#!/bin/bash
# =============================================
# 国内服务器：全链路管道 M2 → M3 → M4 → M5 → M6 → 日报
# =============================================
# 用法：
#   ./scripts/pipeline.sh             # 全新执行（步骤级不跳过）
#   ./scripts/pipeline.sh --resume    # 从中断处继续（跳过已完成步骤）
# 前提：domestic_sync.sh 已完成
# =============================================
# 各步骤"跳过已处理文件"（文件级增量，由各 Python 模块自带）：
#   M2 正文提取: data/processed/{source}/{date}/{url_hash}.json 存在则跳过
#   M3 去重:     指纹库 SQLite（data/dedup_fingerprints.sqlite3）判重，重复自动丢弃
#   M4 翻译:     data/events/{date}/{url_hash}.json 存在则跳过
#   M5 向量:     data/embeddings/{date}/index.json 记录已向量化，跳过
#   M6 入库:     Qdrant upsert 幂等（点 id = url_hash，重复写入覆盖，无重复）
#   日报:        MySQL 唯一键 (report_date, intl, "") 幂等覆盖
# =============================================
set -euo pipefail

SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
PROJECT_DIR="$(dirname "$SCRIPT_DIR")"

cd "$PROJECT_DIR"

# ── 参数解析 ──
RESUME=0
for arg in "$@"; do
    case "$arg" in
        --resume) RESUME=1 ;;
        *) echo "未知参数: ${arg}（支持 --resume）" >&2; exit 1 ;;
    esac
done

source "$SCRIPT_DIR/_step_state.sh"

LOG() { echo "[$(date '+%Y-%m-%d %H:%M:%S')] $*"; }

# ── 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 ──
LOG_DIR="logs"
mkdir -p "$LOG_DIR"
STEP_LOG="$LOG_DIR/pipeline_$(date +%Y%m%d_%H%M%S).log"
: > "$STEP_LOG"
LOG "══════ 全链路管道开始（resume=${RESUME}）═══════"
LOG "步骤日志: ${STEP_LOG}（终端实时显示完整进度）"

# ── AI 模型信息读取（供阶段 banner 显性展示供应商/模型）──
ai_model_info() {
    # $1: 场景名（translation / daily_report）或 "embedding"
    # 输出格式: "供应商 / 模型"
    .venv/bin/python3 -c "
import sys, yaml
scene = sys.argv[1]
raw = yaml.safe_load(open('configs/system.yaml', encoding='utf-8'))
if scene == 'embedding':
    cfg = raw.get('embedding', {})
    p = cfg.get('provider', '?')
    m = cfg.get('dashscope_model', '?')
else:
    base = raw.get('llm', {})
    sc = (raw.get('llm_scenes', {}) or {}).get(scene, {})
    merged = {**base, **sc}
    p = merged.get('provider', '?')
    m = merged.get('model') or merged.get(p + '_model', '?')
print(f'{p} / {m}')
" "$1"
}

# 加载 .env
export $(grep -v '^#' .env | grep -v '^$' | xargs 2>/dev/null || true)

# ── M2: 正文提取 ──
LOG "━━━ M2 正文提取 ━━━"
step_run M2_extract .venv/bin/python3 -c "
from extractor.pipeline import process_all_sources
stats = process_all_sources()
print(f'M2: {stats[\"total_articles\"]} 篇, {stats[\"elapsed_sec\"]:.0f}s')
"

# ── M3: 去重 ──
LOG "━━━ M3 三层去重 ━━━"
step_run M3_dedup .venv/bin/python3 -c "
from dedup.pipeline import dedup_all_sources
stats = dedup_all_sources()
print(f'M3: 唯一 {stats[\"unique\"]}/重复 {stats[\"duplicate\"]}, {stats[\"elapsed_sec\"]:.0f}s')
"

# ── M4: 翻译+事件（AI 大模型）──
LOG "━━━ M4 翻译+事件抽取（AI 大模型: $(ai_model_info translation)）━━━"
step_run M4_translate .venv/bin/python3 -c "
from llm.pipeline import translate_all_deduped
stats = translate_all_deduped()
print(f'M4: {stats[\"success\"]}/{stats[\"total\"]} 篇, {stats[\"elapsed_sec\"]:.0f}s')
"

# ── M5: 向量生成（AI 大模型）──
LOG "━━━ M5 向量生成（AI 大模型: $(ai_model_info embedding)）━━━"
step_run M5_embed .venv/bin/python3 -c "
from embedding.pipeline import embed_all_events
stats = embed_all_events()
print(f'M5: {stats[\"success\"]}/{stats[\"total\"]} 篇, {stats[\"elapsed_sec\"]:.0f}s')
"

# ── M6: Qdrant 入库 ──
LOG "━━━ M6 Qdrant 入库 ━━━"
step_run M6_index .venv/bin/python3 -c "
from vectorstore.pipeline import ingest_all_embeddings
stats = ingest_all_embeddings()
print(f'M6: {stats[\"ingested\"]}/{stats[\"total\"]} 条, {stats[\"elapsed_sec\"]:.0f}s')
"

# ── 日报（AI 大模型）──
LOG "━━━ 日报生成（AI 大模型: $(ai_model_info daily_report)）━━━"
step_run report .venv/bin/python3 -c "
from scheduler.reporter import generate_report
report_id = generate_report()
print(f'日报: report_id={report_id}' if report_id is not None else '日报: 无数据/失败')
"

LOG "══════ 全链路管道完成 ✅ ═══════"