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https://github.com/hansjone/oclaw.git
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oliver
6cfaff06f6
This preserves multimodal fidelity for the latest user turn while preventing historical/tool replay bloat, and adds admin UI support for referenced attachment preview/download flows. Made-with: Cursor
87 lines
3.2 KiB
Python
87 lines
3.2 KiB
Python
from __future__ import annotations
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from typing import Any
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_MIN_B64_CHARS = 200
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def ensure_no_tool_or_embedded_image_payload(*, messages: list[dict[str, Any]], path: str) -> None:
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"""Guard non-turn model paths and degrade in place instead of raising.
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- `role=tool` is downgraded to assistant text summary.
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- Embedded image/base64 payloads are replaced with safe text placeholders.
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"""
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for m in messages or []:
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if not isinstance(m, dict):
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continue
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role = str(m.get("role") or "").strip().lower()
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if role == "tool":
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m["role"] = "assistant"
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m["content"] = f"[model_path_audit:{path}] tool payload omitted"
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continue
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content = m.get("content")
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if _contains_embedded_image_payload(content):
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m["content"] = _sanitize_content(content, path=path)
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def _contains_embedded_image_payload(obj: Any) -> bool:
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if isinstance(obj, str):
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return _contains_large_base64_like_text(obj)
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if isinstance(obj, list):
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return any(_contains_embedded_image_payload(x) for x in obj)
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if not isinstance(obj, dict):
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return False
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typ = str(obj.get("type") or "").strip().lower()
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if typ in {"image", "input_image"}:
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for k in ("data", "image_base64"):
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v = obj.get(k)
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if isinstance(v, str) and len(v.strip()) >= _MIN_B64_CHARS:
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return True
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for v in obj.values():
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if _contains_embedded_image_payload(v):
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return True
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return False
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def _contains_large_base64_like_text(text: str) -> bool:
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s = str(text or "").strip()
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if len(s) < _MIN_B64_CHARS:
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return False
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if s.startswith("data:") and ";base64," in s:
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s = s.split(";base64,", 1)[1]
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head = s[: min(4096, len(s))]
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if len(head) < _MIN_B64_CHARS:
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return False
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allowed = frozenset("ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/=\n\r-_")
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noise = sum(1 for ch in head if ch not in allowed)
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# Similar heuristic to media redaction: mostly base64 alphabet over a long span.
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return noise <= max(4, len(head) // 200)
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def _sanitize_content(content: Any, *, path: str) -> Any:
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if isinstance(content, str):
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if _contains_large_base64_like_text(content):
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return f"[model_path_audit:{path}] base64 payload omitted"
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return content
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if isinstance(content, list):
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out: list[Any] = []
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for item in content:
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if isinstance(item, dict):
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typ = str(item.get("type") or "").strip().lower()
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if typ in {"image", "input_image"}:
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out.append({"type": "text", "text": f"[model_path_audit:{path}] image payload omitted"})
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continue
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out.append(_sanitize_content(item, path=path))
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return out
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if isinstance(content, dict):
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out: dict[str, Any] = {}
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for k, v in content.items():
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if str(k) in {"data", "image_base64"} and isinstance(v, str) and _contains_large_base64_like_text(v):
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out[k] = f"[model_path_audit:{path}] payload omitted"
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continue
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out[k] = _sanitize_content(v, path=path)
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return out
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return content
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__all__ = ["ensure_no_tool_or_embedded_image_payload"]
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