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https://github.com/hansjone/oclaw.git
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oliver
ae44cbcad5
补充历史压缩与会话诊断链路，强化 tool pairing 与空响应兜底观测；完善 Admin MCP 导入/绑定与相关前端展示；新增 web_search_fast/web_fetch_clean 及多项测试与文档更新，并同步技能安装与角色绑定策略改进。 Made-with: Cursor
105 lines
4 KiB
Python
105 lines
4 KiB
Python
from __future__ import annotations
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from dataclasses import dataclass
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from typing import Any
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@dataclass(frozen=True)
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class SessionContextStats:
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session_id: str
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total_messages: int
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sampled_messages: int
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last_n: int
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last_n_total_chars: int
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last_n_tool_total_chars: int
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last_n_max_msg_chars: int
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last_n_max_tool_chars: int
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last_n_max_user_chars: int
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empty_assistant_text_in_sampled: int
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empty_assistant_text_ids: tuple[int, ...]
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max_content_chars_in_sampled: int
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max_tool_chars_in_sampled: int
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def compute_session_context_stats(
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*,
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store: Any,
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session_id: str,
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sample_n: int = 120,
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last_n: int = 80,
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) -> SessionContextStats:
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"""Compute lightweight DB-backed stats for diagnosing context overflow vs empty responses.
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This intentionally only inspects DB text lengths (not token counts).
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"""
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sid = str(session_id or "").strip()
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if not sid:
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raise ValueError("session_id required")
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sample_n = max(1, min(int(sample_n or 120), 2000))
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last_n = max(1, min(int(last_n or 80), 2000))
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total = 0
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sampled_rows: list[tuple[int, str, str, int]] = []
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last_rows: list[tuple[int, str, str, int]] = []
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try:
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# Prefer raw SQL to avoid store-level object hydration overhead.
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with store._connect() as conn: # noqa: SLF001
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cur = conn.execute("select count(1) from chat_message where session_id=?", (sid,))
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total = int((cur.fetchone() or [0])[0] or 0)
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cur = conn.execute(
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"select id, role, event_type, coalesce(length(content),0) as n "
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"from chat_message where session_id=? order by id desc limit ?",
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(sid, int(sample_n)),
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)
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sampled_rows = [(int(r[0]), str(r[1] or ""), str(r[2] or ""), int(r[3] or 0)) for r in (cur.fetchall() or [])]
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cur = conn.execute(
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"select id, role, event_type, coalesce(length(content),0) as n "
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"from chat_message where session_id=? order by id desc limit ?",
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(sid, int(last_n)),
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)
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last_rows = [(int(r[0]), str(r[1] or ""), str(r[2] or ""), int(r[3] or 0)) for r in (cur.fetchall() or [])]
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except Exception:
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# Fallback path via store API if direct SQL fails for any reason.
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msgs = list(store.get_messages(session_id=sid, limit=int(sample_n)))
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total = len(msgs)
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sampled_rows = [
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(int(getattr(m, "id", 0) or 0), str(getattr(m, "role", "") or ""), str(getattr(m, "event_type", "") or ""), len(str(getattr(m, "content", "") or "")))
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for m in msgs
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]
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last_rows = sampled_rows[: int(last_n)]
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empty_ids = tuple(sorted([mid for mid, role, ev, n in sampled_rows if role == "assistant" and ev == "assistant_text" and int(n) == 0]))
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max_content = max([n for *_rest, n in sampled_rows] or [0])
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max_tool = max([n for _mid, role, _ev, n in sampled_rows if role == "tool"] or [0])
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last_total = sum(int(n) for *_rest, n in last_rows)
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last_tool_total = sum(int(n) for _mid, role, _ev, n in last_rows if role == "tool")
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last_max = max([int(n) for *_rest, n in last_rows] or [0])
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last_tool_max = max([int(n) for _mid, role, _ev, n in last_rows if role == "tool"] or [0])
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last_user_max = max([int(n) for _mid, role, _ev, n in last_rows if role == "user"] or [0])
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return SessionContextStats(
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session_id=sid,
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total_messages=int(total),
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sampled_messages=len(sampled_rows),
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last_n=int(last_n),
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last_n_total_chars=int(last_total),
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last_n_tool_total_chars=int(last_tool_total),
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last_n_max_msg_chars=int(last_max),
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last_n_max_tool_chars=int(last_tool_max),
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last_n_max_user_chars=int(last_user_max),
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empty_assistant_text_in_sampled=len(empty_ids),
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empty_assistant_text_ids=empty_ids,
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max_content_chars_in_sampled=int(max_content),
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max_tool_chars_in_sampled=int(max_tool),
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)
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__all__ = [
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"SessionContextStats",
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"compute_session_context_stats",
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]
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