fix(openai_responses): OpenAIResponsesObject is not complete

llama_stack/apis/agents/openai_responses.py

@@ -4,7 +4,7 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from typing import Annotated, Any, Literal, Optional, TypeAlias, Union
+from typing import Annotated, Any, Literal, Optional
 
 from pydantic import BaseModel, Field
 from typing_extensions import TypedDict
@@ -14,12 +14,16 @@ from llama_stack.apis.tools.openai_tool_choice import (
     ToolChoiceCustom,
     ToolChoiceFunction,
     ToolChoiceMcp,
-    ToolChoiceTypes
+    ToolChoiceTypes,
 )
 from llama_stack.apis.vector_io import SearchRankingOptions as FileSearchRankingOptions
 from llama_stack.schema_utils import json_schema_type, register_schema
 
-OpenAIResponsesToolChoice: TypeAlias = Union[ToolChoiceTypes, ToolChoiceAllowed, ToolChoiceFunction, ToolChoiceMcp, ToolChoiceCustom]
+type OpenAIResponsesToolChoice = (
+    ToolChoiceTypes | ToolChoiceAllowed | ToolChoiceFunction | ToolChoiceMcp | ToolChoiceCustom
+)
+register_schema(OpenAIResponsesToolChoice, name="OpenAIResponsesToolChoice")
+
 
 @json_schema_type
 class OpenAIResponseError(BaseModel):
@@ -346,8 +350,8 @@ class OpenAIResponsePrompt(BaseModel):
     """
 
     id: str
-    variables: Optional[dict[str, Any]] = None
-    version: Optional[str] = None
+    variables: dict[str, Any] | None = None
+    version: str | None = None
 
 
 @json_schema_type
@@ -358,26 +362,26 @@ class OpenAIResponseReasoning(BaseModel):
     :param generate_summary: Deprecated. Use the generate_summary_text field instead. (Optional) Whether to generate a summary of the reasoning process.
     """
 
-    effort: Optional[Literal["low", "medium", "high", "minimal"]] = None
-    generate_summary: Optional[str] = None 
-    summary: Optional[str] = None
+    effort: Literal["low", "medium", "high", "minimal"] | None = None
+    generate_summary: str | None = None
+    summary: str | None = None
 
 
 @json_schema_type
 class OpenAIResponsesTool(BaseModel):
-    description: Optional[str] = None
+    description: str | None = None
     """
     The description of the function, including guidance on when and how to call it,
     and guidance about what to tell the user when calling (if anything).
     """
 
-    name: Optional[str] = None
+    name: str | None = None
     """The name of the function."""
 
-    parameters: Optional[object] = None
+    parameters: object | None = None
     """Parameters of the function in JSON Schema."""
 
-    type: Optional[Literal["function"]] = None
+    type: Literal["function"] | None = None
     """The type of the tool, i.e. `function`."""
 
 
@@ -414,7 +418,7 @@ class OpenAIResponseObject(BaseModel):
     """
 
     created_at: int
-    error: Optional[OpenAIResponseError] = None
+    error: OpenAIResponseError | None = None
     id: str
     incomplete_details: Optional[OpenAIResponseIncompleteDetails] = None
     instructions: Optional[str | list[str]] = None

llama_stack/apis/tools/openai_tool_choice.py

@@ -1,10 +1,20 @@
-from typing import Dict, List, Literal, Optional, TypeAlias
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import Literal
 
 from pydantic import BaseModel
 
-ToolChoiceOptions: TypeAlias = Literal["none", "auto", "required"]
+from llama_stack.schema_utils import json_schema_type, register_schema
+
+type ToolChoiceOptions = Literal["none", "auto", "required"]
+register_schema(ToolChoiceOptions, name="ToolChoiceOptions")
 
 
+@json_schema_type
 class ToolChoiceTypes(BaseModel):
     type: Literal[
         "file_search",
@@ -26,6 +36,7 @@ class ToolChoiceTypes(BaseModel):
     """
 
 
+@json_schema_type
 class ToolChoiceAllowed(BaseModel):
     mode: Literal["auto", "required"]
     """Constrains the tools available to the model to a pre-defined set.
@@ -36,7 +47,7 @@ class ToolChoiceAllowed(BaseModel):
     `required` requires the model to call one or more of the allowed tools.
     """
 
-    tools: List[Dict[str, object]]
+    tools: list[dict[str, object]]
     """A list of tool definitions that the model should be allowed to call.
 
     For the Responses API, the list of tool definitions might look like:
@@ -54,6 +65,7 @@ class ToolChoiceAllowed(BaseModel):
     """Allowed tool configuration type. Always `allowed_tools`."""
 
 
+@json_schema_type
 class ToolChoiceFunction(BaseModel):
     name: str
     """The name of the function to call."""
@@ -62,6 +74,7 @@ class ToolChoiceFunction(BaseModel):
     """For function calling, the type is always `function`."""
 
 
+@json_schema_type
 class ToolChoiceMcp(BaseModel):
     server_label: str
     """The label of the MCP server to use."""
@@ -69,10 +82,11 @@ class ToolChoiceMcp(BaseModel):
     type: Literal["mcp"]
     """For MCP tools, the type is always `mcp`."""
 
-    name: Optional[str] = None
+    name: str | None = None
     """The name of the tool to call on the server."""
 
 
+@json_schema_type
 class ToolChoiceCustom(BaseModel):
     name: str
     """The name of the custom tool to call."""

llama_stack/strong_typing/schema.py

@@ -93,7 +93,14 @@ def get_class_property_docstrings(
     """
 
     result = {}
-    for base in inspect.getmro(data_type):
+    # Check if the type has __mro__ (method resolution order)
+    if hasattr(data_type, "__mro__"):
+        bases = inspect.getmro(data_type)
+    else:
+        # For TypeAliasType or other types without __mro__, just use the type itself
+        bases = [data_type] if hasattr(data_type, "__doc__") else []
+
+    for base in bases:
         docstr = docstring.parse_type(base)
         for param in docstr.params.values():
             if param.name in result:
@@ -479,12 +486,25 @@ class JsonSchemaGenerator:
                 }
             return ret
         elif origin_type is Literal:
-            if len(typing.get_args(typ)) != 1:
-                raise ValueError(f"Literal type {typ} has {len(typing.get_args(typ))} arguments")
-            (literal_value,) = typing.get_args(typ)  # unpack value of literal type
+            literal_values = typing.get_args(typ)
+            if len(literal_values) == 1:
+                # Single literal value - use const
+                (literal_value,) = literal_values
                 schema = self.type_to_schema(type(literal_value))
                 schema["const"] = literal_value
                 return schema
+            else:
+                # Multiple literal values - use enum
+                # Check that all literal values have the same type
+                literal_types = {type(value) for value in literal_values}
+                if len(literal_types) != 1:
+                    raise ValueError(f"Literal type {typ} has inconsistent value types: {literal_types}")
+
+                # Create schema based on the common type of all literal values
+                common_type = literal_types.pop()
+                schema = self.type_to_schema(common_type)
+                schema["enum"] = list(literal_values)
+                return schema
         elif origin_type is type:
             (concrete_type,) = typing.get_args(typ)  # unpack single tuple element
             return {"const": self.type_to_schema(concrete_type, force_expand=True)}
@@ -492,13 +512,24 @@ class JsonSchemaGenerator:
             (concrete_type,) = typing.get_args(typ)
             return self.type_to_schema(concrete_type)
 
+        # Check if this is a TypeAliasType (Python 3.12+) which doesn't have __mro__
+        if hasattr(typ, "__mro__"):
             # dictionary of class attributes
             members = dict(inspect.getmembers(typ, lambda a: not inspect.isroutine(a)))
-
             property_docstrings = get_class_property_docstrings(typ, self.options.property_description_fun)
+        else:
+            # TypeAliasType or other types without __mro__
+            members = {}
+            property_docstrings = {}
         properties: Dict[str, Schema] = {}
         required: List[str] = []
-        for property_name, property_type in get_class_properties(typ):
+        # Only process properties if the type supports class properties
+        if hasattr(typ, "__mro__"):
+            class_properties = get_class_properties(typ)
+        else:
+            class_properties = []
+
+        for property_name, property_type in class_properties:
             # rename property if an alias name is specified
             alias = get_annotation(property_type, Alias)
             if alias: