bring back map for AIFunction

src/marvin/components/ai_function.py

@@ -1,6 +1,6 @@
+import asyncio
 import inspect
 import json
-from functools import partial, wraps
 from typing import (
     TYPE_CHECKING,
     Any,
@@ -18,6 +18,11 @@
 
 from marvin.components.prompt import PromptFunction
 from marvin.serializers import create_tool_from_type
+from marvin.utilities.asyncio import (
+    ExposeSyncMethodsMixin,
+    expose_sync_method,
+    run_async,
+)
 from marvin.utilities.jinja import (
     BaseEnvironment,
 )
@@ -30,7 +35,7 @@
 P = ParamSpec("P")
 
 
-class AIFunction(BaseModel, Generic[P, T]):
+class AIFunction(BaseModel, Generic[P, T], ExposeSyncMethodsMixin):
     fn: Optional[Callable[P, T]] = None
     environment: Optional[BaseEnvironment] = None
     prompt: Optional[str] = Field(default=inspect.cleandoc("""
@@ -57,14 +62,32 @@ class AIFunction(BaseModel, Generic[P, T]):
 
     create: Optional[Callable[..., "ChatCompletion"]] = Field(default=None)
 
-    def __call__(self, *args: P.args, **kwargs: P.kwargs) -> T:
-        create = self.create
+    def __call__(self, *args: P.args, **kwargs: P.kwargs) -> Union[T, Awaitable[T]]:
         if self.fn is None:
             raise NotImplementedError
-        if create is None:
-            from marvin.settings import settings
 
-            create = settings.openai.chat.completions.create
+        from marvin import settings
+
+        is_async_fn = asyncio.iscoroutinefunction(self.fn)
+
+        call = "async_call" if is_async_fn else "sync_call"
+        create = (
+            self.create or settings.openai.chat.completions.acreate
+            if is_async_fn
+            else settings.openai.chat.completions.create
+        )
+
+        return getattr(self, call)(create, *args, **kwargs)
+
+    async def async_call(
+        self, acreate: Callable[..., Awaitable[Any]], *args: P.args, **kwargs: P.kwargs
+    ) -> T:
+        _response = await acreate(**self.as_prompt(*args, **kwargs).serialize())
+        return self.parse(_response)
+
+    def sync_call(
+        self, create: Callable[..., Any], *args: P.args, **kwargs: P.kwargs
+    ) -> T:
         _response = create(**self.as_prompt(*args, **kwargs).serialize())
         return self.parse(_response)
 
@@ -93,6 +116,46 @@ def parse(self, response: "ChatCompletion") -> T:
             _arguments: str = json.dumps({self.field_name: json.loads(arguments)})
             return getattr(tool.model.model_validate_json(_arguments), self.field_name)
 
+    @expose_sync_method("map")
+    async def amap(self, *map_args: list[Any], **map_kwargs: list[Any]) -> list[T]:
+        """
+        Map the AI function over a sequence of arguments. Runs concurrently.
+
+        A `map` twin method is provided by the `expose_sync_method` decorator.
+
+        You can use `map` or `amap` synchronously or asynchronously, respectively,
+        regardless of whether the user function is synchronous or asynchronous.
+
+        Arguments should be provided as if calling the function normally, but
+        each argument must be a list. The function is called once for each item
+        in the list, and the results are returned in a list.
+
+        For example, fn.map([1, 2]) is equivalent to [fn(1), fn(2)].
+
+        fn.map([1, 2], x=['a', 'b']) is equivalent to [fn(1, x='a'), fn(2, x='b')].
+        """
+        tasks: list[Any] = []
+        if map_args and map_kwargs:
+            max_length = max(
+                len(arg) for arg in (map_args + tuple(map_kwargs.values()))
+            )
+        elif map_args:
+            max_length = max(len(arg) for arg in map_args)
+        else:
+            max_length = max(len(v) for v in map_kwargs.values())
+
+        for i in range(max_length):
+            call_args = [arg[i] if i < len(arg) else None for arg in map_args]
+            call_kwargs = (
+                {k: v[i] if i < len(v) else None for k, v in map_kwargs.items()}
+                if map_kwargs
+                else {}
+            )
+
+            tasks.append(run_async(self, *call_args, **call_kwargs))
+
+        return await asyncio.gather(*tasks)
+
     def as_prompt(
         self,
         *args: P.args,
@@ -153,33 +216,24 @@ def as_decorator(
         model_description: str = "Formats the response.",
         field_name: str = "data",
         field_description: str = "The data to format.",
-        acreate: Optional[Callable[..., Awaitable[Any]]] = None,
         **render_kwargs: Any,
-    ) -> Union[Self, Callable[[Callable[P, T]], Self]]:
-        if fn is None:
-            return partial(
-                cls,
+    ) -> Union[Callable[[Callable[P, T]], Self], Self]:
+        def decorator(func: Callable[P, T]) -> Self:
+            return cls(
+                fn=func,
                 environment=environment,
-                prompt=prompt,
-                model_name=model_name,
-                model_description=model_description,
+                name=model_name,
+                description=model_description,
                 field_name=field_name,
                 field_description=field_description,
-                acreate=acreate,
                 **({"prompt": prompt} if prompt else {}),
                 **render_kwargs,
             )
 
-        return cls(
-            fn=fn,
-            environment=environment,
-            name=model_name,
-            description=model_description,
-            field_name=field_name,
-            field_description=field_description,
-            **({"prompt": prompt} if prompt else {}),
-            **render_kwargs,
-        )
+        if fn is not None:
+            return decorator(fn)
+
+        return decorator
 
 
 @overload
@@ -221,23 +275,29 @@ def ai_fn(
     field_name: str = "data",
     field_description: str = "The data to format.",
     **render_kwargs: Any,
-) -> Union[Callable[[Callable[P, T]], Callable[P, T]], Callable[P, T],]:
-    def wrapper(func: Callable[P, T], *args: P.args, **kwargs: P.kwargs) -> T:
-        return AIFunction[P, T].as_decorator(
-            func,
+) -> Union[Callable[[Callable[P, T]], Callable[P, T]], Callable[P, T]]:
+    if fn is not None:
+        return AIFunction.as_decorator(  # type: ignore
+            fn=fn,
             environment=environment,
             prompt=prompt,
             model_name=model_name,
             model_description=model_description,
             field_name=field_name,
             field_description=field_description,
             **render_kwargs,
-        )(*args, **kwargs)
-
-    if fn is not None:
-        return wraps(fn)(partial(wrapper, fn))
+        )
 
-    def decorator(fn: Callable[P, T]) -> Callable[P, T]:
-        return wraps(fn)(partial(wrapper, fn))
+    def decorator(func: Callable[P, T]) -> Callable[P, T]:
+        return AIFunction.as_decorator(  # type: ignore
+            fn=func,
+            environment=environment,
+            prompt=prompt,
+            model_name=model_name,
+            model_description=model_description,
+            field_name=field_name,
+            field_description=field_description,
+            **render_kwargs,
+        )
 
     return decorator