simon
012cb615d3
- pipeline.sh 每阶段 banner（━━━ M4 翻译+事件抽取（AI 大模型: deepseek / deepseek-v4-flash）━━━） - ai_model_info() 从 system.yaml 读取场景模型（translation/daily_report/embedding） - Python 层：M4/M5/日报 显性打印 provider/model（场景标注） - 已同步 pi5 实测：客户端初始化日志含供应商/模型
120 lines
4.6 KiB
Bash
Executable File
120 lines
4.6 KiB
Bash
Executable File
#!/bin/bash
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# =============================================
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# 国内服务器：全链路管道 M2 → M3 → M4 → M5 → M6 → 日报
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# =============================================
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# 用法：
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# ./scripts/pipeline.sh # 全新执行（步骤级不跳过）
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# ./scripts/pipeline.sh --resume # 从中断处继续（跳过已完成步骤）
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# 前提：domestic_sync.sh 已完成
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# =============================================
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# 各步骤"跳过已处理文件"（文件级增量，由各 Python 模块自带）：
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# M2 正文提取: data/processed/{source}/{date}/{url_hash}.json 存在则跳过
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# M3 去重: 指纹库 SQLite（data/dedup_fingerprints.sqlite3）判重，重复自动丢弃
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# M4 翻译: data/events/{date}/{url_hash}.json 存在则跳过
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# M5 向量: data/embeddings/{date}/index.json 记录已向量化，跳过
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# M6 入库: Qdrant upsert 幂等（点 id = url_hash，重复写入覆盖，无重复）
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# 日报: MySQL 唯一键 (report_date, intl, "") 幂等覆盖
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# =============================================
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set -euo pipefail
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SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
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PROJECT_DIR="$(dirname "$SCRIPT_DIR")"
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cd "$PROJECT_DIR"
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# ── 参数解析 ──
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RESUME=0
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for arg in "$@"; do
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case "$arg" in
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--resume) RESUME=1 ;;
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*) echo "未知参数: ${arg}（支持 --resume）" >&2; exit 1 ;;
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esac
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done
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source "$SCRIPT_DIR/_step_state.sh"
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LOG() { echo "[$(date '+%Y-%m-%d %H:%M:%S')] $*"; }
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# ── 步骤日志：全部输出实时显示到终端，同时完整写入日志文件 ──
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LOG_DIR="logs"
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mkdir -p "$LOG_DIR"
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STEP_LOG="$LOG_DIR/pipeline_$(date +%Y%m%d_%H%M%S).log"
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: > "$STEP_LOG"
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LOG "══════ 全链路管道开始（resume=${RESUME}）═══════"
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LOG "步骤日志: ${STEP_LOG}（终端实时显示完整进度）"
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# ── AI 模型信息读取（供阶段 banner 显性展示供应商/模型）──
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ai_model_info() {
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# $1: 场景名（translation / daily_report）或 "embedding"
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# 输出格式: "供应商 / 模型"
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.venv/bin/python3 -c "
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import sys, yaml
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scene = sys.argv[1]
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raw = yaml.safe_load(open('configs/system.yaml', encoding='utf-8'))
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if scene == 'embedding':
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cfg = raw.get('embedding', {})
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p = cfg.get('provider', '?')
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m = cfg.get('dashscope_model', '?')
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else:
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base = raw.get('llm', {})
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sc = (raw.get('llm_scenes', {}) or {}).get(scene, {})
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merged = {**base, **sc}
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p = merged.get('provider', '?')
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m = merged.get('model') or merged.get(p + '_model', '?')
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print(f'{p} / {m}')
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" "$1"
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}
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# 加载 .env
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export $(grep -v '^#' .env | grep -v '^$' | xargs 2>/dev/null || true)
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# ── M2: 正文提取 ──
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LOG "━━━ M2 正文提取 ━━━"
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step_run M2_extract .venv/bin/python3 -c "
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from extractor.pipeline import process_all_sources
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stats = process_all_sources()
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print(f'M2: {stats[\"total_articles\"]} 篇, {stats[\"elapsed_sec\"]:.0f}s')
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"
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# ── M3: 去重 ──
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LOG "━━━ M3 三层去重 ━━━"
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step_run M3_dedup .venv/bin/python3 -c "
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from dedup.pipeline import dedup_all_sources
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stats = dedup_all_sources()
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print(f'M3: 唯一 {stats[\"unique\"]}/重复 {stats[\"duplicate\"]}, {stats[\"elapsed_sec\"]:.0f}s')
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"
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# ── M4: 翻译+事件（AI 大模型）──
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LOG "━━━ M4 翻译+事件抽取（AI 大模型: $(ai_model_info translation)）━━━"
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step_run M4_translate .venv/bin/python3 -c "
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from llm.pipeline import translate_all_deduped
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stats = translate_all_deduped()
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print(f'M4: {stats[\"success\"]}/{stats[\"total\"]} 篇, {stats[\"elapsed_sec\"]:.0f}s')
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"
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# ── M5: 向量生成（AI 大模型）──
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LOG "━━━ M5 向量生成（AI 大模型: $(ai_model_info embedding)）━━━"
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step_run M5_embed .venv/bin/python3 -c "
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from embedding.pipeline import embed_all_events
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stats = embed_all_events()
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print(f'M5: {stats[\"success\"]}/{stats[\"total\"]} 篇, {stats[\"elapsed_sec\"]:.0f}s')
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"
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# ── M6: Qdrant 入库 ──
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LOG "━━━ M6 Qdrant 入库 ━━━"
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step_run M6_index .venv/bin/python3 -c "
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from vectorstore.pipeline import ingest_all_embeddings
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stats = ingest_all_embeddings()
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print(f'M6: {stats[\"ingested\"]}/{stats[\"total\"]} 条, {stats[\"elapsed_sec\"]:.0f}s')
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"
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# ── 日报（AI 大模型）──
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LOG "━━━ 日报生成（AI 大模型: $(ai_model_info daily_report)）━━━"
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step_run report .venv/bin/python3 -c "
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from scheduler.reporter import generate_report
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report_id = generate_report()
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print(f'日报: report_id={report_id}' if report_id is not None else '日报: 无数据/失败')
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"
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LOG "══════ 全链路管道完成 ✅ ═══════"
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