Add a Function object

The `Function` object encapsulates a prompt template, a (`transformers`)
model name and an output structure. It can then simply be called with a
prompt throughout an application.

outlines/__init__.py

@@ -4,6 +4,7 @@
 import outlines.text.generate
 from outlines.base import vectorize
 from outlines.caching import clear_cache, disable_cache, get_cache
+from outlines.function import Function
 from outlines.prompts import prompt
 
 __all__ = [

outlines/function.py

@@ -0,0 +1,50 @@
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Callable, Optional, Union
+
+from outlines import generate, models
+
+if TYPE_CHECKING:
+    from outlines.generate.api import SequenceGenerator
+
+
+@dataclass
+class Function:
+    """Represents an Outlines function.
+
+    Functions are a convenient way to encapsulate a prompt template, a language
+    model and a Pydantic model that define the output structure. Once defined,
+    the function can be called with arguments that will be used to render the
+    prompt template.
+
+    """
+
+    prompt_template: Callable
+    model_name: str
+    schema: Union[str, Callable, object]
+    generator: Optional["SequenceGenerator"] = None
+
+    def init_generator(self):
+        """Load the model and initialize the generator."""
+        model = models.transformers(self.model_name)
+        self.generator = generate.json(model, self.schema)
+
+    def __call__(self, *args, **kwargs):
+        """Call the function.
+
+        .. warning::
+
+           This currently does not support batching.
+
+        Parameters
+        ----------
+        args
+            Values to pass to the prompt template as positional arguments.
+        kwargs
+            Values to pass to the prompt template as keyword arguments.
+
+        """
+        if self.generator is None:
+            self.init_generator()
+
+        prompt = self.prompt_template(*args, **kwargs)
+        return self.generator(prompt)

tests/test_function.py

@@ -0,0 +1,20 @@
+from pydantic import BaseModel
+
+import outlines
+from outlines.function import Function
+
+
+def test_function_basic():
+    @outlines.prompt
+    def test_template(text: str):
+        """{{ text }}"""
+
+    class Foo(BaseModel):
+        id: int
+
+    fn = Function(test_template, "hf-internal-testing/tiny-random-GPTJForCausalLM", Foo)
+
+    assert fn.generator is None
+
+    result = fn("test")
+    assert isinstance(result, BaseModel)