self.NAME,
-    )
+        # Make initial API call
+        response = self.client.chat.completions.create(**request_kwargs)
+
+        # Handle tool responses if needed
+        while response.choices[0].message.tool_calls:
+            print(response)
+            # Handle each tool call
+            for tool in converted_tools:
+                tool.handle(response, formatted_messages)
+                if tool.is_executed():
+                    # Make another API call with the updated messages
+                    response = self.client.chat.completions.create(
+                        **request_kwargs
+                    )
+                    tool.reset_result()
+
+        final_message = response.choices[0].message.content
+
+        return Message(
+            role="assistant",
+            text=final_message or "",
+            raw=response,
+            llm_model=conversation.llm_model or self.DEFAULT_MODEL,
+            llm_provider=self.NAME,
+        )
 
     @logger
     def structured_response(
@@ -231,3 +233,8 @@ def send_conversation(
         for chunk in response:
             if chunk.choices[0].delta.content is not None:
                 yield chunk.choices[0].delta.content
+
+    @cached_property
+    def tool(self) -> Type[BaseTool]:
+        """The tool implementation for OpenAI."""
+        return OpenAITool

tests/test_tools.py

@@ -4,12 +4,12 @@ import pytest
 from pydantic import Field
 
 import simplemind as sm
-from simplemind.providers import Anthropic, BaseTool
+from simplemind.providers import Anthropic, BaseTool, OpenAI
 
 MODELS = [
     Anthropic,
     # Gemini,
-    # OpenAI,
+    OpenAI,
     # Groq,
     # Ollama,
     # Amazon