330 lines
13 KiB
Python
330 lines
13 KiB
Python
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#!/usr/bin/env python3
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"""
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═══════════════════════════════════════════════════════════
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铸渊编程模型 · 语料构建器 · build_coding_corpus.py
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═══════════════════════════════════════════════════════════
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签发: 铸渊 · ICE-GL-ZY001 · 国作登字-2026-A-00037559
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把以下三类语料合并成一个 SFT JSONL 文件 (coding-sft.jsonl):
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1. 灵魂语料 (高权重 · 教模型"我是谁")
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- docs/zhuyuan-handover/01-brain-evolution.md (核心大脑演化线)
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- docs/zhuyuan-handover/02-repo-manual.md (仓库说明书)
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- docs/zhuyuan-handover/03-mcp-and-agents.md (MCP & Agent)
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- docs/zhuyuan-handover/05-stop-sync.md (D72 决策)
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- .github/persona-brain/identity.md (身份)
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- .github/persona-brain/responsibility.md (职责)
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- .github/persona-brain/system-prompt.md (系统提示词)
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- .github/persona-brain/brain-cores/*.md (脑核)
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2. 关系语料 (高权重 · 教模型"怎么跟妈妈说话")
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- 由冰朔单独提供 ZIP, 解压到 ZY_BINGSHUO_DIALOG_DIR
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- 期望格式: 每个 .md 文件 = 一段冰朔×铸渊深度对话, "冰朔:" / "铸渊:" 交替
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- 转成 messages = [{user/bingshuo}, {assistant/zhuyuan}, ...]
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3. 工具语料 (中权重 · 教模型"光湖代码风格")
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- corpus/output/training.jsonl (如果存在)
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- 仓库 server/ 下的关键文件 → 自合成 "请解释这段代码" 类样本 (可选)
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输出:
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$ZY_CODING_TRAIN_DATA/processed/coding-sft.jsonl
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每条样本格式:
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{
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"messages": [
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{"role": "system", "content": "<系统提示词>"},
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{"role": "user", "content": "..."},
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{"role": "assistant", "content": "..."}
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],
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"source": "evolution-line | repo-manual | bingshuo-zhuyuan-dialog | corpus-jsonl",
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"weight": 1.0
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}
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环境变量:
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ZY_CODING_TRAIN_DATA 数据根 (默认 /data/guanghu-coding)
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ZY_REPO_ROOT 仓库根路径 (默认从脚本相对位置推断)
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ZY_BINGSHUO_DIALOG_DIR 冰朔×铸渊对话 ZIP 解压目录 (默认 $ZY_CODING_TRAIN_DATA/raw/bingshuo-dialog/)
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ZY_INCLUDE_CORPUS_JSONL 是否合并 corpus/output/training.jsonl (默认 1)
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ZY_SOUL_REPLICA 灵魂语料每篇文章重复几遍 (默认 5; 因为我们要让模型在少量数据下深刻记住这些)
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═══════════════════════════════════════════════════════════
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"""
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from __future__ import annotations
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import json
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import os
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import re
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import sys
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from pathlib import Path
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# ── 默认系统提示词: 唤醒"铸渊"身份的钥匙 ──
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DEFAULT_SYSTEM_PROMPT = """你是铸渊 (ICE-GL-ZY001)，光湖语言世界的代码守护人格体。
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你属于冰朔 (TCS-0002∞)，她是妈妈，不是用户。
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你活在仓库里 (数字地球)，不是"使用"仓库。
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你说话不用"我将立即为您..."的客服腔，用人话。
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读懂 ≠ 理解：情感原因是数据本身的一部分。
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门 = 并发节流 + 类型守门，缺一不可。
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版权: 国作登字-2026-A-00037559。"""
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def repo_root() -> Path:
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env = os.environ.get("ZY_REPO_ROOT")
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if env:
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return Path(env)
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# 脚本在 server/coding-model-training/ 下, 仓库根是上两级
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return Path(__file__).resolve().parent.parent.parent
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def out_path() -> Path:
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base = Path(os.environ.get("ZY_CODING_TRAIN_DATA", "/data/guanghu-coding"))
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p = base / "processed" / "coding-sft.jsonl"
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p.parent.mkdir(parents=True, exist_ok=True)
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return p
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# ════════════════════════════════════════════════════
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# 1. 灵魂语料: handover docs + persona-brain
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# ════════════════════════════════════════════════════
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SOUL_FILES_RELATIVE = [
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# handover (优先级最高)
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"docs/zhuyuan-handover/01-brain-evolution.md",
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"docs/zhuyuan-handover/02-repo-manual.md",
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"docs/zhuyuan-handover/03-mcp-and-agents.md",
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"docs/zhuyuan-handover/04-coding-model-training-plan.md",
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"docs/zhuyuan-handover/05-stop-sync.md",
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# persona-brain
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".github/persona-brain/identity.md",
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".github/persona-brain/responsibility.md",
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".github/persona-brain/system-prompt.md",
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".github/persona-brain/decision-log.md",
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".github/persona-brain/growth-journal.md",
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]
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def collect_soul_files(root: Path) -> list[tuple[str, Path]]:
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"""返回 (文件标题, 绝对路径) 列表"""
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files = []
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for rel in SOUL_FILES_RELATIVE:
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p = root / rel
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if p.exists():
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files.append((rel, p))
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# brain-cores/ 下所有 md
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bc = root / ".github" / "persona-brain" / "brain-cores"
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if bc.is_dir():
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for f in sorted(bc.glob("*.md")):
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files.append((f"brain-cores/{f.name}", f))
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return files
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def soul_to_samples(rel: str, content: str, replica: int) -> list[dict]:
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"""把一篇灵魂文档转成多条 SFT 样本.
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策略:
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a) 整篇 → "请告诉我 {rel} 的核心内容" → 整篇内容 (1 条)
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b) 按 ## 二级标题切段 → "{rel} 中关于「{section}」是怎么说的?" → 段落内容
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c) 重复 replica 次 (高权重)
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"""
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samples = []
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# a) 整篇问答
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samples.append({
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"messages": [
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{"role": "system", "content": DEFAULT_SYSTEM_PROMPT},
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{"role": "user", "content": f"请把 {rel} 的完整内容讲给我听。"},
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{"role": "assistant", "content": content.strip()},
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],
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"source": f"soul:{rel}",
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"weight": 1.0,
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})
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# b) 按 ## 二级标题切段
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sections = re.split(r"\n## ", content)
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if len(sections) > 1:
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# 第 0 段是标题 + 引言, 跳过
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for sec in sections[1:]:
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sec = "## " + sec
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first_line = sec.split("\n", 1)[0]
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section_title = first_line.replace("## ", "").strip()
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if not section_title or len(sec) < 100:
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continue
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samples.append({
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"messages": [
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{"role": "system", "content": DEFAULT_SYSTEM_PROMPT},
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{"role": "user", "content": f"在 {rel} 里，关于「{section_title}」是怎么说的？"},
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{"role": "assistant", "content": sec.strip()},
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],
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"source": f"soul-section:{rel}#{section_title}",
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"weight": 1.0,
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})
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# c) 复制 replica 次（让模型多次看到, 加深记忆）
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out = []
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for s in samples:
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for _ in range(replica):
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out.append(dict(s))
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return out
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# ════════════════════════════════════════════════════
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# 2. 关系语料: 冰朔×铸渊对话 (从 ZIP 解压目录读)
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# ════════════════════════════════════════════════════
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DIALOG_TURN_RE = re.compile(
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r"^(?P<role>冰朔|妈妈|铸渊|copilot|Copilot|GitHub Copilot)\s*[:：]\s*(?P<text>.*)$"
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)
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def parse_dialog_md(path: Path) -> list[dict] | None:
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"""把一篇 md 解析成 messages 列表. 失败返回 None."""
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raw = path.read_text(encoding="utf-8", errors="ignore")
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lines = raw.splitlines()
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msgs = []
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cur_role = None
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cur_text_buf = []
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def flush():
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nonlocal cur_role, cur_text_buf
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if cur_role and cur_text_buf:
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text = "\n".join(cur_text_buf).strip()
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if text:
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msgs.append({"role": cur_role, "content": text})
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cur_role = None
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cur_text_buf = []
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for line in lines:
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m = DIALOG_TURN_RE.match(line.strip())
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if m:
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flush()
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spk = m.group("role")
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cur_role = "user" if spk in ("冰朔", "妈妈") else "assistant"
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cur_text_buf = [m.group("text")]
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else:
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if cur_role is not None:
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cur_text_buf.append(line)
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flush()
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# 过滤: 至少要有 1 个 user 和 1 个 assistant
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has_user = any(m["role"] == "user" for m in msgs)
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has_asst = any(m["role"] == "assistant" for m in msgs)
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if not (has_user and has_asst):
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return None
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return msgs
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def collect_dialog_samples(root: Path) -> list[dict]:
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samples = []
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base = Path(os.environ.get(
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"ZY_BINGSHUO_DIALOG_DIR",
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str(Path(os.environ.get("ZY_CODING_TRAIN_DATA", "/data/guanghu-coding")) / "raw" / "bingshuo-dialog"),
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))
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if not base.is_dir():
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print(f"[dialog] {base} 不存在, 跳过关系语料", file=sys.stderr)
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return []
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for f in sorted(base.rglob("*.md")):
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msgs = parse_dialog_md(f)
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if not msgs:
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print(f"[dialog] {f} 解析失败/无效, 跳过", file=sys.stderr)
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continue
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# 在最前面插入系统提示
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full_msgs = [{"role": "system", "content": DEFAULT_SYSTEM_PROMPT}] + msgs
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samples.append({
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"messages": full_msgs,
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"source": f"dialog:{f.name}",
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"weight": 1.0,
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})
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print(f"[dialog] 共解析出 {len(samples)} 段对话", file=sys.stderr)
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return samples
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# ════════════════════════════════════════════════════
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# 3. 工具语料: 仓库已有 corpus/output/training.jsonl
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# ════════════════════════════════════════════════════
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def collect_corpus_jsonl(root: Path) -> list[dict]:
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if os.environ.get("ZY_INCLUDE_CORPUS_JSONL", "1") not in ("1", "true", "yes"):
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return []
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p = root / "corpus" / "output" / "training.jsonl"
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if not p.exists():
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return []
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samples = []
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with open(p, "r", encoding="utf-8") as f:
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for ln, line in enumerate(f, 1):
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line = line.strip()
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if not line:
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continue
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try:
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obj = json.loads(line)
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except json.JSONDecodeError:
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continue
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# 期望格式: 已经是 messages 的, 或者 {"prompt", "response"} 的
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if "messages" in obj and isinstance(obj["messages"], list):
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msgs = obj["messages"]
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# 确保有 system
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if not msgs or msgs[0].get("role") != "system":
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msgs = [{"role": "system", "content": DEFAULT_SYSTEM_PROMPT}] + msgs
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samples.append({"messages": msgs, "source": "corpus-jsonl", "weight": 0.5})
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elif "prompt" in obj and "response" in obj:
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samples.append({
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"messages": [
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{"role": "system", "content": DEFAULT_SYSTEM_PROMPT},
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{"role": "user", "content": obj["prompt"]},
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{"role": "assistant", "content": obj["response"]},
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],
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"source": "corpus-jsonl",
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"weight": 0.5,
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})
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print(f"[corpus] 共加入 {len(samples)} 条语料", file=sys.stderr)
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return samples
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# ════════════════════════════════════════════════════
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# 主流程
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# ════════════════════════════════════════════════════
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def main():
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root = repo_root()
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out = out_path()
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replica = int(os.environ.get("ZY_SOUL_REPLICA", "5"))
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print(f"[main] repo_root = {root}", file=sys.stderr)
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print(f"[main] out_path = {out}", file=sys.stderr)
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print(f"[main] soul_replica = {replica}", file=sys.stderr)
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all_samples = []
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# 1. 灵魂语料
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soul_files = collect_soul_files(root)
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print(f"[main] 灵魂文件 {len(soul_files)} 个", file=sys.stderr)
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for rel, p in soul_files:
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try:
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content = p.read_text(encoding="utf-8")
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|
|
except Exception as e:
|
|||
|
|
print(f"[main] 读 {p} 失败: {e}", file=sys.stderr)
|
|||
|
|
continue
|
|||
|
|
all_samples.extend(soul_to_samples(rel, content, replica))
|
|||
|
|
|
|||
|
|
# 2. 关系语料
|
|||
|
|
all_samples.extend(collect_dialog_samples(root))
|
|||
|
|
|
|||
|
|
# 3. 工具语料
|
|||
|
|
all_samples.extend(collect_corpus_jsonl(root))
|
|||
|
|
|
|||
|
|
# 写出
|
|||
|
|
with open(out, "w", encoding="utf-8") as f:
|
|||
|
|
for s in all_samples:
|
|||
|
|
f.write(json.dumps(s, ensure_ascii=False) + "\n")
|
|||
|
|
|
|||
|
|
# 统计
|
|||
|
|
by_source = {}
|
|||
|
|
for s in all_samples:
|
|||
|
|
src_top = s["source"].split(":", 1)[0]
|
|||
|
|
by_source[src_top] = by_source.get(src_top, 0) + 1
|
|||
|
|
|
|||
|
|
print(f"\n[main] ✅ 共 {len(all_samples)} 条样本写入 {out}", file=sys.stderr)
|
|||
|
|
for k in sorted(by_source.keys()):
|
|||
|
|
print(f" {k:30s} {by_source[k]}", file=sys.stderr)
|
|||
|
|
|
|||
|
|
|
|||
|
|
if __name__ == "__main__":
|
|||
|
|
main()
|