Bind positional-only arguments correctly in direct and HTTP calls

great_ai/deploy/routes/bootstrap_prediction_endpoint.py

@@ -16,6 +16,11 @@ def bootstrap_prediction_endpoint(
     )
 
     schema = _get_schema(func)
+    positional_names = [
+        p.name
+        for p in inspect.signature(func).parameters.values()
+        if p.kind == p.POSITIONAL_ONLY and p.name in schema.model_fields
+    ]
 
     @router.post("/predict", status_code=status.HTTP_200_OK, response_model=Trace)
     async def predict(input_value: schema) -> Trace:  # type: ignore
@@ -25,9 +30,12 @@ def bootstrap_prediction_endpoint(
             arguments = {
                 name: getattr(input_value, name) for name in schema.model_fields
             }
+            positional = [arguments.pop(name) for name in positional_names]
             if inspect.iscoroutinefunction(func):
-                return await cast(Callable[..., Awaitable[Trace]], func)(**arguments)
-            return cast(Callable[..., Trace], func)(**arguments)
+                return await cast(Callable[..., Awaitable[Trace]], func)(
+                    *positional, **arguments
+                )
+            return cast(Callable[..., Trace], func)(*positional, **arguments)
         except Exception as e:
             raise HTTPException(
                 status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,

great_ai/helper/get_arguments.py

@@ -9,16 +9,7 @@ def get_arguments(
 
     signature = inspect.signature(func)
 
-    defaults = {
-        p.name: p.default
-        for p in signature.parameters.values()
-        if p.default != inspect._empty
-    }
-
-    filter_keys = [
-        param.name
-        for param in signature.parameters.values()
-        if param.kind == param.POSITIONAL_OR_KEYWORD
-    ]
-
-    return {**defaults, **dict(zip(filter_keys, args)), **kwargs}
+    # Model-injected parameters may not have been supplied at this wrapper layer.
+    arguments = signature.bind_partial(*args, **kwargs)
+    arguments.apply_defaults()
+    return dict(arguments.arguments)

great_ai/parameters/automatically_decorate_parameters.py

@@ -12,7 +12,8 @@ def automatically_decorate_parameters(func: F) -> F:
     parameter_names = [
         param.name
         for param in signature.parameters.values()
-        if param.kind in (param.POSITIONAL_OR_KEYWORD, param.KEYWORD_ONLY)
+        if param.kind
+        in (param.POSITIONAL_ONLY, param.POSITIONAL_OR_KEYWORD, param.KEYWORD_ONLY)
     ]
 
     metadata = get_function_metadata_store(func)

tests/test_argument_regressions.py

@@ -17,3 +17,18 @@ def test_keyword_only_inputs_are_validated_and_exposed(isolated_context: None) -
     with TestClient(predict.app) as client:
         assert client.post("/predict", json={"value": 8}).json()["output"] == 8
         assert client.post("/predict", json={"value": "wrong"}).status_code == 422
+
+
+def test_positional_only_inputs_keep_their_bindings(isolated_context: None) -> None:
+    @GreatAI.create
+    def predict(first: str, /, second: int = 7) -> str:
+        return first * second
+
+    trace = predict("a", 2)
+    assert trace.output == "aa"
+    assert trace.logged_values["arg:first:value"] == "a"
+    assert trace.logged_values["arg:second:value"] == 2
+    with TestClient(predict.app) as client:
+        response = client.post("/predict", json={"first": "b", "second": 3})
+        assert response.status_code == 200
+        assert response.json()["output"] == "bbb"