MAINT: Bump the minor-and-patch group across 1 directory with 10 updates

pyproject.toml

@@ -132,12 +132,14 @@ speech = [
 
 litellm = [
     # Cap below 1.92.0: 1.92.0+ replaced the universal py3-none-any wheel with a
-    # Rust (PyO3) extension and only publishes manylinux wheels for CPython
-    # 3.10-3.13 (Linux only). On Windows, macOS, or Python 3.14 there is no
-    # matching wheel, so pip/uv falls back to building the Rust extension from the
-    # sdist, which fails (no Rust toolchain in CI; PyO3 <=3.13 has no 3.14 support).
-    # 1.91.x is the last line with a universal wheel that installs on every OS and
-    # Python version we support. Revisit once litellm ships win/mac/py3.14 wheels.
+    # Rust (PyO3) extension and only publishes manylinux_2_28 (x86_64/aarch64) and
+    # win_amd64 wheels. macOS (any arch), musl/Alpine, glibc <2.28, and Windows
+    # ARM64 have no matching wheel, so pip/uv falls back to building the Rust
+    # extension from the sdist, which fails without a Rust toolchain. Environment
+    # markers cannot express libc flavor or glibc version, so the affected
+    # platforms cannot be pinned back selectively. 1.91.x is the last line with a
+    # universal wheel that installs everywhere. Revisit once litellm ships macOS
+    # and musllinux wheels.
     "litellm>=1.83.0,<1.92.0",
 ]
 
@@ -149,7 +151,7 @@ all = [
     "flask>=3.1.3",
     "ipykernel>=6.29.5",
     "jupyter>=1.1.1",
-    "litellm>=1.83.0,<1.92.0",  # <1.92.0: 1.92.0+ ships Linux-only Rust wheels (no win/mac/py3.14); see litellm group above
+    "litellm>=1.83.0,<1.92.0",  # 1.92.0+ drops the universal wheel (no mac/musl/old-glibc/win-arm64); see litellm group
     "ollama>=0.5.1",
     "opencv-python>=4.11.0.86",
     "playwright>=1.49.0",

pyrit/memory/memory_models.py

@@ -1893,9 +1893,8 @@ def __init__(self, *, entry: ScenarioResult) -> None:
 
         # Convert ComponentIdentifier to dict for JSON storage
         target_identifier = entry.objective_target_identifier
-        self.objective_target_identifier = (  # type: ignore[ty:invalid-assignment]
-            target_identifier.model_dump() if target_identifier else None
-        )
+        target_identifier_dict = target_identifier.model_dump() if target_identifier else None
+        self.objective_target_identifier = target_identifier_dict  # type: ignore[ty:invalid-assignment]
         # Always recompute eval_hash before dumping so the stored JSON carries the
         # freshly computed value for DB-level filtering (never a value from storage).
         scorer_identifier = entry.objective_scorer_identifier