add simple litellm example

examples/single_litellm_example.py

@@ -0,0 +1,48 @@
+from typing import List
+from pydantic import BaseModel, Field
+from bespokelabs import curator
+from datasets import Dataset
+
+# Define response format using Pydantic
+class Recipe(BaseModel):
+    title: str = Field(description="Title of the recipe")
+    ingredients: List[str] = Field(description="List of ingredients needed")
+    instructions: List[str] = Field(description="Step by step cooking instructions")
+
+def prompt_func(cuisine_type):
+    return f"Generate a random {cuisine_type} recipe. Be creative but keep it realistic."
+
+def parse_func(row, response):
+    return {
+        "title": response.title,
+        "ingredients": response.ingredients,
+        "instructions": response.instructions,
+        "cuisine": row["cuisine"]  # Keep track of cuisine type
+    }
+
+def main():
+    # List of cuisines to generate recipes for
+    cuisines = ["Chinese"] * 1001
+
+    # Create input dataset with cuisine types
+    input_data = [{"cuisine": cuisine} for cuisine in cuisines]
+    input_dataset = Dataset.from_list(input_data)
+
+    # Create prompter using LiteLLM backend
+    recipe_prompter = curator.Prompter(
+        model_name="gpt-4o-mini",
+        prompt_func=prompt_func,
+        parse_func=parse_func,
+        response_format=Recipe,
+        backend="litellm",
+    )
+
+    # Generate recipes for all cuisines
+    recipes = recipe_prompter(input_dataset)
+
+    # Print results
+    df = recipes.to_pandas()
+    print(df)
+
+if __name__ == "__main__":
+    main()