Added example for using unions of models, useful for agents

examples/let_the_llm_choose_a_tool.py

@@ -0,0 +1,50 @@
+from pydantic import BaseModel, Field
+from typing import Union
+import instructor
+from openai import OpenAI
+
+class Search(BaseModel):
+    """Search action class with a 'query' field and a process method."""
+    query: str = Field(description="The search query")
+
+    def process(self):
+        """Process the search action."""
+        return f"Search method called for query: {self.query}"
+
+class Lookup(BaseModel):
+    """Lookup action class with a 'keyword' field and a process method."""
+    keyword: str = Field(description="The lookup keyword")
+
+    def process(self):
+        """Process the lookup action."""
+        return f"Lookup method called for keyword: {self.keyword}"
+
+class Finish(BaseModel):
+    """Finish action class with an 'answer' field and a process method."""
+    answer: str = Field(description="The answer for finishing the process")
+
+    def process(self):
+        """Process the finish action."""
+        return f"Finish method called with answer: {self.answer}"
+
+# Union of Search, Lookup, and Finish
+class TakeAction(BaseModel):
+    action: Union[Search, Lookup, Finish]
+
+    def process(self):
+        """Process the action."""
+        return self.action.process()
+
+
+# Enables `response_model`
+client = instructor.patch(OpenAI())
+action = client.chat.completions.create(
+    model="gpt-3.5-turbo",
+    response_model=TakeAction,
+    messages=[
+        {"role": "user", "content": "Please choose one action"},
+    ]
+)
+assert isinstance(action, TakeAction)
+print(action.process())
+