chore: import original project baseline
Import the pre-repair source tree as the history baseline. Runtime data (data/), virtualenvs, bytecode caches and logs are gitignored so local secrets and user state stay out of the repo.
This commit is contained in:
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"""
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core/agent/context.py
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=====================
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🌟 pi 上下文管理的 Python 1:1 移植 —— 令牌估算 + 输出预算钳制
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对照 pi-main 源码：
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packages/ai/src/api/simple-options.ts
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- CONTEXT_SAFETY_TOKENS = 4096 (行 12)
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- clampMaxTokensToContext() (行 12-34)
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maxOutput = contextWindow - inputTokens - 4096
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return min(model.maxTokens, maxOutput) (下限 1)
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- clampOutputsToContext() (行 36-60)
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多模型共享窗口时按比例分配剩余输出预算
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packages/ai/src/api/token-utils.ts
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- 每条消息估算: ceil(chars/4) + 4
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🌟 已声明偏差（仅 1 处，其余全部 1:1）：
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pi 按 chars/4 估算（针对英文）。中文 1 字 ≈ 1 token，chars/4 会严重低估。
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这里采用 CJK 感知估算：CJK 字符按 1 token/字，其余按 4 字符/token。
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这直接影响「压缩触发时机」和「输出预算钳制」，必须更准才不会撞 vLLM 上限。
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pi 的 usage 锚定机制 1:1 保留：若历史里存在有效 assistant usage，
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总估算 = 该 usage 的 totalTokens（服务商精确值）+ 其后消息的逐条估算。
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"""
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from __future__ import annotations
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import math
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import re
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from dataclasses import dataclass
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from typing import Any, Dict, List, Optional, Tuple
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from .types import AgentMessage, ModelConfig
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# ======================================================================
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# 对照 simple-options.ts: export const CONTEXT_SAFETY_TOKENS = 4096
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# 用途：上下文钳制时预留的安全余量（tokenizer 估算误差 + vLLM 精确计数的偏差）
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# ======================================================================
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CONTEXT_SAFETY_TOKENS = 4096
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# 图片 token 估算（对照 pi 对 image part 的固定估算）
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IMAGE_TOKENS = 1600
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_CJK_RE = re.compile(
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r"[\u2e80-\u2eff\u3040-\u30ff\u3400-\u4dbf\u4e00-\u9fff"
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r"\uf900-\ufaff\uff00-\uffef]"
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)
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def _count_text_chars(value: Any) -> int:
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"""统计内容里的字符数（对照 pi token-utils 的字符统计口径）"""
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if isinstance(value, str):
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return len(value)
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if isinstance(value, list): # OpenAI 多模态 content 数组
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n = 0
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for part in value:
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if isinstance(part, dict):
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if part.get("type") == "text":
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n += len(part.get("text", "") or "")
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elif part.get("type") == "image_url":
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n += 0 # 图片单独按 IMAGE_TOKENS 计
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else:
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n += len(str(part))
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return n
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return len(str(value or ""))
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def _count_image_parts(content: Any) -> int:
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if isinstance(content, list):
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return sum(1 for p in content
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if isinstance(p, dict) and p.get("type") == "image_url")
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return 0
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def estimate_message_tokens(message: AgentMessage) -> int:
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"""
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单条消息 token 估算（CJK 感知版）。
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pi 口径: ceil(chars/4) + 4。本项目: CJK 字 ×1 + 其余 /4 + 4 + 图片×1600。
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"""
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raw = message.content
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if isinstance(raw, str):
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chars = len(raw)
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cjk = len(_CJK_RE.findall(raw))
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else:
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# 数组 content：拼接所有文本部分
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joined = []
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for part in raw if isinstance(raw, list) else []:
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if isinstance(part, dict) and part.get("type") == "text":
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joined.append(part.get("text", "") or "")
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text = "".join(joined)
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chars = len(text)
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cjk = len(_CJK_RE.findall(text))
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tokens = cjk + math.ceil((chars - cjk) / 4) + 4
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tokens += _count_image_parts(raw) * IMAGE_TOKENS
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# 思考内容与工具参数也占上下文（assistant 的 reasoning / tool_calls）
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if message.reasoning:
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rcjk = len(_CJK_RE.findall(message.reasoning))
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tokens += rcjk + math.ceil((len(message.reasoning) - rcjk) / 4)
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for tc in message.tool_calls:
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arg_text = _json_str(tc.arguments)
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tokens += math.ceil(len(arg_text) / 4)
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# toolResult 的 content 走 content 字段（str 或 list）
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return tokens
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def _json_str(obj: Any) -> str:
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import json
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try:
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return json.dumps(obj, ensure_ascii=False)
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except Exception:
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return str(obj)
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def _estimate_text_tokens(text: str) -> int:
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"""CJK 感知文本 token 估算（与 estimate_message_tokens 同公式，不含每条 +4 开销）"""
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if not text:
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return 0
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cjk = len(_CJK_RE.findall(text))
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return cjk + math.ceil((len(text) - cjk) / 4)
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def _estimate_tools_tokens(tools) -> int:
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"""
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🆕 P2: 工具 schema token 估算 —— name/description/parameters 的 JSON 字符数（CJK 感知）。
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对照 pi Context 分支: all tools = Σ estimate_text(json(tool))。
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"""
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total = 0
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for t in tools or []:
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schema = {
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"name": getattr(t, "name", "") or "",
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"description": getattr(t, "description", "") or "",
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"parameters": getattr(t, "parameters", None),
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}
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total += _estimate_text_tokens(_json_str(schema))
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return total
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def estimate_context_tokens(messages: List[AgentMessage],
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system_prompt: str = "",
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tools: Optional[List[Any]] = None
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) -> "ContextUsageEstimate":
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"""
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🌟 1:1 对照 pi estimateContextTokens（usage 锚定）：
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- 找到最后一条带有效 usage 的 assistant 消息（🆕 P0: 含 timestamp 时效校验）
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- 总 tokens = 该 usage 的精确值 + 其后消息的逐条估算
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- 无 usage 时 = 全部消息逐条估算
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🆕 P2: 可选 system_prompt / tools 参数（对照 pi Context 分支）：
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- 仅在无锚点分支计入 system 提示词 + 工具 schema 的估算
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（有锚点时 usage 已是含 system+tools 的精确总值，不重复计）
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逐条估算用 CJK 感知版 estimate_message_tokens（已声明偏差）。
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"""
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last_idx, last_usage = _find_last_usage(messages)
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sys_tokens = _estimate_text_tokens(system_prompt) if system_prompt else 0
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tool_tokens = _estimate_tools_tokens(tools) if tools else 0
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if last_idx is None:
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estimated = (sum(estimate_message_tokens(m) for m in messages)
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+ sys_tokens + tool_tokens)
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return ContextUsageEstimate(tokens=estimated, usage_tokens=0,
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trailing_tokens=estimated, last_usage_index=None)
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usage_tokens = calculate_context_tokens(last_usage)
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trailing = sum(estimate_message_tokens(m) for m in messages[last_idx + 1:])
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return ContextUsageEstimate(tokens=usage_tokens + trailing,
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usage_tokens=usage_tokens,
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trailing_tokens=trailing,
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last_usage_index=last_idx)
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@dataclass
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class ContextUsageEstimate:
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"""
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🌟 1:1 对照 pi estimateContextTokens 的返回值
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packages/agent/src/harness/compaction/compaction.ts ContextUsageEstimate:
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tokens 总估算
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usageTokens 最后一条有效 assistant usage 报告的精确 token 数
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trailingTokens 该 usage 之后消息的估算值
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lastUsageIndex 提供 usage 的消息下标（无则 None）
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"""
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tokens: int = 0
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usage_tokens: int = 0
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trailing_tokens: int = 0
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last_usage_index: Optional[int] = None
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def calculate_context_tokens(usage: Dict[str, Any]) -> int:
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"""pi calculateContextTokens: totalTokens || input+output+cacheRead+cacheWrite"""
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if not usage:
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return 0
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total = int(usage.get("totalTokens", 0) or 0)
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if total:
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return total
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return (int(usage.get("input", 0) or 0) + int(usage.get("output", 0) or 0)
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+ int(usage.get("cacheRead", 0) or 0)
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+ int(usage.get("cacheWrite", 0) or 0))
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def _get_assistant_usage(msg: AgentMessage) -> Dict[str, Any]:
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"""
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pi getAssistantUsage：只认「有效」的 assistant usage——
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stopReason 不是 aborted/error，且 calculateContextTokens > 0。
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（🆕 P0: 锚点扫描还需通过 timestamp 时效校验，见 _find_last_usage）
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"""
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if msg.role != "assistant":
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return {}
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if msg.stop_reason in ("aborted", "error"):
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return {}
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usage = msg.usage or {}
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if usage and calculate_context_tokens(usage) > 0:
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return usage
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return {}
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def _row_block_tool_flags(messages: List["AgentMessage"]) -> List[bool]:
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"""
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🆕 G1: 逐条标记「其 DB 行（同非空 db_msg_id 的连续块）是否含工具活动」。
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含工具输出的行，其入库 usage 是「本轮最后一个子请求」的内存快照
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（工具结果未受 4000 字回放截断），不等于下一请求（截断回放）→ 锚点失效。
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轮中内存条目（db_msg_id=""）一律 False：保持原规则
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（轮中条目的内存载荷 == 下一子请求载荷，锚点有效）。
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"""
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n = len(messages)
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flags = [False] * n
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i = 0
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while i < n:
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rid = getattr(messages[i], "db_msg_id", "") or ""
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if not rid:
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i += 1
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continue
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j = i
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has_tool = False
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while j < n and (getattr(messages[j], "db_msg_id", "") or "") == rid:
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mj = messages[j]
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if mj.role == "toolResult" or (mj.role == "assistant" and mj.tool_calls):
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has_tool = True
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j += 1
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for k in range(i, j):
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flags[k] = has_tool
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i = j
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return flags
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def _find_last_usage(messages: List["AgentMessage"]):
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"""
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🆕 P0: 1:1 对照 pi getLastAssistantUsageInfo（含锚点失效校验）：
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assistant 的 usage 要成为锚点，必须满足
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① timestamp >= 它前面所有消息的最大 timestamp（即它不比任何前消息「更旧」）。
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场景：压缩后 state = [新摘要消息(T_new)] + [保留的旧消息(T_old)]，
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保留窗里 assistant 的 usage 来自压缩前（如 60K 精确值）——不校验的话，
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下一次 should_compact 会锚定到过期值 → 误触发二次压缩（摘要套摘要）。
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摘要消息 timestamp 晚于全部保留消息 → 前缀游标抬高 → 旧 usage 自动失效；
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压缩后第一个新 assistant 回复到达 → 新 usage 锚点自动恢复。
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② 🆕 G1: 该 usage 所在 DB 行不含工具活动（toolResult / assistant.tool_calls）。
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含工具输出的行，入库 usage = 本轮内存快照（工具结果未截断）≠ 下一请求
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（4000 字截断回放）→ 系统性高估 → 误触发压缩（实测 113904 锚点 vs
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真实下一请求 ~90k）。纯文本行 / 轮中内存条目的 usage 仍是精确值，保留锚定。
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（消息无 timestamp 信息（全 0）时行为等同旧版「取最后一条有效」，完全向后兼容。）
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"""
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latest_prefix_ts = -1 # 对照 pi: Number.NEGATIVE_INFINITY
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last_idx = None
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last_usage: Dict[str, Any] = {}
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row_tool_flags = _row_block_tool_flags(messages)
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for i, msg in enumerate(messages):
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if msg.role == "assistant":
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applies = (msg.timestamp or 0) >= latest_prefix_ts
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if (applies
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and not row_tool_flags[i]
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and msg.stop_reason not in ("aborted", "error")
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and msg.usage
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and calculate_context_tokens(msg.usage) > 0):
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last_idx, last_usage = i, msg.usage
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ts = msg.timestamp or 0
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if ts > latest_prefix_ts:
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latest_prefix_ts = ts
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return last_idx, last_usage
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def clamp_max_tokens_to_context(model: ModelConfig,
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context: List[AgentMessage],
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system_prompt: str = "",
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tools: Optional[List[Any]] = None
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) -> Optional[Tuple[int, int]]:
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"""
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🌟 对照 simple-options.ts:12-34 clampMaxTokensToContext（1:1 公式）：
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input_estimate = estimateContextTokens(context) # 🆕 P2: 含 system+tools（pi Context 实参）
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if (input_estimate > model.contextWindow) return null // 上下文已溢出
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maxOutput = contextWindow - inputTokens - CONTEXT_SAFETY_TOKENS
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return max(1, min(model.maxTokens, maxOutput))
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这就是 pi 约束公式的本体：
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输入 + 输出 ≤ contextWindow − 4096 ≤ vLLM max_model_len
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🆕 P2: system_prompt / tools 仅在无 usage 锚点分支计入（首轮流式精确记账，
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与 pi 传完整 Context 的行为一致）；有锚点时 usage 已精确覆盖，不重复计。
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返回 (max_tokens, input_tokens)；输入已溢出窗口时返回 None（由恢复逻辑接管）。
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"""
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input_tokens = estimate_context_tokens(
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context, system_prompt=system_prompt, tools=tools).tokens
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if input_tokens > model.context_window:
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return None
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max_output = model.context_window - input_tokens - CONTEXT_SAFETY_TOKENS
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return max(1, min(model.max_tokens, max_output)), input_tokens
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def clamp_outputs_to_context(context: List[AgentMessage],
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models: List[ModelConfig],
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reserve_tokens: int = CONTEXT_SAFETY_TOKENS
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) -> List[int]:
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"""
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对照 simple-options.ts:36-60 clampOutputsToContext：
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多个模型共享同一窗口时，把剩余输出预算按比例分配给各模型。
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（haocode 单模型场景用不到，保留以求框架完整）
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"""
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input_tokens = estimate_context_tokens(context).tokens
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available = max(0, sum(m.context_window for m in models) - input_tokens - reserve_tokens)
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if not models:
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return []
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weights = [m.max_tokens for m in models]
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total_w = sum(weights) or 1
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alloc = [max(1, int(available * w / total_w)) for w in weights]
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# 各自不超过自身 max_tokens
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alloc = [min(a, m.max_tokens) for a, m in zip(alloc, models)]
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return alloc
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def should_compact(messages: List[AgentMessage], model: ModelConfig,
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reserve_tokens: int = 16384,
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system_prompt: str = "",
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tools: Optional[List[Any]] = None) -> Tuple[bool, int]:
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"""
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🌟 1:1 对照 pi compaction.ts shouldCompact（harness 原版公式）：
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contextTokens > contextWindow - reserveTokens
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reserveTokens 默认 16384（DEFAULT_COMPACTION_SETTINGS.reserveTokens），
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即「为摘要提示词与输出预留 16K」。估算值取 usage 锚定估算。
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🆕 G2: system_prompt/tools 透传 —— 无锚点分支（工具行快照锚点被 G1 失效后
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更常走到）也要计入 system + 工具 schema，度量「下一请求真实载荷」，
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与显示端（update_context_display）同口径。
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"""
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tokens = estimate_context_tokens(messages, system_prompt=system_prompt,
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tools=tools).tokens
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return (tokens > model.context_window - reserve_tokens), tokens
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