feat(types): add is_given TypeGuard helpers for NotGiven sentinels

Pyright can't narrow identity checks against module-level NotGiven
sentinels (they aren't typed as singletons), which leaves many
NotGiven-bearing unions stuck as unnarrowed types throughout the
codebase. Introduce is_given TypeGuard helpers so narrowing works via
isinstance under the hood.

Each helper is co-located with the NotGiven flavor it guards:

- services/settings.py: upgrade the existing is_given to a TypeGuard.
- processors/aggregators/llm_context.py: add an is_given for
  LLMContext's NotGiven. Treat LLMContext's re-exported types
  (LLMStandardMessage, LLMContextToolChoice, NOT_GIVEN, NotGiven) as
  LLMContext's own — independent definitions that happen to coincide
  with OpenAI's as an implementation detail.
- adapters/services/anthropic_adapter.py: add is_given for anthropic's
  NotGiven.
- adapters/services/open_ai_adapter.py: add is_given for openai's
  NotGiven.

src/pipecat/adapters/services/anthropic_adapter.py

@@ -9,7 +9,7 @@
 import copy
 import json
 from dataclasses import dataclass
-from typing import Any, TypedDict
+from typing import Any, TypedDict, TypeGuard, TypeVar
 
 from anthropic import NOT_GIVEN, NotGiven
 from anthropic.types.message_param import MessageParam
@@ -26,6 +26,29 @@ from pipecat.processors.aggregators.llm_context import (
     LLMStandardMessage,
 )
 
+_T = TypeVar("_T")
+
+
+def is_given(value: _T | NotGiven) -> TypeGuard[_T]:
+    """Check whether a value was explicitly provided.
+
+    Typically used when checking whether a parameter or field typed with
+    Anthropic's ``NotGiven`` was set::
+
+        if is_given(system):
+            ...
+
+    Also acts as a type guard: inside a true branch, the value is narrowed
+    to exclude ``NotGiven`` (e.g. ``str | NotGiven`` becomes ``str``).
+
+    Args:
+        value: The value to check.
+
+    Returns:
+        ``True`` if *value* is anything other than ``NOT_GIVEN``.
+    """
+    return not isinstance(value, NotGiven)
+
 
 class AnthropicLLMInvocationParams(TypedDict):
     """Context-based parameters for invoking Anthropic's LLM API."""

src/pipecat/adapters/services/open_ai_adapter.py

@@ -6,7 +6,7 @@
 
 """OpenAI LLM adapter for Pipecat."""
 
-from typing import Any, TypedDict
+from typing import Any, TypedDict, TypeGuard, TypeVar
 
 from openai._types import NotGiven as OpenAINotGiven
 from openai.types.chat import (
@@ -25,6 +25,31 @@ from pipecat.processors.aggregators.llm_context import (
     NotGiven,
 )
 
+_T = TypeVar("_T")
+
+
+def is_given(value: _T | OpenAINotGiven) -> TypeGuard[_T]:
+    """Check whether a value was explicitly provided.
+
+    Typically used when checking whether a parameter or field typed with
+    OpenAI's ``NotGiven`` was set::
+
+        if is_given(tool_choice):
+            ...
+
+    Also acts as a type guard: inside a true branch, the value is narrowed
+    to exclude ``OpenAINotGiven`` (e.g.
+    ``ChatCompletionToolChoiceOptionParam | OpenAINotGiven`` becomes
+    ``ChatCompletionToolChoiceOptionParam``).
+
+    Args:
+        value: The value to check.
+
+    Returns:
+        ``True`` if *value* is anything other than ``NOT_GIVEN``.
+    """
+    return not isinstance(value, OpenAINotGiven)
+
 
 class OpenAILLMInvocationParams(TypedDict):
     """Context-based parameters for invoking OpenAI ChatCompletion API."""

src/pipecat/processors/aggregators/llm_context.py

@@ -21,7 +21,7 @@ import io
 import wave
 from collections.abc import Callable
 from dataclasses import dataclass
-from typing import Any, TypeAlias
+from typing import Any, TypeAlias, TypeGuard, TypeVar
 
 from loguru import logger
 from openai._types import NOT_GIVEN as OPEN_AI_NOT_GIVEN
@@ -36,16 +36,45 @@ from pipecat.adapters.schemas.tools_schema import ToolsSchema
 from pipecat.frames.frames import AudioRawFrame
 
 # "Re-export" types from OpenAI that we're using as universal context types.
-# NOTE: if universal message types need to someday diverge from OpenAI's, we
-# should consider managing our own definitions. But we should do so carefully,
-# as the OpenAI messages are somewhat of a standard and we want to continue
-# supporting them.
+# NOTE: these are aliased to OpenAI's today, but callers should treat them as
+# LLMContext's own types — independent definitions that happen to coincide
+# with OpenAI's as an implementation detail. If universal context types need
+# to someday diverge from OpenAI's, we should consider managing our own
+# definitions (but with care, since OpenAI's types are somewhat of a standard
+# and we want to continue supporting them). In the meantime, code at the
+# LLMContext/OpenAI boundary should use explicit casts rather than rely on
+# the aliasing.
 LLMStandardMessage = ChatCompletionMessageParam
 LLMContextToolChoice = ChatCompletionToolChoiceOptionParam
 NOT_GIVEN = OPEN_AI_NOT_GIVEN
 NotGiven = OpenAINotGiven
 
 
+_T = TypeVar("_T")
+
+
+def is_given(value: _T | NotGiven) -> TypeGuard[_T]:
+    """Check whether a value was explicitly provided.
+
+    Typically used when checking whether a ``NotGiven``-valued field or
+    parameter was set::
+
+        if is_given(context.tools):
+            ...
+
+    Also acts as a type guard: inside a true branch, the value is narrowed
+    to exclude ``NotGiven`` (e.g. ``ToolsSchema | NotGiven`` becomes
+    ``ToolsSchema``).
+
+    Args:
+        value: The value to check.
+
+    Returns:
+        ``True`` if *value* is anything other than ``NOT_GIVEN``.
+    """
+    return not isinstance(value, NotGiven)
+
+
 @dataclass
 class LLMSpecificMessage:
     """A container for a context message that is specific to a particular LLM service.

src/pipecat/services/settings.py

@@ -39,7 +39,7 @@ from __future__ import annotations
 import copy
 from collections.abc import Mapping
 from dataclasses import dataclass, field, fields
-from typing import TYPE_CHECKING, Any, ClassVar, TypeVar
+from typing import TYPE_CHECKING, Any, ClassVar, TypeGuard, TypeVar
 
 from loguru import logger
 
@@ -88,7 +88,10 @@ Valid only in delta-mode settings objects.  Must never appear in a service's
 """
 
 
-def is_given(value: Any) -> bool:
+_T = TypeVar("_T")
+
+
+def is_given(value: _T | _NotGiven) -> TypeGuard[_T]:
     """Check whether a delta field was explicitly provided.
 
     Typically used when processing a delta to decide whether a field
@@ -98,6 +101,10 @@ def is_given(value: Any) -> bool:
             # caller wants to change the voice
             ...
 
+    Also acts as a type guard: inside a true branch, the value is narrowed
+    to exclude ``_NotGiven`` (e.g. ``str | None | _NotGiven`` becomes
+    ``str | None``).
+
     For store-mode objects this always returns ``True`` (since
     ``validate_complete`` ensures no ``NOT_GIVEN`` fields remain).