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:
@@ -0,0 +1,637 @@
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"""
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core/agent/compaction.py
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========================
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🌟 pi 上下文压缩算法的 Python 1:1 移植（harness 原版算法）
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对照 pi-main 源码（逐函数对应）：
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packages/agent/src/harness/compaction/compaction.ts
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DEFAULT_COMPACTION_SETTINGS -> CompactionSettings / DEFAULT_COMPACTION_SETTINGS
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shouldCompact -> 见 context.py: should_compact（同一公式）
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estimateTokens / estimateContextTokens -> context.py（usage 锚定，CJK 感知见说明）
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findValidCutPoints / findCutPoint -> find_valid_cut_points / find_cut_point
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findTurnStartIndex -> find_turn_start
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prepareCompaction -> prepare_compaction
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SUMMARIZATION_SYSTEM_PROMPT -> 同名（逐字移植）
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SUMMARIZATION_PROMPT -> 同名（逐字移植）
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UPDATE_SUMMARIZATION_PROMPT -> 同名（逐字移植，迭代更新用）
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TURN_PREFIX_SUMMARIZATION_PROMPT -> 同名（逐字移植，断轮前缀用）
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generateSummaryWithUsage -> generate_summary（maxTokens = 0.8×reserve）
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generateTurnPrefixSummary -> generate_turn_prefix_summary（0.5×reserve）
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compact -> compact_context（断轮双摘要 + 拼接格式 1:1）
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packages/agent/src/harness/compaction/utils.ts
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serializeConversation -> serialize_conversation（1:1，含 2000 字符截断）
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extractFileOpsFromMessage -> extract_file_ops_from_message
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computeFileLists -> compute_file_lists
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formatFileOperations -> format_file_operations
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TOOL_RESULT_MAX_CHARS = 2000 -> 同名常量
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摘要 LLM 调用由上层注入：
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summarize_fn(prompt_text: str, system_prompt: str, max_tokens: int) -> str
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（pi 里是 models.completeSimple + retry；haocode 用 OpenAI 客户端非流式调用，
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由 llm_engine.AgentWorker 实现并注入。）
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已声明的偏差（仅 2 处，见 context.py 头注）：
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1. 单条消息 token 估算用 CJK 感知启发式（pi 是 chars/4）——对中文会话更安全
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2. 摘要 LLM 调用失败时降级为机械摘录（pi 返回 CompactionError）——桌面应用优先不丢上下文
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其余全部 1:1：触发公式、usage 锚定、token 预算切点、有效切点规则、断轮双摘要、
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迭代式 previousSummary 更新、摘要提示词逐字、文件操作附录、拼接格式。
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"""
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from __future__ import annotations
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import json
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from dataclasses import dataclass, field
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from typing import Callable, List, Optional, Set, Tuple
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from .types import AgentMessage, ModelConfig
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# ======================================================================
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# 压缩设置 —— 1:1 对照 DEFAULT_COMPACTION_SETTINGS
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# ======================================================================
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@dataclass
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class CompactionSettings:
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"""pi: interface CompactionSettings { enabled; reserveTokens; keepRecentTokens }"""
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enabled: bool = True
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reserve_tokens: int = 16384 # pi: 16384（摘要提示词与输出预留）
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keep_recent_tokens: int = 20000 # pi: 20000（压缩后保留的近期上下文预算）
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DEFAULT_COMPACTION_SETTINGS = CompactionSettings()
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# pi utils.ts: const TOOL_RESULT_MAX_CHARS = 2000
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TOOL_RESULT_MAX_CHARS = 2000
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# ======================================================================
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# 摘要提示词 —— 从 pi compaction.ts 逐字移植（不得改写，摘要质量依赖它）
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# ======================================================================
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SUMMARIZATION_SYSTEM_PROMPT = \
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"You are a context summarization assistant. Your task is to read a conversation " \
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"between a user and an AI assistant, then produce a structured summary following " \
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"the exact format specified.\n\n" \
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"Do NOT continue the conversation. Do NOT respond to any questions in the " \
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"conversation. ONLY output the structured summary."
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SUMMARIZATION_PROMPT = """The messages above are a conversation to summarize. Create a structured context checkpoint summary that another LLM will use to continue the work.
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Use this EXACT format:
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## Goal
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[What is the user trying to accomplish? Can be multiple items if the session covers different tasks.]
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## Constraints & Preferences
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- [Any constraints, preferences, or requirements mentioned by user]
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- [Or "(none)" if none were mentioned]
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## Progress
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### Done
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- [x] [Completed tasks/changes]
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### In Progress
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- [ ] [Current work]
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### Blocked
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- [Issues preventing progress, if any]
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## Key Decisions
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- **[Decision]**: [Brief rationale]
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## Next Steps
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1. [Ordered list of what should happen next]
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## Critical Context
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- [Any data, examples, or references needed to continue]
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- [Or "(none)" if not applicable]
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Keep each section concise. Preserve exact file paths, function names, and error messages."""
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UPDATE_SUMMARIZATION_PROMPT = """The messages above are NEW conversation messages to incorporate into the existing summary provided in <previous-summary> tags.
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Update the existing structured summary with new information. RULES:
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- PRESERVE all existing information from the previous summary
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- ADD new progress, decisions, and context from the new messages
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- UPDATE the Progress section: move items from "In Progress" to "Done" when completed
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- UPDATE "Next Steps" based on what was accomplished
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- PRESERVE exact file paths, function names, and error messages
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- If something is no longer relevant, you may remove it
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Use this EXACT format:
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## Goal
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[Preserve existing goals, add new ones if the task expanded]
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## Constraints & Preferences
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- [Preserve existing, add new ones discovered]
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## Progress
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### Done
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- [x] [Include previously done items AND newly completed items]
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### In Progress
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- [ ] [Current work - update based on progress]
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### Blocked
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- [Current blockers - remove if resolved]
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## Key Decisions
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- **[Decision]**: [Brief rationale] (preserve all previous, add new)
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## Next Steps
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1. [Update based on current state]
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## Critical Context
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- [Preserve important context, add new if needed]
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Keep each section concise. Preserve exact file paths, function names, and error messages."""
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TURN_PREFIX_SUMMARIZATION_PROMPT = """This is the PREFIX of a turn that was too large to keep. The SUFFIX (recent work) is retained.
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Summarize the prefix to provide context for the retained suffix:
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## Original Request
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[What did the user ask for in this turn?]
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## Early Progress
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- [Key decisions and work done in the prefix]
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## Context for Suffix
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- [Information needed to understand the retained recent work]
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Be concise. Focus on what's needed to understand the kept suffix."""
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# ======================================================================
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# 对话序列化 —— 1:1 对照 utils.ts serializeConversation
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# ======================================================================
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def _safe_json(value) -> str:
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try:
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s = json.dumps(value, ensure_ascii=False)
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return s if s is not None else "undefined"
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except Exception:
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return "[unserializable]"
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def _content_text(content) -> str:
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"""pi contentText: str 或 [{type:"text",text}] 列表取文本拼接"""
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if content is None:
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return ""
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if isinstance(content, str):
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return content
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if isinstance(content, list):
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parts = []
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for b in content:
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if isinstance(b, dict) and b.get("type") == "text":
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parts.append(str(b.get("text", "")))
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return "\n".join(p for p in parts if p)
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return str(content)
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def _truncate_for_summary(text: str, max_chars: int) -> str:
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"""pi utils.ts truncateForSummary（逐字逻辑）"""
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if len(text) <= max_chars:
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return text
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truncated = len(text) - max_chars
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return f"{text[:max_chars]}\n\n[... {truncated} more characters truncated]"
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def serialize_conversation(messages: List[AgentMessage]) -> str:
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"""
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1:1 对照 utils.ts serializeConversation（输出格式逐字一致）：
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[User]: ...
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[Assistant thinking]: ...
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[Assistant]: ...
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[Assistant tool calls]: name(k=v, k2=v2); name2(...)
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[Tool result]: ...（超 2000 字符截断）
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"""
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parts: List[str] = []
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for msg in messages:
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if msg.role == "user":
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content = _content_text(msg.content)
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if content:
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parts.append(f"[User]: {content}")
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elif msg.role == "assistant":
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thinking_parts = []
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tool_calls = []
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if msg.reasoning:
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thinking_parts.append(msg.reasoning)
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text = _content_text(msg.content)
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for tc in (msg.tool_calls or []):
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args_str = ", ".join(f"{k}={_safe_json(v)}"
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for k, v in (tc.arguments or {}).items())
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tool_calls.append(f"{tc.name}({args_str})")
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if thinking_parts:
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parts.append(f"[Assistant thinking]: {chr(10).join(thinking_parts)}")
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if text:
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parts.append(f"[Assistant]: {text}")
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if tool_calls:
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parts.append(f"[Assistant tool calls]: {'; '.join(tool_calls)}")
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elif msg.role == "toolResult":
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content = _content_text(msg.content)
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if content:
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parts.append(f"[Tool result]: "
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f"{_truncate_for_summary(content, TOOL_RESULT_MAX_CHARS)}")
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return "\n\n".join(parts)
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# ======================================================================
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# 文件操作提取 —— 1:1 对照 utils.ts extractFileOps*/computeFileLists/formatFileOperations
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# ======================================================================
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class FileOperations:
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def __init__(self):
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self.read: Set[str] = set()
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self.written: Set[str] = set()
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self.edited: Set[str] = set()
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def extract_file_ops_from_message(message: AgentMessage, file_ops: FileOperations):
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"""pi: assistant 的 toolCall 参数里 path 字段 → read/write/edit 归类"""
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if message.role != "assistant":
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return
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for tc in (message.tool_calls or []):
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args = tc.arguments or {}
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path = args.get("path")
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if not isinstance(path, str) or not path:
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continue
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if tc.name == "read":
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file_ops.read.add(path)
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elif tc.name == "write":
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file_ops.written.add(path)
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elif tc.name == "edit":
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file_ops.edited.add(path)
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def compute_file_lists(file_ops: FileOperations) -> Tuple[List[str], List[str]]:
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"""pi computeFileLists: modified = edited|written；readOnly = read-modified；均排序"""
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modified = file_ops.edited | file_ops.written
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read_only = sorted(f for f in file_ops.read if f not in modified)
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return read_only, sorted(modified)
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def format_file_operations(read_files: List[str], modified_files: List[str]) -> str:
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"""pi formatFileOperations: <read-files>/<modified-files> 标签拼接"""
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sections = []
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if read_files:
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sections.append("<read-files>\n" + "\n".join(read_files) + "\n</read-files>")
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if modified_files:
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sections.append("<modified-files>\n" + "\n".join(modified_files) + "\n</modified-files>")
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if not sections:
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return ""
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return "\n\n" + "\n\n".join(sections)
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# ======================================================================
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# 切分点 —— 1:1 对照 findValidCutPoints / findTurnStartIndex / findCutPoint
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# ======================================================================
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@dataclass
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class CutPointResult:
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"""pi: interface CutPointResult"""
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first_kept_index: int # 保留段首条在 compactable 列表里的下标
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turn_start_index: int = -1 # 断轮时：该轮起点（user 消息）下标；否则 -1
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is_split_turn: bool = False
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def find_valid_cut_points(messages: List[AgentMessage],
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start_index: int, end_index: int) -> List[int]:
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"""
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pi findValidCutPoints：消息角色为 user/assistant 的位置是有效切点
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（toolResult 不能做切点——它会与前面的 toolCall 分离）。
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pi 里的 bashExecution/branchSummary/compactionSummary 等角色
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在 haocode 消息模型中不存在，等价规则即 role in (user, assistant)。
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"""
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cut_points = []
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for i in range(start_index, end_index):
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if messages[i].role in ("user", "assistant"):
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cut_points.append(i)
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return cut_points
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def find_turn_start(messages: List[AgentMessage], entry_index: int,
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start_index: int) -> int:
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"""pi findTurnStartIndex：向前找本轮起点（user 消息 / branch_summary）"""
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for i in range(entry_index, start_index - 1, -1):
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if messages[i].role == "user":
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return i
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return -1
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def _estimate(msg: AgentMessage) -> int:
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from .context import estimate_message_tokens
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return estimate_message_tokens(msg)
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def find_cut_point(messages: List[AgentMessage], start_index: int,
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end_index: int, keep_recent_tokens: int) -> CutPointResult:
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"""
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1:1 对照 pi findCutPoint：
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1. 从尾部向前累计 token，直到累计 >= keep_recent_tokens
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2. 取该位置（含）之后的第一个有效切点
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3. 切点不是 user 消息 → 断轮：找本轮起点，前缀单独摘要
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（pi 的「回退跳过状态条目」循环针对 session 状态条目；
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haocode 消息列表没有状态条目，等价省略。）
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"""
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cut_points = find_valid_cut_points(messages, start_index, end_index)
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if not cut_points:
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return CutPointResult(first_kept_index=start_index)
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accumulated = 0
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cut_index = cut_points[0]
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for i in range(end_index - 1, start_index - 1, -1):
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accumulated += _estimate(messages[i])
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if accumulated >= keep_recent_tokens:
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for c in cut_points:
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if c >= i:
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cut_index = c
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break
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break
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is_user = messages[cut_index].role == "user"
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turn_start = -1 if is_user else find_turn_start(messages, cut_index, start_index)
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is_split = (not is_user) and turn_start != -1
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return CutPointResult(first_kept_index=cut_index,
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turn_start_index=turn_start,
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is_split_turn=is_split)
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|
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# ======================================================================
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# 压缩准备 —— 1:1 对照 prepareCompaction
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# ======================================================================
|
||||
@dataclass
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||||
class CompactionPreparation:
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||||
"""pi: interface CompactionPreparation"""
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||||
messages_to_summarize: List[AgentMessage] = field(default_factory=list)
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||||
turn_prefix_messages: List[AgentMessage] = field(default_factory=list)
|
||||
retained_tail: List[AgentMessage] = field(default_factory=list)
|
||||
is_split_turn: bool = False
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||||
tokens_before: int = 0
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||||
previous_summary: Optional[str] = None
|
||||
file_ops: FileOperations = field(default_factory=FileOperations)
|
||||
settings: CompactionSettings = DEFAULT_COMPACTION_SETTINGS
|
||||
|
||||
|
||||
def _compaction_diag(line: str) -> None:
|
||||
"""压缩自诊断日志（与 recovery.compact_diag_log 同一文件/规范：
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||||
print + 追加 compaction_diag.log + 失败静默）。"""
|
||||
import os as _os
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||||
import time as _time
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||||
t = _time.time()
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||||
stamp = (f"[{_time.strftime('%H:%M:%S', _time.localtime(t))}"
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||||
f".{int(t * 1000) % 1000:03d}]")
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||||
print(f"{stamp} {line}", flush=True)
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||||
try:
|
||||
path = _os.path.join(
|
||||
_os.path.dirname(_os.path.dirname(_os.path.dirname(_os.path.abspath(__file__)))),
|
||||
"compaction_diag.log")
|
||||
with open(path, "a", encoding="utf-8") as f:
|
||||
f.write(f"{stamp} {line}\n")
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
|
||||
def _nothing_to_summarize_diag(messages: List[AgentMessage],
|
||||
compactable: List[AgentMessage],
|
||||
cut: CutPointResult,
|
||||
settings: CompactionSettings) -> str:
|
||||
"""prepare_compaction 返回 None（无可摘要内容）时的自诊断行：
|
||||
记录分支子类型 + 关键量，下次失败可直接从日志定位原因。
|
||||
|
||||
子类型：
|
||||
no_valid_cut_points —— 可压缩范围内没有任何 user/assistant 条目
|
||||
total_below_keep_recent —— 可压缩范围总量 < keep_recent（通常意味着
|
||||
上下文主体是旧摘要本身，无新内容）
|
||||
cut_pinned_at_zero —— 预算从尾部累加只在 i=0 才达标，即首条
|
||||
条目独占 ≥ (总量-keep_recent) 的 token
|
||||
（单条超长消息/巨型工具输出主导上下文）
|
||||
"""
|
||||
from .context import estimate_context_tokens, estimate_message_tokens
|
||||
try:
|
||||
total = estimate_context_tokens(messages).tokens
|
||||
n_user = sum(1 for m in compactable if m.role == "user")
|
||||
n_asst = sum(1 for m in compactable if m.role == "assistant")
|
||||
ests = [(estimate_message_tokens(m), i) for i, m in enumerate(compactable)]
|
||||
e0 = ests[0][0] if ests else 0
|
||||
rest = sum(e for e, _ in ests[1:])
|
||||
top3 = sorted(ests, reverse=True)[:3]
|
||||
roles = " ".join(("T" if m.role == "toolResult" else m.role[0].upper())
|
||||
for m in compactable[:10])
|
||||
if not find_valid_cut_points(compactable, 0, len(compactable)):
|
||||
sub = "no_valid_cut_points"
|
||||
elif sum(e for e, _ in ests) < settings.keep_recent_tokens:
|
||||
sub = "total_below_keep_recent"
|
||||
else:
|
||||
sub = "cut_pinned_at_zero"
|
||||
big = "; ".join(f"idx{i}={e}" for e, i in top3)
|
||||
return (f"[COMPACT_NONE] branch=nothing_to_summarize sub={sub} "
|
||||
f"msgs={len(messages)} est_total={total} compactable={len(compactable)} "
|
||||
f"keep_recent={settings.keep_recent_tokens} "
|
||||
f"first_kept={cut.first_kept_index} turn_start={cut.turn_start_index} "
|
||||
f"split={cut.is_split_turn} user={n_user} asst={n_asst} "
|
||||
f"est_first={e0} est_rest={rest} top3=[{big}] head_roles=[{roles}]")
|
||||
except Exception as ex: # 诊断本身失败不能影响主流程
|
||||
return f"[COMPACT_NONE] branch=nothing_to_summarize (diag failed: {ex})"
|
||||
|
||||
|
||||
def prepare_compaction(messages: List[AgentMessage],
|
||||
settings: Optional[CompactionSettings] = None
|
||||
) -> Optional[CompactionPreparation]:
|
||||
"""
|
||||
1:1 对照 pi prepareCompaction：
|
||||
- 上一条压缩摘要（messages[0].kind == "compaction_summary"）不重复摘要，
|
||||
其内容作为 previousSummary 走迭代更新提示词
|
||||
- 可压缩范围 = 摘要之后的全部消息（pi 里等价于「上次保留尾 + 新消息」）
|
||||
- 切点在可压缩范围内选；tokens_before 按完整上下文（含摘要消息）估算
|
||||
不可压缩（空/无摘要对象）时返回 None（对照 pi 返回 ok(undefined)）。
|
||||
"""
|
||||
from .context import estimate_context_tokens
|
||||
settings = settings or DEFAULT_COMPACTION_SETTINGS
|
||||
if not messages:
|
||||
_compaction_diag("[COMPACT_NONE] branch=empty_messages")
|
||||
return None
|
||||
if messages[-1].kind == "compaction_summary":
|
||||
_compaction_diag(
|
||||
f"[COMPACT_NONE] branch=tail_is_summary msgs={len(messages)} "
|
||||
f"tail_head={_content_text(messages[-1].content)[:60]!r}")
|
||||
return None
|
||||
|
||||
previous_summary = None
|
||||
if messages[0].kind == "compaction_summary":
|
||||
previous_summary = _content_text(messages[0].content)
|
||||
compactable = messages[1:]
|
||||
else:
|
||||
compactable = messages
|
||||
if not compactable:
|
||||
_compaction_diag("[COMPACT_NONE] branch=compactable_empty (上下文只剩旧摘要)")
|
||||
return None
|
||||
|
||||
tokens_before = estimate_context_tokens(messages).tokens
|
||||
cut = find_cut_point(compactable, 0, len(compactable),
|
||||
settings.keep_recent_tokens)
|
||||
|
||||
history_end = cut.turn_start_index if cut.is_split_turn else cut.first_kept_index
|
||||
messages_to_summarize = compactable[:history_end]
|
||||
turn_prefix_messages = []
|
||||
if cut.is_split_turn:
|
||||
turn_prefix_messages = compactable[cut.turn_start_index:cut.first_kept_index]
|
||||
retained_tail = compactable[cut.first_kept_index:]
|
||||
|
||||
if not messages_to_summarize and not turn_prefix_messages:
|
||||
# 🆕 自诊断：记录是哪种子条件导致无东西可摘要（见 _nothing_to_summarize_diag）
|
||||
_compaction_diag(_nothing_to_summarize_diag(messages, compactable, cut,
|
||||
settings))
|
||||
return None # 没有可摘要内容
|
||||
|
||||
file_ops = FileOperations()
|
||||
for m in messages_to_summarize:
|
||||
extract_file_ops_from_message(m, file_ops)
|
||||
if cut.is_split_turn:
|
||||
for m in turn_prefix_messages:
|
||||
extract_file_ops_from_message(m, file_ops)
|
||||
|
||||
return CompactionPreparation(
|
||||
messages_to_summarize=messages_to_summarize,
|
||||
turn_prefix_messages=turn_prefix_messages,
|
||||
retained_tail=retained_tail,
|
||||
is_split_turn=cut.is_split_turn,
|
||||
tokens_before=tokens_before,
|
||||
previous_summary=previous_summary,
|
||||
file_ops=file_ops,
|
||||
settings=settings,
|
||||
)
|
||||
|
||||
|
||||
# ======================================================================
|
||||
# 摘要生成 —— 1:1 对照 generateSummaryWithUsage / generateTurnPrefixSummary
|
||||
# ======================================================================
|
||||
def _build_summary_prompt(conversation_text: str, previous_summary: Optional[str]) -> str:
|
||||
"""pi generateSummaryWithUsage 的 prompt 组装（逐字结构）"""
|
||||
base = UPDATE_SUMMARIZATION_PROMPT if previous_summary else SUMMARIZATION_PROMPT
|
||||
prompt = f"<conversation>\n{conversation_text}\n</conversation>\n\n"
|
||||
if previous_summary:
|
||||
prompt += f"<previous-summary>\n{previous_summary}\n</previous-summary>\n\n"
|
||||
prompt += base
|
||||
return prompt
|
||||
|
||||
|
||||
def generate_summary(messages: List[AgentMessage],
|
||||
summarize_fn: Callable[[str, str, int], str],
|
||||
reserve_tokens: int,
|
||||
model_max_tokens: int,
|
||||
previous_summary: Optional[str] = None
|
||||
) -> Tuple[Optional[str], Optional[str]]:
|
||||
"""
|
||||
返回 (summary_text, error)。
|
||||
maxTokens = min(0.8 × reserveTokens, model.maxTokens) —— 1:1 对照。
|
||||
"""
|
||||
max_tokens = min(
|
||||
int(0.8 * reserve_tokens),
|
||||
model_max_tokens if model_max_tokens > 0 else (1 << 30),
|
||||
)
|
||||
conversation = serialize_conversation(messages)
|
||||
prompt = _build_summary_prompt(conversation, previous_summary)
|
||||
try:
|
||||
text = summarize_fn(prompt, SUMMARIZATION_SYSTEM_PROMPT, max_tokens)
|
||||
except Exception as e:
|
||||
return None, f"Summarization failed: {e}"
|
||||
if not text or not text.strip():
|
||||
return None, "Summarization failed: empty response"
|
||||
return text.strip(), None
|
||||
|
||||
|
||||
def generate_turn_prefix_summary(messages: List[AgentMessage],
|
||||
summarize_fn: Callable[[str, str, int], str],
|
||||
reserve_tokens: int,
|
||||
model_max_tokens: int
|
||||
) -> Tuple[Optional[str], Optional[str]]:
|
||||
"""maxTokens = min(0.5 × reserveTokens, model.maxTokens) —— 1:1 对照"""
|
||||
max_tokens = min(
|
||||
int(0.5 * reserve_tokens),
|
||||
model_max_tokens if model_max_tokens > 0 else (1 << 30),
|
||||
)
|
||||
conversation = serialize_conversation(messages)
|
||||
prompt = f"<conversation>\n{conversation}\n</conversation>\n\n{TURN_PREFIX_SUMMARIZATION_PROMPT}"
|
||||
try:
|
||||
text = summarize_fn(prompt, SUMMARIZATION_SYSTEM_PROMPT, max_tokens)
|
||||
except Exception as e:
|
||||
return None, f"Turn prefix summarization failed: {e}"
|
||||
if not text or not text.strip():
|
||||
return None, "Turn prefix summarization failed: empty response"
|
||||
return text.strip(), None
|
||||
|
||||
|
||||
# ======================================================================
|
||||
# 主入口 —— 1:1 对照 compact()
|
||||
# ======================================================================
|
||||
def compact_context(messages: List[AgentMessage],
|
||||
model: ModelConfig,
|
||||
summarize_fn: Callable[[str, str, int], str],
|
||||
settings: Optional[CompactionSettings] = None
|
||||
) -> Optional[List[AgentMessage]]:
|
||||
"""
|
||||
执行压缩。返回 [压缩摘要消息] + 保留尾巴；不可压缩时返回 None。
|
||||
|
||||
摘要消息: role="user", kind="compaction_summary"，content 为纯摘要文本
|
||||
（pi 的 compaction 条目；下次压缩时自动走迭代更新提示词）。
|
||||
|
||||
断轮（切点落在某轮中间）时 1:1 对照 pi compact()：
|
||||
历史摘要 与 轮前缀摘要 分两次 LLM 调用，拼接为
|
||||
{history}\n\n---\n\n**Turn Context (split turn):**\n\n{prefix}
|
||||
最后追加文件操作附录（<read-files>/<modified-files>）。
|
||||
"""
|
||||
settings = settings or DEFAULT_COMPACTION_SETTINGS
|
||||
prep = prepare_compaction(messages, settings)
|
||||
if prep is None:
|
||||
return None
|
||||
|
||||
history_text: Optional[str] = None
|
||||
prefix_error: Optional[str] = None
|
||||
history_error: Optional[str] = None
|
||||
|
||||
if prep.is_split_turn and prep.turn_prefix_messages:
|
||||
if prep.messages_to_summarize:
|
||||
history_text, history_error = generate_summary(
|
||||
prep.messages_to_summarize, summarize_fn,
|
||||
prep.settings.reserve_tokens, model.max_tokens,
|
||||
prep.previous_summary)
|
||||
if history_error:
|
||||
return _degraded_compact(prep, history_error)
|
||||
else:
|
||||
history_text = "No prior history."
|
||||
prefix_text, prefix_error = generate_turn_prefix_summary(
|
||||
prep.turn_prefix_messages, summarize_fn,
|
||||
prep.settings.reserve_tokens, model.max_tokens)
|
||||
if prefix_error:
|
||||
return _degraded_compact(prep, prefix_error)
|
||||
summary = (f"{history_text}\n\n---\n\n"
|
||||
f"**Turn Context (split turn):**\n\n{prefix_text}")
|
||||
else:
|
||||
if not prep.messages_to_summarize:
|
||||
return None
|
||||
summary, history_error = generate_summary(
|
||||
prep.messages_to_summarize, summarize_fn,
|
||||
prep.settings.reserve_tokens, model.max_tokens,
|
||||
prep.previous_summary)
|
||||
if history_error:
|
||||
return _degraded_compact(prep, history_error)
|
||||
|
||||
read_files, modified_files = compute_file_lists(prep.file_ops)
|
||||
summary += format_file_operations(read_files, modified_files)
|
||||
|
||||
summary_msg = AgentMessage(role="user", content=summary,
|
||||
kind="compaction_summary")
|
||||
return [summary_msg] + prep.retained_tail
|
||||
|
||||
|
||||
def _degraded_compact(prep: CompactionPreparation,
|
||||
error: str) -> Optional[List[AgentMessage]]:
|
||||
"""
|
||||
已声明偏差（对照 pi: 直接返回 CompactionError）：
|
||||
桌面应用优先「不丢上下文」——摘要失败时降级为机械摘录，
|
||||
保留尾巴原样不动。
|
||||
"""
|
||||
old = prep.messages_to_summarize + prep.turn_prefix_messages
|
||||
if not old:
|
||||
return None
|
||||
excerpt = serialize_conversation(old)[-500:]
|
||||
summary = (f"(自动压缩：摘要生成失败 [{error}]，以下为旧对话尾部摘录)\n\n{excerpt}")
|
||||
summary_msg = AgentMessage(role="user", content=summary,
|
||||
kind="compaction_summary")
|
||||
return [summary_msg] + prep.retained_tail
|
||||
Reference in New Issue
Block a user