#!/bin/bash
  
  # Xinference REST API 调用示例
  # 演示如何通过 HTTP API 调用 Qwen3-Embedding 和 Qwen3-Reranker
  
  # 设置服务地址
  XINFERENCE_HOST="http://localhost:9997"
  MODEL_EMBEDDING="qwen3-embedding"
  MODEL_RERANKER="qwen3-reranker"
  
  echo "========================================="
  echo "  Xinference REST API 调用示例"
  echo "========================================="
  echo ""
  
  # 颜色定义
  GREEN='\033[0;32m'
  BLUE='\033[0;34m'
  YELLOW='\033[1;33m'
  NC='\033[0m' # No Color
  
  print_section() {
      echo ""
      echo -e "${BLUE}=========================================${NC}"
      echo -e "${BLUE}  $1${NC}"
      echo -e "${BLUE}=========================================${NC}"
      echo ""
  }
  
  print_info() {
      echo -e "${GREEN}➜${NC} $1"
  }
  
  # ============================================
  # 1. 查看服务状态
  # ============================================
  print_section "1. 查看服务状态和已部署模型"
  
  print_info "查看所有已部署的模型:"
  curl -s "${XINFERENCE_HOST}/v1/models" | python3 -m json.tool
  
  echo ""
  print_info "查看服务健康状态:"
  curl -s "${XINFERENCE_HOST}/v1/models" > /dev/null && echo "✅ 服务健康" || echo "❌ 服务异常"
  
  # ============================================
  # 2. Embedding API 调用
  # ============================================
  print_section "2. Qwen3-Embedding API 调用"
  
  print_info "单个文本 embedding 生成:"
  curl -X POST "${XINFERENCE_HOST}/v1/embeddings" \
    -H "Content-Type: application/json" \
    -d '{
      "model": "'${MODEL_EMBEDDING}'",
      "input": ["适合老人用的智能手机大屏幕长续航"]
    }' | python3 -m json.tool
  
  echo ""
  print_info "批量文本 embedding 生成:"
  curl -X POST "${XINFERENCE_HOST}/v1/embeddings" \
    -H "Content-Type: application/json" \
    -d '{
      "model": "'${MODEL_EMBEDDING}'",
      "input": [
        "红米Note12 5000mAh大电量",
        "华为畅享60 6000mAh超长续航",
        "小米手环8 智能运动监测"
      ]
    }' | python3 -m json.tool
  
  # ============================================
  # 3. Reranker API 调用
  # ============================================
  print_section "3. Qwen3-Reranker API 调用"
  
  print_info "精排候选商品:"
  
  curl -X POST "${XINFERENCE_HOST}/v1/rerank" \
    -H "Content-Type: application/json" \
    -d '{
      "model": "'${MODEL_RERANKER}'",
      "query": "适合老人用的智能手机大屏幕长续航",
      "documents": [
        "红米Note12 5000mAh大电量 6.67英寸大屏 老人模式",
        "iPhone 15 Pro Max 专业摄影旗舰",
        "华为畅享60 6000mAh超长续航 护眼大屏",
        "OPPO A1 5000mAh电池 简易模式适合长辈",
        "小米手环8 智能运动监测"
      ],
      "top_n": 5
    }' | python3 -m json.tool
  
  # ============================================
  # 4. 电商搜索实战：两阶段检索
  # ============================================
  print_section "4. 电商搜索实战：完整两阶段检索流程"
  
  # 阶段1: 密集检索
  print_info "阶段1: 为用户 query 生成向量"
  echo ""
  echo "Query: 适合老人用的智能手机大屏幕长续航"
  
  QUERY_VECTOR=$(curl -s -X POST "${XINFERENCE_HOST}/v1/embeddings" \
    -H "Content-Type: application/json" \
    -d '{
      "model": "'${MODEL_EMBEDDING}'",
      "input": ["适合老人用的智能手机大屏幕长续航"]
    }' | python3 -c "import sys, json; print(json.dumps(json.load(sys.stdin)['data'][0]['embedding'])))")
  
  echo "Query 向量维度: $(echo $QUERY_VECTOR | python3 -c "import sys, json; print(len(json.load(sys.stdin)))")"
  
  echo ""
  print_info "为候选商品生成向量（在实际应用中，这些向量应预计算并存储）"
  
  # 为简化演示，这里只显示部分候选商品的向量生成
  CANDIDATES=(
    "红米Note12 5000mAh大电量"
    "华为畅享60 6000mAh超长续航"
    "小米手环8"
  )
  
  for candidate in "${CANDIDATES[@]}"; do
    echo ""
    echo "商品: $candidate"
    curl -s -X POST "${XINFERENCE_HOST}/v1/embeddings" \
      -H "Content-Type: application/json" \
      -d '{
        "model": "'${MODEL_EMBEDDING}'",
        "input": ["'${candidate}'"]
      }' | python3 -c "import sys, json; data=json.load(sys.stdin); print(f\"  向量维度: {len(data['data'][0]['embedding'])}\")"
  done
  
  # 阶段2: 精排
  echo ""
  print_info "阶段2: 使用 Reranker 对召回结果进行精排"
  
  curl -X POST "${XINFERENCE_HOST}/v1/rerank" \
    -H "Content-Type: application/json" \
    -d '{
      "model": "'${MODEL_RERANKER}'",
      "query": "适合老人用的智能手机大屏幕长续航",
      "documents": [
        "红米Note12 5000mAh大电量 6.67英寸大屏 老人模式",
        "华为畅享60 6000mAh超长续航 护眼大屏",
        "OPPO A1 5000mAh电池 简易模式适合长辈",
        "iPhone 15 Pro Max 专业摄影旗舰",
        "小米手环8 智能运动监测"
      ],
      "top_n": 3
    }' | python3 -m json.tool
  
  # ============================================
  # 5. 高级用法：批量处理
  # ============================================
  print_section "5. 批量 Embedding 生成（离线任务）"
  
  print_info "为大量商品生成 embedding（模拟离线任务）"
  echo "注意: 实际生产环境中，批量大小建议为 100-1000"
  
  # 创建批量输入文件
  cat > /tmp/batch_input.json <<EOF
  {
    "model": "${MODEL_EMBEDDING}",
    "input": [
      "商品1: 红米Note12 5000mAh大电量 6.67英寸大屏",
      "商品2: iPhone 15 Pro Max 专业摄影旗舰",
      "商品3: 华为畅享60 6000mAh超长续航 护眼大屏",
      "商品4: OPPO A1 5000mAh电池 简易模式适合长辈",
      "商品5: 小米手环8 智能运动监测",
      "商品6: vivo Y78 5000mAh大电池 120Hz高刷屏",
      "商品7: 三星Galaxy A54 5000mAh 防水防尘",
      "商品8: 荣耀Play7T 6000mAh巨量电池",
      "商品9: 真我11 Pro 2亿像素 100W快充",
      "商品10: 诺基亚C31 5050mAh电池 耐用三防"
    ]
  }
  EOF
  
  echo ""
  echo "批量生成 10 个商品的 embedding..."
  curl -X POST "${XINFERENCE_HOST}/v1/embeddings" \
    -H "Content-Type: application/json" \
    -d @/tmp/batch_input.json | python3 -c "import sys, json; data=json.load(sys.stdin); print(f'✅ 成功生成 {len(data[\"data\"])} 个向量'); print(f'向量维度: {len(data[\"data\"][0][\"embedding\"])}')"
  
  # ============================================
  # 6. Python 调用示例
  # ============================================
  print_section "6. Python 调用示例"
  
  cat << 'EOF'
  
  # Python 调用示例代码:
  
  import requests
  import numpy as np
  
  XINFERENCE_HOST = "http://localhost:9997"
  
  # Embedding 调用
  def get_embedding(text: str) -> list:
      """获取文本的 embedding 向量"""
      response = requests.post(
          f"{XINFERENCE_HOST}/v1/embeddings",
          json={
              "model": "qwen3-embedding",
              "input": [text]
          }
      )
      return response.json()["data"][0]["embedding"]
  
  # 批量 Embedding
  def get_embeddings(texts: list) -> list:
      """批量获取 embedding 向量"""
      response = requests.post(
          f"{XINFERENCE_HOST}/v1/embeddings",
          json={
              "model": "qwen3-embedding",
              "input": texts
          }
      )
      return [item["embedding"] for item in response.json()["data"]]
  
  # Reranker 调用
  def rerank(query: str, documents: list, top_n: int = 10) -> list:
      """使用 reranker 对文档排序"""
      response = requests.post(
          f"{XINFERENCE_HOST}/v1/rerank",
          json={
              "model": "qwen3-reranker",
              "query": query,
              "documents": documents,
              "top_n": top_n
          }
      )
      return response.json()["results"]
  
  # 计算余弦相似度
  def cosine_similarity(vec1: list, vec2: list) -> float:
      """计算两个向量的余弦相似度"""
      v1 = np.array(vec1)
      v2 = np.array(vec2)
      return float(np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2)))
  
  # 完整搜索流程
  def search(query: str, products: list) -> list:
      """两阶段搜索"""
      # 阶段1: 密集检索（简化示例）
      query_vec = get_embedding(query)
      similarities = []
      for product in products:
          prod_vec = get_embedding(product)
          sim = cosine_similarity(query_vec, prod_vec)
          similarities.append((product, sim))
  
      # 取 Top-200
      similarities.sort(key=lambda x: x[1], reverse=True)
      top_200 = [p for p, s in similarities[:200]]
  
      # 阶段2: 精排
      reranked = rerank(query, top_200, top_n=10)
      return reranked
  
  # 使用示例
  if __name__ == "__main__":
      query = "适合老人用的智能手机大屏幕长续航"
      products = [
          "红米Note12 5000mAh大电量 6.67英寸大屏 老人模式",
          "华为畅享60 6000mAh超长续航 护眼大屏",
          "小米手环8 智能运动监测"
      ]
  
      results = search(query, products)
      for r in results:
          print(f"[{r['relevance_score']:.4f}] {r['document']}")
  
  EOF
  
  # ============================================
  # 7. 性能测试
  # ============================================
  print_section "7. 性能测试"
  
  print_info "测试 Embedding API 响应时间:"
  echo ""
  
  for i in {1..5}; do
    START=$(date +%s%N)
    curl -s -X POST "${XINFERENCE_HOST}/v1/embeddings" \
      -H "Content-Type: application/json" \
      -d '{
        "model": "'${MODEL_EMBEDDING}'",
        "input": ["测试文本"]
      }' > /dev/null
    END=$(date +%s%N)
    ELAPSED=$((($END - $START) / 1000000))
    echo "  请求 $i: ${ELAPSED}ms"
  done
  
  echo ""
  print_info "测试 Reranker API 响应时间:"
  
  for i in {1..5}; do
    START=$(date +%s%N)
    curl -s -X POST "${XINFERENCE_HOST}/v1/rerank" \
      -H "Content-Type: application/json" \
      -d '{
        "model": "'${MODEL_RERANKER}'",
        "query": "测试查询",
        "documents": ["文档1", "文档2", "文档3"],
        "top_n": 3
      }' > /dev/null
    END=$(date +%s%N)
    ELAPSED=$((($END - $START) / 1000000))
    echo "  请求 $i: ${ELAPSED}ms"
  done
  
  # ============================================
  # 8. 常见问题排查
  # ============================================
  print_section "8. 常见问题排查"
  
  print_info "检查服务是否运行:"
  curl -s "${XINFERENCE_HOST}/v1/models" > /dev/null && echo "✅ 服务正常" || echo "❌ 服务未启动，请运行: ./start.sh"
  
  echo ""
  print_info "检查模型是否部署:"
  MODELS=$(curl -s "${XINFERENCE_HOST}/v1/models")
  echo "$MODELS" | python3 -c "
  import sys, json
  try:
      models = json.load(sys.stdin)
      if models:
          print('✅ 已部署模型:')
          for m in models:
              print(f'   - {m.get(\"model_type\")}: {m.get(\"model_uid\")}')
      else:
          print('❌ 没有已部署的模型，请运行: python deploy_models.py')
  except:
      print('❌ 无法获取模型信息')
  "
  
  echo ""
  print_info "查看 GPU 使用情况:"
  if command -v nvidia-smi &> /dev/null; then
      nvidia-smi --query-gpu=index,name,memory.used,memory.total --format=csv,noheader | while read line; do
          echo "  GPU $line"
      done
  else
      echo "  ⚠️  nvidia-smi 未安装，无法查看 GPU 信息"
  fi
  
  echo ""
  echo "========================================="
  echo "  ✅ API 调用示例演示完成"
  echo "========================================="