Data-Commerce-Agent/start.sh at main · EVEDensity/Data-Commerce-Agent · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
#!/bin/bash
# ============================================================
# shopkeeper-agent 一键启动脚本
# 首次运行会执行完整初始化，后续启动只会重启服务
# ============================================================
set -e

COMPOSE_FILE="docker/docker-compose.yaml"
ENV_FILE=".env"

echo "========================================="
echo "  shopkeeper-agent 启动中..."
echo "========================================="

# ---------- Step 1: 启动 Docker 基础服务 ----------
echo ""
echo "[1/5] 启动 Docker 基础服务 (MySQL/ES/Qdrant/Redis/Embedding)..."
docker compose -f "$COMPOSE_FILE" --env-file "$ENV_FILE" up -d
echo "  ✅ Docker 服务已启动"

# ---------- Step 2: 安装后端依赖（首次） ----------
echo ""
echo "[2/5] 检查后端依赖..."
if [ ! -d ".venv/lib" ] 2>/dev/null && [ ! -d ".venv/Lib" ] 2>/dev/null && [ ! -d ".venv/lib64" ] 2>/dev/null; then
    echo "  首次运行，安装依赖中..."
    uv sync
    echo "  ✅ 依赖安装完成"
else
    echo "  ✅ 依赖已安装，跳过"
fi

# ---------- Step 3: 下载 Embedding 模型（首次） ----------
echo ""
echo "[3/5] 检查 Embedding 模型..."
if [ ! -f "docker/embedding/bge-large-zh-v1.5/pytorch_model.bin" ]; then
    echo "  首次运行，下载模型中（约 1.2GB）..."
    uv run hf download BAAI/bge-large-zh-v1.5 --local-dir docker/embedding/bge-large-zh-v1.5
    echo "  ✅ 模型下载完成"
else
    echo "  ✅ 模型已存在，跳过"
fi

# ---------- Step 4: 等待服务就绪 ----------
echo ""
echo "[4/5] 等待基础服务就绪..."
echo "  等待 MySQL..."
until docker compose -f "$COMPOSE_FILE" --env-file "$ENV_FILE" exec -T mysql mysqladmin ping -h127.0.0.1 -uroot -p"${MYSQL_ROOT_PASSWORD}" --silent 2>/dev/null; do
    sleep 2
done
echo "  ✅ MySQL 就绪"

echo "  等待 Qdrant..."
until curl -sf http://localhost:7333/health > /dev/null 2>&1; do sleep 2; done
echo "  ✅ Qdrant 就绪"

echo "  等待 Elasticsearch..."
until curl -sf http://localhost:10200/_cluster/health > /dev/null 2>&1; do sleep 2; done
echo "  ✅ Elasticsearch 就绪"

echo "  等待 Embedding..."
until curl -sf http://localhost:9081/health > /dev/null 2>&1; do sleep 2; done
echo "  ✅ Embedding 就绪"

# ---------- Step 5: 构建知识库（首次或强制） ----------
echo ""
echo "[5/5] 构建元数据知识库..."
uv run python -m app.scripts.build_meta_knowledge -c conf/meta_config.yaml
echo "  ✅ 知识库构建完成"

# ---------- 启动后端 ----------
echo ""
echo "========================================="
echo "  基础服务全部就绪，启动后端服务..."
echo "========================================="
echo ""
echo "  后端地址: http://127.0.0.1:9000"
echo "  API 文档: http://127.0.0.1:9000/docs"
echo "  健康检查: http://127.0.0.1:9000/health"
echo ""
echo "  启动前端: cd frontend && pnpm install && pnpm dev"
echo ""

uv run fastapi dev main.py --port 9000