#!/usr/bin/env python3
"""
完整流程测试：parse-service → embedding-api → Elasticsearch
"""
import requests
import json
import time
import sys
import os

# 配置
PARSE_SERVICE_URL = "http://localhost:8000"
EMBEDDING_API_URL = "http://localhost:8084"

# 测试文件
TEST_FILES = [
    "/Users/chaizi/Work/Projects/yusys/ai-study/code/four-level-schedule/duomotai/examples/5.docx",
    "/Users/chaizi/Work/Projects/yusys/ai-study/code/four-level-schedule/duomotai/examples/20以内口算.pdf",
]


def check_services():
    """检查服务状态"""
    print("=" * 60)
    print("检查服务状态...")
    print("=" * 60)

    # 检查 parse-service
    try:
        response = requests.get(f"{PARSE_SERVICE_URL}/health", timeout=5)
        if response.status_code == 200:
            print("✅ parse-service 正常")
        else:
            print(f"❌ parse-service 异常: {response.status_code}")
            return False
    except Exception as e:
        print(f"❌ parse-service 不可达: {e}")
        print("   请先启动: cd parse-service && python main.py")
        return False

    # 检查 schedule-embedding-api
    try:
        response = requests.get(f"{EMBEDDING_API_URL}/actuator/health", timeout=5)
        if response.status_code == 200:
            data = response.json()
            if data.get("status") == "UP":
                print("✅ schedule-embedding-api 正常")
            else:
                print(f"❌ schedule-embedding-api 状态异常: {data}")
                return False
        else:
            print(f"❌ schedule-embedding-api 异常: {response.status_code}")
            return False
    except Exception as e:
        print(f"❌ schedule-embedding-api 不可达: {e}")
        print("   请先启动 schedule-embedding-api")
        return False

    print()
    return True


def parse_file(file_path, task_id):
    """调用 parse-service 解析文件"""
    print(f"  步骤 1/2: 调用 parse-service 解析...")
    print(f"    文件: {file_path}")
    print(f"    任务ID: {task_id}")

    url = f"{PARSE_SERVICE_URL}/api/v1/parse/path?file_path={file_path}"

    try:
        response = requests.post(url, json={}, timeout=300)
        if response.status_code != 200:
            print(f"    ❌ HTTP错误: {response.status_code}")
            print(f"       {response.text}")
            return None

        data = response.json()
        if data.get("code") != 200:
            print(f"    ❌ 解析失败: {data.get('message')}")
            return None

        result = data.get("data")
        print(f"    ✅ 解析成功!")
        print(f"       - 文件类型: {result.get('file_type')}")
        print(f"       - 内容长度: {len(result.get('content', ''))} 字符")
        print(f"       - 解析耗时: {result.get('parse_time_ms')} ms")

        return result

    except Exception as e:
        print(f"    ❌ 调用异常: {e}")
        return None


def index_document(task_id, file_path, parse_result):
    """调用 embedding-api 向量化并入库"""
    print(f"  步骤 2/2: 调用 embedding-api 向量化...")

    url = f"{EMBEDDING_API_URL}/api/v1/documents/index"

    # 提取文件名
    file_name = os.path.basename(file_path)

    # 构建请求体
    request_body = {
        "docId": task_id,
        "fileName": file_name,
        "fullText": parse_result.get("content", ""),
        "filePath": file_path,
        "fileType": parse_result.get("file_type"),
        "metadata": parse_result.get("metadata", {}),
    }

    # 添加 fileSize
    metadata = parse_result.get("metadata", {})
    if metadata and "file_size" in metadata:
        request_body["fileSize"] = metadata["file_size"]

    try:
        response = requests.post(
            url,
            json=request_body,
            headers={"Content-Type": "application/json"},
            timeout=120,
        )

        if response.status_code != 200:
            print(f"    ❌ HTTP错误: {response.status_code}")
            print(f"       {response.text}")
            return False

        data = response.json()
        if data.get("success"):
            print(f"    ✅ 向量化并入库成功!")
            return True
        else:
            print(f"    ❌ 向量化失败: {data.get('message')}")
            return False

    except Exception as e:
        print(f"    ❌ 调用异常: {e}")
        return False


def main():
    print("\n")
    print("╔" + "=" * 58 + "╗")
    print("║" + " " * 15 + "多模态解析 - 完整流程测试" + " " * 15 + "║")
    print("╚" + "=" * 58 + "╝")
    print()

    # 检查服务
    if not check_services():
        return 1

    # 处理每个文件
    success_count = 0
    fail_count = 0

    print("=" * 60)
    print(f"开始处理 {len(TEST_FILES)} 个文件...")
    print("=" * 60)

    for i, file_path in enumerate(TEST_FILES, 1):
        print()
        print("-" * 60)
        print(f"文件 {i}/{len(TEST_FILES)}: {os.path.basename(file_path)}")
        print("-" * 60)

        if not os.path.exists(file_path):
            print(f"  ❌ 文件不存在: {file_path}")
            fail_count += 1
            continue

        # 生成任务ID
        task_id = f"test-{int(time.time())}-{i}"

        # 1. 解析文件
        parse_result = parse_file(file_path, task_id)
        if not parse_result:
            fail_count += 1
            continue

        # 2. 向量化并入库
        if index_document(task_id, file_path, parse_result):
            success_count += 1
        else:
            fail_count += 1

        # 稍微延迟一下
        time.sleep(1)

    # 总结
    print()
    print("=" * 60)
    print("测试总结")
    print("=" * 60)
    print(f"总计: {len(TEST_FILES)}")
    print(f"成功: {success_count}")
    print(f"失败: {fail_count}")

    if fail_count == 0:
        print()
        print("🎉 所有测试通过!")
    else:
        print()
        print("⚠️  部分测试失败")

    print("=" * 60)
    print()

    return 0 if fail_count == 0 else 1


if __name__ == "__main__":
    sys.exit(main())