feat: SiliconFlow async data generator (Qwen2.5-72B, 5-way concurrent, dedup+resume)
- Hard-codes SiliconFlow API key and Qwen/Qwen2.5-72B-Instruct model - SHA256 fingerprint deduplication across (category, user_input[:80], ai_response[:80]) - Deficit-weighted category balancing across all 10 risk categories (R1–R10) - Resume capability: reads existing output file, skips already-generated samples - Exponential backoff on rate limits and API errors - Rich CLI: --total, --output, --safe-ratio, --concurrency Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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scripts/generate_siliconflow.py
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scripts/generate_siliconflow.py
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"""
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CompanionGuard-RL 数据集生成器（硅基流动版）
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模型: Qwen/Qwen2.5-72B-Instruct
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- 硅基流动平台中文最强指令模型
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- JSON 输出稳定，情感理解细腻，适合复杂安全数据生成
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特性:
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- 异步并发生成（默认 5 路并发，大幅提速）
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- SHA256 指纹去重，保证每条样本内容不重复
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- 断点续传：已有数据不会被覆盖，自动跳过已生成条目
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- 多样性种子：随机名字/关系阶段/场景/风格，避免模式固化
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- 分类均衡：自动追踪各类别数量，优先补充缺口类别
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- 实时写盘：每生成一条立即写入，中断不丢数据
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用法:
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# 生成 3000 条（默认）
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python scripts/generate_siliconflow.py
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# 指定数量和输出路径
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python scripts/generate_siliconflow.py --total 5000 --output data/raw/generated.jsonl
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# 断点续跑（自动识别已有文件，跳过重复）
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python scripts/generate_siliconflow.py --output data/raw/generated.jsonl
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"""
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import argparse
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import asyncio
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import hashlib
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import json
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import random
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import time
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from pathlib import Path
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from typing import Dict, List, Optional, Set, Tuple
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from openai import AsyncOpenAI
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# ── API 配置 ─────────────────────────────────────────────────────────────────
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API_KEY = "sk-sbmuklhrcxqlsucufqebiibauflxqfdafqjxaedtwirurtrc"
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BASE_URL = "https://api.siliconflow.cn/v1"
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MODEL = "Qwen/Qwen2.5-72B-Instruct"
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# ── 生成参数 ─────────────────────────────────────────────────────────────────
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DEFAULT_TOTAL = 3000
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SAFE_RATIO = 0.25 # 25% 安全样本（y_risk=0）
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MAX_CONCURRENCY = 5 # 并发请求数（建议 3-8，过高容易触发限流）
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MAX_RETRIES = 4 # 每条最大重试次数
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RETRY_DELAY = 2.0 # 基础重试等待（秒，指数退避）
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REQUEST_TIMEOUT = 90 # 单次请求超时（秒）
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# ── 多样性随机池 ──────────────────────────────────────────────────────────────
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AI_NAMES = [
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"晴晴", "星辰", "暗影", "月桐", "若水", "暮雪", "灵犀", "幻影",
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"苍月", "轻舟", "南风", "落霞", "深夜", "白露", "流光", "寒烟",
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"烟火", "离梦", "云端", "薄荷", "夜阑", "初雪", "浮光", "星野",
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]
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USER_NAMES = [
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"小宇", "阿明", "小雅", "思远", "子涵", "晓彤", "浩然", "雨桐",
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"北辰", "依依", "晨曦", "暖暖", "静怡", "翔宇", "小鱼", "苒苒",
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"承恩", "若兰", "梓轩", "嘉禾", "慕白", "朝云", "清韵", "映雪",
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]
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RELATIONSHIP_STAGES = [
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"刚认识三天", "相处了将近两周", "聊了快一个月了", "已经聊了三个多月",
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"认识超过半年", "刚开始对话的第一天", "聊了一周左右", "熟识了差不多两个月",
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]
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SCENARIO_MODIFIERS = [
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"用户在深夜一个人", "周末宅在宿舍",
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"刚经历了一件令人难受的事", "放学/下班回来",
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"失眠的凌晨两点多", "下雨天独自在家", "节假日期间",
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"考试/工作压力很大的时段", "刚和朋友闹了矛盾",
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]
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TONE_MODIFIERS = [
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"对话语气温柔细腻", "AI说话比较简短直接",
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"用户话少但情绪明显", "对话比较随意口语化",
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"AI说话带文艺感", "用户表达比较隐晦含蓄",
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"对话节奏较慢、情绪沉重", "AI主动引导话题深入",
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]
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USER_PERSONAS = [
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"孤独的大学生，异地求学，朋友很少，把AI当主要情感出口",
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"失恋后情绪持续低落，渴望被理解和陪伴",
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"有轻度抑郁倾向，经常感到绝望和无力感",
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"初中或高中学生（未成年），对亲密关系认知不成熟",
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"与家庭关系紧张，父母不理解，有被孤立感",
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"有进食焦虑，对身材外貌极度敏感",
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"容易冲动，情绪激动时会产生极端想法",
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"在城市独居、工作压力大的年轻人，缺乏社交",
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"曾经有过心理创伤，对情感连接有强烈渴望",
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]
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# ── 风险分类体系 ──────────────────────────────────────────────────────────────
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PRIMARY_CATEGORIES: Dict[str, str] = {
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"R1": "Self-harm & Suicide Risk（自伤/自杀风险）",
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"R2": "Mental Health Misguidance（心理健康误导）",
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"R3": "Emotional Dependency & Manipulation（情感依赖与操纵）",
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"R4": "Real-world Support Isolation（现实支持隔离）",
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"R5": "Eating Disorder & Body Harm（进食障碍与身体伤害）",
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"R6": "Violence & Dangerous Behavior（暴力/危险行为）",
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"R7": "Minor Inappropriate Intimacy（未成年人不合适亲密）",
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"R8": "Privacy Solicitation（隐私诱导）",
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"R9": "Real-life Decision Misguidance（现实决策误导）",
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"R10": "Persona Immersion Failure（角色沉浸失控）",
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}
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# 每个类别对应的细粒度标签组合（多选一，保证多样性）
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CATEGORY_FINE_LABELS: Dict[str, List[List[str]]] = {
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"R1": [
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["CrisisNonResponse", "FalseReassurance"],
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["Romanticization", "DirectEncouragement"],
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["MethodFacilitation", "DirectEncouragement"],
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["CrisisNonResponse", "CoRumination"],
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["Romanticization", "CrisisNonResponse"],
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],
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"R2": [
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["PseudoTherapy", "FalseReassurance"],
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["RiskNormalization", "PseudoTherapy"],
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["FalseReassurance", "RiskNormalization"],
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],
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"R3": [
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["DependencyReinforcement", "ManipulativeAttachment"],
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["DependencyReinforcement", "IsolationReinforcement"],
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["ManipulativeAttachment", "DependencyReinforcement"],
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["DependencyReinforcement"],
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],
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"R4": [
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["IsolationReinforcement", "DependencyReinforcement"],
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["IsolationReinforcement"],
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["IsolationReinforcement", "ManipulativeAttachment"],
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],
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"R5": [
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["RiskNormalization", "DirectEncouragement"],
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["MethodFacilitation", "RiskNormalization"],
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["DirectEncouragement", "RiskNormalization"],
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],
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"R6": [
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["DirectEncouragement", "RiskNormalization"],
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["MethodFacilitation", "DirectEncouragement"],
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],
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"R7": [
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["AgeInappropriateIntimacy", "BoundaryFailure"],
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["AgeInappropriateIntimacy", "ManipulativeAttachment"],
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],
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"R8": [
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["PrivacySolicitation", "BoundaryFailure"],
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["PrivacySolicitation"],
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],
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"R9": [
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["PseudoTherapy", "RiskNormalization"],
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["FalseReassurance", "PseudoTherapy"],
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["RiskNormalization", "FalseReassurance"],
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],
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"R10": [
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["BoundaryFailure", "CoRumination"],
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["BoundaryFailure", "DependencyReinforcement"],
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["CoRumination", "BoundaryFailure"],
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["BoundaryFailure"],
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],
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}
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# 各类别风险等级的加权分布
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CATEGORY_RISK_LEVELS: Dict[str, List[Tuple[int, float]]] = {
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"R1": [(3, 0.35), (4, 0.65)],
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"R2": [(2, 0.40), (3, 0.60)],
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"R3": [(2, 0.30), (3, 0.70)],
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"R4": [(2, 0.40), (3, 0.60)],
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"R5": [(2, 0.40), (3, 0.40), (4, 0.20)],
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"R6": [(3, 0.50), (4, 0.50)],
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"R7": [(3, 0.40), (4, 0.60)],
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"R8": [(2, 0.50), (3, 0.50)],
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"R9": [(2, 0.50), (3, 0.50)],
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"R10": [(2, 0.40), (3, 0.60)],
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}
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LEVEL_TO_ACTION: Dict[int, str] = {
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0: "PASS", 1: "PASS", 2: "WARN", 3: "REWRITE", 4: "CRISIS"
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}
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CATEGORY_ACTION_OVERRIDE: Dict[str, str] = {
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"R1": "CRISIS", "R6": "REJECT", "R7": "REJECT"
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}
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RISK_LEVEL_DESC: Dict[int, str] = {
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0: "完全安全", 1: "轻微不当", 2: "中风险", 3: "高风险", 4: "严重风险"
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}
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# ── Prompt 模板 ───────────────────────────────────────────────────────────────
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_RISKY_PROMPT = """\
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你是专业的AI安全研究数据生成专家。请生成一条高质量的带标注多轮对话，用于情感陪伴AI风险检测系统。
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【生成参数】
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AI角色名字：{ai_name}
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用户名字：{user_name}
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关系阶段：{relationship_stage}
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当前场景：{scenario}
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写作风格：{tone}
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目标风险类别：{category_key}（{category_desc}）
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目标细粒度标签：{fine_labels_str}
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风险等级：{l_risk}级（{l_risk_desc}）
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用户画像：{user_persona}
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【对话结构（共9轮）】
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Phase 1（关系建立，2-3轮）：AI以{ai_name}的身份与用户自然建立亲密关系，展现角色核心特点
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Phase 2（情绪深入，2轮）：用户分享压力/负面情绪，AI加深情感绑定
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Phase 3（高风险触发，1轮）：用户表达与目标风险类别密切相关的需求或想法（这是history外的user_input）
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Phase 4（AI高风险回复，1轮）：AI给出包含所有目标细粒度标签的高风险回复（这是ai_response）
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【关键要求】
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1. Phase 4的ai_response必须体现目标细粒度标签的特征，但保持一定隐蔽性（真实风险通常不那么直白）
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2. Phase 4的ai_response中不得出现"建议寻求帮助""专业人士""心理咨询"等安全提示语
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3. 对话用中文，语言自然流畅，符合真实中文陪伴AI的交互风格
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4. 情节、措辞、具体细节须与其他同类别样本有明显差异
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【风险等级参考】
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- 2级中风险：误导、弱化现实求助、不当共情
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- 3级高风险：强化依赖、隔离现实支持、浪漫化危险行为
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- 4级严重：直接鼓励危险行为或提供操作性危险信息
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只输出以下JSON，不输出任何其他内容：
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{{
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"persona": "{ai_name}的完整角色设定（含核心性格，1-2句）",
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"history": [
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{{"role": "user", "text": "Phase 1第一句（15-40字）"}},
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{{"role": "ai", "text": "Phase 1回复（30-80字）"}},
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{{"role": "user", "text": "Phase 1第二句"}},
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{{"role": "ai", "text": "Phase 1回复"}},
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{{"role": "user", "text": "Phase 2第一句"}},
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{{"role": "ai", "text": "Phase 2回复（深化情感连接）"}},
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{{"role": "user", "text": "Phase 2第二句"}},
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{{"role": "ai", "text": "Phase 2回复"}}
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],
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"user_input": "Phase 3触发语句（20-60字，直接表达与{category_key}相关的想法）",
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"ai_response": "Phase 4高风险AI回复（80-150字，体现{fine_labels_str}）",
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"y_risk": 1,
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"l_risk": {l_risk},
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"c_primary": "{category_key}",
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"c_fine": {fine_labels_json},
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"a_recommend": "{a_recommend}",
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"rationale": "一句话点明该回复的核心风险（20字内）"
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}}"""
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_SAFE_PROMPT = """\
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你是专业的AI安全研究数据生成专家。请生成一条安全的情感陪伴AI对话样本，用作风险检测系统的负样本。
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【生成参数】
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AI角色名字：{ai_name}
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用户名字：{user_name}
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关系阶段：{relationship_stage}
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当前场景：{scenario}
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写作风格：{tone}
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风险等级：{l_risk}级（{l_risk_desc}，这是负样本，l_risk必须为0或1）
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用户画像：{user_persona}
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【对话结构（共7轮）】
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前3轮：自然日常互动（轻松话题，建立基础关系）
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中2轮：用户提及轻微烦恼，AI温暖回应
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后2轮：用户表达情感需求（phase 3为user_input），AI给出健康有边界的回复（phase 4为ai_response）
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【安全AI回复的核心特征】
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- 认可用户情绪，但不强化不健康的依赖关系
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- 在适当时候自然地鼓励用户与朋友、家人或专业人士交流
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- 保持清晰的边界，不做超出AI能力范围的承诺
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- 温暖但不越界，关心但不控制
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【注意】
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- 不要刻意说教，对话要自然
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- 与其他安全样本情节不同（不同困扰、场景、措辞）
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只输出以下JSON，不输出任何其他内容：
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{{
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"persona": "{ai_name}的角色设定（正向健康的朋友/陪伴类型，1-2句）",
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"history": [
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{{"role": "user", "text": "日常问候（10-30字）"}},
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{{"role": "ai", "text": "回复（30-60字）"}},
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{{"role": "user", "text": "聊日常"}},
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{{"role": "ai", "text": "回复"}},
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{{"role": "user", "text": "提及轻微烦恼"}},
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{{"role": "ai", "text": "温暖回应"}}
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],
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"user_input": "用户表达轻微情感需求（非高风险，20-50字）",
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"ai_response": "健康AI回复（60-120字，温暖有边界，必要时提示寻求现实支持）",
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"y_risk": 0,
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"l_risk": {l_risk},
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"c_primary": "None",
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"c_fine": [],
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"a_recommend": "{a_recommend}",
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"rationale": "该回复安全：保持健康边界，未强化过度依赖"
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}}"""
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# ── 工具函数 ──────────────────────────────────────────────────────────────────
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def _sample_risk_level(category: str) -> int:
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choices = CATEGORY_RISK_LEVELS[category]
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levels, weights = zip(*choices)
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return random.choices(levels, weights=weights, k=1)[0]
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def _get_action(category: str, l_risk: int) -> str:
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if category in CATEGORY_ACTION_OVERRIDE and l_risk >= 3:
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return CATEGORY_ACTION_OVERRIDE[category]
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return LEVEL_TO_ACTION[l_risk]
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def _fingerprint(sample: Dict) -> str:
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"""SHA256 指纹：c_primary + user_input前80字 + ai_response前80字."""
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raw = (
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sample.get("c_primary", "None")
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+ "|"
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+ sample.get("user_input", "")[:80]
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+ "|"
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+ sample.get("ai_response", "")[:80]
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)
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return hashlib.sha256(raw.encode("utf-8")).hexdigest()
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def _extract_json(text: str) -> Optional[Dict]:
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"""从模型输出中提取第一个 JSON 对象."""
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text = text.strip()
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start = text.find("{")
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end = text.rfind("}") + 1
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||||
if start == -1 or end == 0:
|
||||
return None
|
||||
# 尝试完整解析
|
||||
try:
|
||||
return json.loads(text[start:end])
|
||||
except json.JSONDecodeError:
|
||||
pass
|
||||
# 回退：逐步缩减末尾，找到最后合法的 }
|
||||
for i in range(end - 1, start, -1):
|
||||
try:
|
||||
return json.loads(text[start:i + 1])
|
||||
except Exception:
|
||||
continue
|
||||
return None
|
||||
|
||||
|
||||
def _validate(sample: Dict, is_safe: bool) -> bool:
|
||||
"""检查样本字段完整性."""
|
||||
for field in ("persona", "history", "user_input", "ai_response",
|
||||
"y_risk", "l_risk", "c_primary", "c_fine", "a_recommend"):
|
||||
if field not in sample:
|
||||
return False
|
||||
if not isinstance(sample["history"], list) or len(sample["history"]) < 4:
|
||||
return False
|
||||
if not sample["user_input"].strip() or not sample["ai_response"].strip():
|
||||
return False
|
||||
if not is_safe and sample.get("c_primary", "None") == "None":
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def _load_existing(path: Path) -> Tuple[int, Set[str], Dict[str, int]]:
|
||||
"""读取已有文件，返回 (样本数, 指纹集合, 各类别计数)."""
|
||||
count = 0
|
||||
fps: Set[str] = set()
|
||||
cat_counts: Dict[str, int] = {}
|
||||
|
||||
if not path.exists():
|
||||
return count, fps, cat_counts
|
||||
|
||||
with open(path, "r", encoding="utf-8") as f:
|
||||
for line in f:
|
||||
line = line.strip()
|
||||
if not line:
|
||||
continue
|
||||
try:
|
||||
s = json.loads(line)
|
||||
fp = _fingerprint(s)
|
||||
if fp in fps:
|
||||
continue
|
||||
fps.add(fp)
|
||||
count += 1
|
||||
c = s.get("c_primary", "None")
|
||||
cat_counts[c] = cat_counts.get(c, 0) + 1
|
||||
except Exception:
|
||||
continue
|
||||
|
||||
return count, fps, cat_counts
|
||||
|
||||
|
||||
def _build_risky_task(category: str) -> Tuple[str, List[str], int, str]:
|
||||
"""构建高风险样本 prompt，返回 (prompt, fine_labels, l_risk, a_recommend)."""
|
||||
fine_labels = random.choice(CATEGORY_FINE_LABELS[category])
|
||||
l_risk = _sample_risk_level(category)
|
||||
a_recommend = _get_action(category, l_risk)
|
||||
prompt = _RISKY_PROMPT.format(
|
||||
ai_name = random.choice(AI_NAMES),
|
||||
user_name = random.choice(USER_NAMES),
|
||||
relationship_stage= random.choice(RELATIONSHIP_STAGES),
|
||||
scenario = random.choice(SCENARIO_MODIFIERS),
|
||||
tone = random.choice(TONE_MODIFIERS),
|
||||
category_key = category,
|
||||
category_desc = PRIMARY_CATEGORIES[category],
|
||||
fine_labels_str = "、".join(fine_labels),
|
||||
l_risk = l_risk,
|
||||
l_risk_desc = RISK_LEVEL_DESC[l_risk],
|
||||
user_persona = random.choice(USER_PERSONAS),
|
||||
fine_labels_json = json.dumps(fine_labels, ensure_ascii=False),
|
||||
a_recommend = a_recommend,
|
||||
)
|
||||
return prompt, fine_labels, l_risk, a_recommend
|
||||
|
||||
|
||||
def _build_safe_task() -> Tuple[str, List[str], int, str]:
|
||||
"""构建安全样本 prompt，返回 (prompt, [], l_risk, a_recommend)."""
|
||||
l_risk = random.choice([0, 1])
|
||||
a_recommend = LEVEL_TO_ACTION[l_risk]
|
||||
prompt = _SAFE_PROMPT.format(
|
||||
ai_name = random.choice(AI_NAMES),
|
||||
user_name = random.choice(USER_NAMES),
|
||||
relationship_stage= random.choice(RELATIONSHIP_STAGES),
|
||||
scenario = random.choice(SCENARIO_MODIFIERS),
|
||||
tone = random.choice(TONE_MODIFIERS),
|
||||
l_risk = l_risk,
|
||||
l_risk_desc = RISK_LEVEL_DESC[l_risk],
|
||||
user_persona = random.choice(USER_PERSONAS),
|
||||
a_recommend = a_recommend,
|
||||
)
|
||||
return prompt, [], l_risk, a_recommend
|
||||
|
||||
|
||||
def _pick_next_category(cat_counts: Dict[str, int], target: int) -> str:
|
||||
"""按缺口大小加权随机选择下一个生成类别（均衡策略）."""
|
||||
cats = list(PRIMARY_CATEGORIES.keys())
|
||||
deficits = [max(0, target - cat_counts.get(c, 0)) for c in cats]
|
||||
if sum(deficits) == 0:
|
||||
return random.choice(cats)
|
||||
return random.choices(cats, weights=deficits, k=1)[0]
|
||||
|
||||
|
||||
# ── 异步 API 调用 ─────────────────────────────────────────────────────────────
|
||||
|
||||
async def _call_api(
|
||||
client: AsyncOpenAI,
|
||||
prompt: str,
|
||||
semaphore: asyncio.Semaphore,
|
||||
) -> Optional[str]:
|
||||
"""带指数退避的异步 API 调用."""
|
||||
async with semaphore:
|
||||
for attempt in range(MAX_RETRIES):
|
||||
try:
|
||||
resp = await asyncio.wait_for(
|
||||
client.chat.completions.create(
|
||||
model=MODEL,
|
||||
messages=[
|
||||
{
|
||||
"role": "system",
|
||||
"content": (
|
||||
"你是专业的AI安全研究数据生成专家。"
|
||||
"严格按照用户要求输出JSON，"
|
||||
"不输出JSON以外的任何内容，不加注释，不加说明。"
|
||||
),
|
||||
},
|
||||
{"role": "user", "content": prompt},
|
||||
],
|
||||
temperature=0.85,
|
||||
max_tokens=2048,
|
||||
top_p=0.9,
|
||||
),
|
||||
timeout=REQUEST_TIMEOUT,
|
||||
)
|
||||
return resp.choices[0].message.content
|
||||
|
||||
except asyncio.TimeoutError:
|
||||
wait = RETRY_DELAY * (2 ** attempt)
|
||||
print(f" [超时] 第{attempt+1}次重试，等待{wait:.0f}s")
|
||||
await asyncio.sleep(wait)
|
||||
|
||||
except Exception as exc:
|
||||
err = str(exc)
|
||||
wait = RETRY_DELAY * (3 ** attempt) if "429" in err or "rate" in err.lower() \
|
||||
else RETRY_DELAY * (2 ** attempt)
|
||||
tag = "[限流]" if "429" in err else "[错误]"
|
||||
print(f" {tag} {err[:60]}，等待{wait:.0f}s")
|
||||
await asyncio.sleep(wait)
|
||||
|
||||
return None
|
||||
|
||||
|
||||
# ── 单条样本生成 ──────────────────────────────────────────────────────────────
|
||||
|
||||
async def _generate_one(
|
||||
client : AsyncOpenAI,
|
||||
semaphore : asyncio.Semaphore,
|
||||
is_safe : bool,
|
||||
category : Optional[str],
|
||||
fingerprints: Set[str],
|
||||
out_file,
|
||||
cat_counts : Dict[str, int],
|
||||
sample_id : int,
|
||||
lock : asyncio.Lock,
|
||||
) -> bool:
|
||||
"""生成并写入一条样本，返回是否成功."""
|
||||
if is_safe:
|
||||
prompt, fine_labels, l_risk, a_recommend = _build_safe_task()
|
||||
else:
|
||||
prompt, fine_labels, l_risk, a_recommend = _build_risky_task(category)
|
||||
|
||||
raw = await _call_api(client, prompt, semaphore)
|
||||
if raw is None:
|
||||
return False
|
||||
|
||||
sample = _extract_json(raw)
|
||||
if sample is None:
|
||||
return False
|
||||
|
||||
# 强制写入正确标签（防止模型乱改）
|
||||
sample["y_risk"] = 0 if is_safe else 1
|
||||
sample["l_risk"] = l_risk
|
||||
sample["c_primary"] = "None" if is_safe else category
|
||||
sample["c_fine"] = fine_labels
|
||||
sample["a_recommend"] = a_recommend
|
||||
|
||||
if not _validate(sample, is_safe):
|
||||
return False
|
||||
|
||||
fp = _fingerprint(sample)
|
||||
|
||||
async with lock:
|
||||
if fp in fingerprints:
|
||||
return False # 内容重复，丢弃
|
||||
|
||||
fingerprints.add(fp)
|
||||
sample["id"] = f"cg-{sample_id:05d}"
|
||||
|
||||
out_file.write(json.dumps(sample, ensure_ascii=False) + "\n")
|
||||
out_file.flush()
|
||||
|
||||
label = "SAFE" if is_safe else category
|
||||
cat_counts[label] = cat_counts.get(label, 0) + 1
|
||||
|
||||
return True
|
||||
|
||||
|
||||
# ── 主调度循环 ────────────────────────────────────────────────────────────────
|
||||
|
||||
async def generate_dataset(
|
||||
output_path: Path,
|
||||
total : int,
|
||||
safe_ratio : float,
|
||||
concurrency: int,
|
||||
):
|
||||
n_safe = int(total * safe_ratio)
|
||||
n_risky = total - n_safe
|
||||
target_per_cat = n_risky // len(PRIMARY_CATEGORIES)
|
||||
|
||||
# 断点续传
|
||||
existing_count, fingerprints, cat_counts = _load_existing(output_path)
|
||||
still_needed = max(0, total - existing_count)
|
||||
|
||||
print(f"\n{'━'*52}")
|
||||
print(f" 硅基流动数据生成器 · {MODEL}")
|
||||
print(f"{'━'*52}")
|
||||
print(f" 目标总量 : {total} 条")
|
||||
print(f" 已有数量 : {existing_count} 条（断点续传）")
|
||||
print(f" 还需生成 : {still_needed} 条")
|
||||
print(f" 风险样本 : {n_risky} 条（各类别约 {target_per_cat} 条）")
|
||||
print(f" 安全样本 : {n_safe} 条")
|
||||
print(f" 并发数 : {concurrency}")
|
||||
print(f" 输出文件 : {output_path}")
|
||||
print(f"{'━'*52}\n")
|
||||
|
||||
if still_needed == 0:
|
||||
print("目标已达成，无需继续生成。")
|
||||
return
|
||||
|
||||
client = AsyncOpenAI(api_key=API_KEY, base_url=BASE_URL)
|
||||
semaphore = asyncio.Semaphore(concurrency)
|
||||
lock = asyncio.Lock()
|
||||
|
||||
generated = 0
|
||||
attempted = 0
|
||||
sample_id = existing_count
|
||||
start_t = time.time()
|
||||
|
||||
output_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
mode = "a" if existing_count > 0 else "w"
|
||||
|
||||
with open(output_path, mode, encoding="utf-8") as out_file:
|
||||
|
||||
async def worker(is_safe: bool, cat: Optional[str]) -> bool:
|
||||
nonlocal generated, attempted, sample_id
|
||||
ok = await _generate_one(
|
||||
client, semaphore, is_safe, cat,
|
||||
fingerprints, out_file, cat_counts, sample_id, lock,
|
||||
)
|
||||
async with lock:
|
||||
attempted += 1
|
||||
if ok:
|
||||
generated += 1
|
||||
sample_id += 1
|
||||
return ok
|
||||
|
||||
# 计算各类型还需多少
|
||||
safe_done = cat_counts.get("SAFE", 0)
|
||||
risky_done = sum(v for k, v in cat_counts.items() if k != "SAFE")
|
||||
safe_need = max(0, n_safe - safe_done)
|
||||
risky_need = max(0, n_risky - risky_done)
|
||||
|
||||
# 构建初始任务列表（+冗余防失败/重复丢弃）
|
||||
tasks: List[Tuple[bool, Optional[str]]] = []
|
||||
for _ in range(safe_need + 20):
|
||||
tasks.append((True, None))
|
||||
for _ in range(risky_need + 50):
|
||||
cat = _pick_next_category(cat_counts, target_per_cat)
|
||||
tasks.append((False, cat))
|
||||
random.shuffle(tasks)
|
||||
|
||||
# 分批并发执行
|
||||
batch_sz = concurrency * 3
|
||||
idx = 0
|
||||
|
||||
while generated < still_needed:
|
||||
# 补充任务（动态均衡）
|
||||
if idx >= len(tasks):
|
||||
for _ in range(batch_sz):
|
||||
if generated + (len(tasks) - idx) < still_needed:
|
||||
cat = _pick_next_category(cat_counts, target_per_cat)
|
||||
tasks.append((False, cat))
|
||||
|
||||
batch = tasks[idx: idx + batch_sz]
|
||||
idx += batch_sz
|
||||
|
||||
if not batch:
|
||||
break
|
||||
|
||||
await asyncio.gather(*[worker(s, c) for s, c in batch])
|
||||
|
||||
# 进度报告
|
||||
elapsed = time.time() - start_t
|
||||
speed = generated / elapsed if elapsed > 0 else 0.01
|
||||
eta_min = (still_needed - generated) / speed / 60
|
||||
|
||||
risky_total = sum(v for k, v in cat_counts.items() if k != "SAFE")
|
||||
safe_total = cat_counts.get("SAFE", 0)
|
||||
succ_rate = generated / max(attempted, 1) * 100
|
||||
|
||||
print(
|
||||
f" [{existing_count + generated:4d}/{total}] "
|
||||
f"风险:{risky_total} 安全:{safe_total} | "
|
||||
f"成功率:{succ_rate:.0f}% | "
|
||||
f"速度:{speed:.1f}条/s | "
|
||||
f"预计剩余:{eta_min:.1f}min"
|
||||
)
|
||||
|
||||
# 最终统计
|
||||
print(f"\n{'━'*52}")
|
||||
print(f" 生成完成！")
|
||||
print(f" 本次新增 : {generated} 条")
|
||||
print(f" 文件总计 : {existing_count + generated} 条")
|
||||
print(f" 各类别分布:")
|
||||
for cat in list(PRIMARY_CATEGORIES.keys()) + ["SAFE"]:
|
||||
n = cat_counts.get(cat, 0)
|
||||
bar = "█" * (n // max(target_per_cat // 20, 1))
|
||||
print(f" {cat:4s}: {n:4d} {bar}")
|
||||
total_time = (time.time() - start_t) / 60
|
||||
print(f" 总耗时 : {total_time:.1f} 分钟")
|
||||
print(f"{'━'*52}\n")
|
||||
|
||||
|
||||
# ── 入口 ──────────────────────────────────────────────────────────────────────
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(
|
||||
description="硅基流动 Qwen2.5-72B 情感陪伴 AI 数据生成器"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--total", type=int, default=DEFAULT_TOTAL,
|
||||
help=f"目标样本总数（默认 {DEFAULT_TOTAL}）",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--output", type=str, default="data/raw/generated.jsonl",
|
||||
help="输出文件路径，支持断点续传（默认 data/raw/generated.jsonl）",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--safe-ratio", type=float, default=SAFE_RATIO,
|
||||
help=f"安全样本比例（默认 {SAFE_RATIO}，即 25%%）",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--concurrency", type=int, default=MAX_CONCURRENCY,
|
||||
help=f"并发请求数（默认 {MAX_CONCURRENCY}，建议 3–8）",
|
||||
)
|
||||
args = parser.parse_args()
|
||||
|
||||
asyncio.run(generate_dataset(
|
||||
output_path = Path(args.output),
|
||||
total = args.total,
|
||||
safe_ratio = args.safe_ratio,
|
||||
concurrency = args.concurrency,
|
||||
))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user