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Context-managed monkey patching for token monitoring (user-defined).
Users provide PatchSpec entries, each specifying:
- how to get the original callable (getter)
- how to set the wrapped callable (setter)
- which wrapper/decorator to apply (wrapper)
Example (OpenAI legacy endpoint patchable):
import openai
from memory.token_monitor import token_monitor
from memory.monkey_patch import MonkeyPatcher, PatchSpec, make_attr_patch
# Patch openai.ChatCompletion.create (legacy, class attribute is patchable)
getter, setter = make_attr_patch(openai.ChatCompletion, "create")
spec = PatchSpec(
name="openai.ChatCompletion.create",
getter=getter,
setter=setter,
wrapper=token_monitor(
extract_model_name=lambda *a, **k: (k.get("model", "gpt-3.5-turbo"), {}),
extract_input_dict=lambda *a, **k: {"messages": k.get("messages", [])},
extract_output_dict=lambda r: {
"messages": [{"role": "assistant", "content": r.choices[0].message.content}]
},
),
)
with MonkeyPatcher([spec]):
# Calls to openai.ChatCompletion.create are now monitored
...
Example (LiteLLM patchable):
import litellm
from memory.token_monitor import token_monitor
from memory.monkey_patch import MonkeyPatcher, PatchSpec, make_attr_patch
getter, setter = make_attr_patch(litellm, "completion")
spec = PatchSpec(
name="litellm.completion",
getter=getter,
setter=setter,
wrapper=token_monitor(
extract_model_name=lambda *a, **k: (k.get("model", "gpt-3.5-turbo"), {}),
extract_input_dict=lambda *a, **k: {"messages": k.get("messages", [])},
extract_output_dict=lambda r: {
"messages": [{"role": "assistant", "content": r.choices[0].message.content}]
} if hasattr(r, "choices") else {"messages": str(r)},
),
)
with MonkeyPachter([spec]):
...
Notes:
- The new OpenAI client exposes `OpenAI.chat` via a cached_property; patching
`openai.OpenAI.chat.completions` at the class level will fail. Prefer patching
legacy endpoints (e.g., `openai.ChatCompletion.create`) or wrapping an instance
method at runtime if you manage the instance creation site.
"""
from __future__ import annotations
from dataclasses import dataclass
from pydantic import BaseModel
from typing import (
Callable,
Any,
List,
Dict,
Tuple,
)
@dataclass
class PatchSpec:
"""
A single patch rule.
- name: human-readable identifier (for debugging/restore logging)
- getter: returns the current callable to patch
- setter: sets the new callable in-place
- wrapper: a decorator (Callable[[Callable], Callable]) that wraps the target callable
"""
name: str
getter: Callable[[], Callable[..., Any]]
setter: Callable[[Callable[..., Any]], None]
wrapper: Callable[[Callable[..., Any]], Callable[..., Any]]
class MonkeyPatcher:
"""
Context manager to apply user-defined patch specs on enter and restore originals on exit.
"""
def __init__(self, specs: List[PatchSpec]) -> None:
self.specs = specs
self._originals: Dict[str, Callable[..., Any]] = {}
self._active = False
def __enter__(self) -> MonkeyPatcher:
if self._active:
return self
for spec in self.specs:
original = spec.getter()
wrapped = spec.wrapper(original)
spec.setter(wrapped)
self._originals[spec.name] = original
self._active = True
return self
def __exit__(self, exc_type, exc, tb) -> None:
# Best-effort restore in reverse order
for spec in reversed(self.specs):
if spec.name in self._originals:
try:
spec.setter(self._originals[spec.name])
except Exception:
pass
self._originals.clear()
self._active = False
return None
def make_attr_patch(obj: Any, attr: str) -> Tuple[Callable[[], Callable[..., Any]], Callable[[Callable[..., Any]], None]]:
"""
Helper to build (getter, setter) closures for a given object attribute.
Example:
getter, setter = make_attr_patch(openai.ChatCompletion, "create")
spec = PatchSpec(
name="openai.ChatCompletion.create",
getter=getter,
setter=setter,
wrapper=token_monitor(...),
)
"""
def getter() -> Callable[..., Any]:
return getattr(obj, attr)
if isinstance(obj, BaseModel):
def setter(fn: Callable[..., Any]) -> None:
object.__setattr__(obj, attr, fn)
else:
def setter(fn: Callable[..., Any]) -> None:
setattr(obj, attr, fn)
return getter, setter |