#!/usr/bin/env python3
"""
Text2SQL 多智能体系统 - CLI演示入口

用法：
    python main.py "查询2024年1月销售额最高的前5个产品"
    python main.py --question "查询所有状态为Active的账户数量"
    python main.py --interactive  # 交互式模式
"""

import os
import sys
import argparse
import logging
from pathlib import Path
from typing import Optional

from dotenv import load_dotenv

# 配置日志
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s [%(levelname)s] %(name)s: %(message)s',
    datefmt='%Y-%m-%d %H:%M:%S'
)

logger = logging.getLogger(__name__)

_DEFAULT_EMBEDDING_PATH = "./data/models/Qwen3-Embedding-0.6B"


def _load_project_env():
    """加载项目根目录 .env（与 main.py 同目录），供后续 os.getenv 使用。"""
    load_dotenv(Path(__file__).resolve().parent / ".env")


def _embedding_model_path() -> str:
    return os.getenv("EMBEDDING_MODEL_PATH", _DEFAULT_EMBEDDING_PATH).strip()


def resolve_sql_dialect(name: str) -> str:
    """CLI / 配置中的方言别名统一为 sqlglot 方言名（SQL Server -> tsql）。"""
    n = (name or "sqlserver").lower().strip()
    if n in ("sqlserver", "mssql"):
        return "tsql"
    return n


def setup_environment():
    """环境检查"""
    _load_project_env()
    use_local_emb = os.getenv("USE_LOCAL_EMBEDDING", "true").strip().lower() in (
        "1", "true", "yes", "on",
    )
    if use_local_emb:
        # 与 .env 中 EMBEDDING_MODEL_PATH 及 utils.embedding 一致
        model_path = Path(_embedding_model_path())
        if not model_path.exists():
            logger.warning(f"Embedding模型不存在: {model_path}")
            logger.info("请先下载模型：")
            logger.info("  modelscope download --model 'Qwen/Qwen3-Embedding-0.6B' "
                        f"--local_dir '{model_path}'")
            logger.info("或使用远程 Embedding API：USE_LOCAL_EMBEDDING=false，并配置 "
                        "MODELSCOPE_API_KEY、或 OPENAI_API_KEY+OPENAI_EMBEDDING_MODEL"
                        "（及可选 OPENAI_BASE_URL）、或 DASHSCOPE_*（百炼）")
            return False
    else:
        ms_key = os.getenv("MODELSCOPE_API_KEY", "").strip()
        oa_key = os.getenv("OPENAI_API_KEY", "").strip()
        oa_key_ok = oa_key and not (
            oa_key.startswith("http://") or oa_key.startswith("https://")
        )
        if ms_key:
            pass  # ModelScope：BASE_URL / MODEL 有默认值，仅需 KEY
        elif oa_key_ok:
            if not os.getenv("OPENAI_EMBEDDING_MODEL", "").strip():
                logger.warning("USE_LOCAL_EMBEDDING=false 但未设置 OPENAI_EMBEDDING_MODEL")
                return False
        else:
            if not os.getenv("DASHSCOPE_API_KEY", "").strip():
                logger.warning(
                    "USE_LOCAL_EMBEDDING=false 但未设置 MODELSCOPE_API_KEY、"
                    "OPENAI_API_KEY+OPENAI_EMBEDDING_MODEL 或 DASHSCOPE_API_KEY"
                )
                return False
            base = (
                os.getenv("DASHSCOPE_BASE_URL") or os.getenv("DASHSCOPE_base_url", "")
            ).strip()
            if not base:
                logger.warning("未设置 DASHSCOPE_BASE_URL（或 DASHSCOPE_base_url）")
                return False
            if not os.getenv("DASHSCOPE_MODEL", "").strip():
                logger.warning("未设置 DASHSCOPE_MODEL")
                return False

    # 检查Schema文件
    schema_path = Path("./data/schemas/G3SB_MCDataDictionary_table_structure.json")
    if not schema_path.exists():
        logger.warning(f"Schema文件不存在: {schema_path}")
        logger.info("请将G3SB Schema文件放置在 ./data/schemas/ 目录")
        return False

    # 检查API Key
    if not os.getenv("DEEPSEEK_API_KEY"):
        logger.warning("环境变量 DEEPSEEK_API_KEY 未设置")
        logger.info("请在 .env 文件中配置，或 export DEEPSEEK_API_KEY=your_key")
        return False

    return True


def _default_g3sb_meta_path(structure_path: str) -> Optional[str]:
    """若存在与 table_structure 同名的 table_meta 文件则返回其路径。"""
    p = Path(structure_path)
    if "table_structure" not in p.name:
        return None
    cand = p.parent / p.name.replace("table_structure", "table_meta")
    return str(cand) if cand.is_file() else None


def load_schema(schema_path: str, schema_meta_path: Optional[str] = None):
    """加载 Schema；G3SB structure JSON 会自动尝试配对 table_meta（可用 --schema-meta 指定）。"""
    from schema.manager import SchemaManager

    meta = (
        schema_meta_path
        if schema_meta_path is not None
        else _default_g3sb_meta_path(schema_path)
    )
    logger.info(f"加载Schema: {schema_path}")
    if meta:
        logger.info(f"  表注释(meta): {meta}")

    schema_mgr = SchemaManager.load_from_json(
        schema_path, g3sb_meta_path=meta
    )

    stats = schema_mgr.get_statistics()
    logger.info(
        f"[OK] Schema加载完成: {stats['database']}, "
        f"共{stats['total_tables']}张表, {stats['total_columns']}个字段"
    )

    return schema_mgr


def create_orchestrator(schema_mgr, args):
    """创建编排器"""
    from agents.orchestrator import Text2SQLOrchestrator
    from llm.deepseek_client import DeepSeekConfig

    api_key = (args.api_key or os.getenv("DEEPSEEK_API_KEY") or "").strip()
    if not api_key:
        raise ValueError(
            "未配置 DeepSeek API Key：请在 .env 中设置 DEEPSEEK_API_KEY，"
            "或使用命令行参数 --api-key"
        )
    base_url = (os.getenv("DEEPSEEK_BASE_URL") or "https://api.deepseek.com").strip()

    config = DeepSeekConfig(
        api_key=api_key,
        base_url=base_url,
        model_name=args.model or "deepseek-chat",
        temperature=args.temperature,
        max_tokens=args.max_tokens,
    )

    orchestrator = Text2SQLOrchestrator(
        schema_manager=schema_mgr,
        deepseek_config=config,
        embedding_model_path=args.embedding_model,
        vector_db_path=args.vector_db,
        max_retry=args.max_retry,
        use_vector_search=not args.no_vector_search,
        # Few-shot配置
        fewshot_enabled=not args.no_fewshot,
        fewshot_top_k=args.fewshot_top_k,
        fewshot_min_rating=args.fewshot_min_rating,
    )

    return orchestrator


def single_query(orchestrator, question: str, dialect: str = "tsql"):
    """单次查询"""
    import time

    logger.info(f"[Q] 问题: {question}")

    start = time.time()
    result = orchestrator.generate(
        question=question,
        dialect=dialect,
        top_k_candidates=20
    )
    elapsed = time.time() - start

    print("\n" + "=" * 60)
    print("生成结果:")
    print("=" * 60)

    if result.valid:
        print(f"[OK] SQL (耗时 {elapsed:.2f}s, 尝试 {result.attempts} 次):\n")
        print(result.sql)
    else:
        print(f"[FAIL] 生成失败 (尝试 {result.attempts} 次)")
        for err in result.errors:
            print(f"  - {err}")

    if result.warnings:
        print("\n[WARN] 警告:")
        for w in result.warnings:
            print(f"  - {w}")

    print(f"\n使用表: {result.tables_used}")

    return result


def interactive_mode(orchestrator, dialect: str = "tsql"):
    """交互式模式"""
    print("\n" + "=" * 60)
    print("Text2SQL 交互模式（输入 'quit' 或 'exit' 退出）")
    print("=" * 60 + "\n")

    while True:
        try:
            question = input("❓ 请输入问题: ").strip()
            if question.lower() in ('quit', 'exit', 'q'):
                print("再见！")
                break

            if not question:
                continue

            result = single_query(orchestrator, question, dialect)
            print()

        except KeyboardInterrupt:
            print("\n再见！")
            break
        except Exception as e:
            logger.error(f"查询失败: {e}")


def batch_mode(orchestrator, questions: list, dialect: str = "tsql"):
    """批量查询模式"""
    print(f"\n批量模式：共 {len(questions)} 个问题\n")

    results = []
    for i, question in enumerate(questions, 1):
        print(f"[{i}/{len(questions)}] {question}")
        result = single_query(orchestrator, question, dialect)
        results.append(result)
        print()

    # 统计
    success_count = sum(1 for r in results if r.valid)
    print("=" * 60)
    print(f"统计: {success_count}/{len(questions)} 成功 "
          f"({success_count/len(questions)*100:.1f}%)")

    return results


def main():
    parser = argparse.ArgumentParser(
        description="Text2SQL 多智能体系统 - 自然语言生成SQL"
    )
    parser.add_argument(
        "question",
        nargs="?",
        help="自然语言问题（如不提供则进入交互模式）"
    )
    parser.add_argument(
        "--schema", "-s",
        default="./data/schemas/G3SB_MCDataDictionary_table_structure.json",
        help="Schema文件路径（默认: ./data/schemas/G3SB_MCDataDictionary_table_structure.json）"
    )
    parser.add_argument(
        "--schema-meta",
        default=None,
        help="G3SB table_meta.json；默认自动使用同目录下文件名含 table_meta 的配对文件",
    )
    parser.add_argument(
        "--dialect", "-d",
        default=os.getenv("TEXT2SQL_DIALECT", "sqlserver").strip(),
        choices=["mysql", "postgresql", "sqlite", "tsql", "sqlserver", "mssql"],
        help="SQL方言（默认: sqlserver / T-SQL；可用环境变量 TEXT2SQL_DIALECT 覆盖）",
    )
    parser.add_argument(
        "--api-key",
        help="DeepSeek API Key（默认从DEEPSEEK_API_KEY环境变量读取）"
    )
    parser.add_argument(
        "--model", "-m",
        default="deepseek-chat",
        help="DeepSeek模型名称（默认: deepseek-chat）"
    )
    parser.add_argument(
        "--temperature", "-t",
        type=float,
        default=0.3,
        help="生成温度（默认: 0.3）"
    )
    parser.add_argument(
        "--max-tokens",
        type=int,
        default=4096,
        help="最大token数（默认: 4096）"
    )
    parser.add_argument(
        "--max-retry",
        type=int,
        default=2,
        help="最大重试次数（默认: 2）"
    )
    _load_project_env()
    parser.add_argument(
        "--embedding-model",
        default=_embedding_model_path(),
        help="Embedding模型路径（默认来自环境变量 EMBEDDING_MODEL_PATH）"
    )
    parser.add_argument(
        "--vector-db",
        default=os.getenv("VECTOR_DB_PATH", "./data/embeddings/chroma").strip(),
        help="向量数据库路径（默认来自环境变量 VECTOR_DB_PATH）"
    )
    parser.add_argument(
        "--no-vector-search",
        action="store_true",
        help="禁用向量检索（使用所有表）"
    )
    # Few-shot 配置
    parser.add_argument(
        "--no-fewshot",
        action="store_true",
        help="禁用few-shot示例增强"
    )
    parser.add_argument(
        "--fewshot-top-k",
        type=int,
        default=int(os.getenv("FEWSHOT_TOP_K", "3")),
        help="每次使用的few-shot示例数量（默认: 3）"
    )
    parser.add_argument(
        "--fewshot-min-rating",
        type=int,
        default=int(os.getenv("FEWSHOT_MIN_RATING", "7")),
        help="few-shot示例最低评分（默认: 7）"
    )
    parser.add_argument(
        "--interactive", "-i",
        action="store_true",
        help="交互模式"
    )
    parser.add_argument(
        "--batch", "-b",
        help="批量文件路径（每行一个问题）"
    )
    parser.add_argument(
        "--verbose", "-v",
        action="store_true",
        help="详细日志"
    )

    args = parser.parse_args()
    args.dialect = resolve_sql_dialect(args.dialect)

    # 日志级别
    if args.verbose:
        logging.getLogger().setLevel(logging.DEBUG)

    # 环境检查
    if not setup_environment():
        sys.exit(1)

    # 加载Schema
    try:
        schema_mgr = load_schema(args.schema, args.schema_meta)
    except Exception as e:
        logger.error(f"Schema加载失败: {e}")
        sys.exit(1)

    # 创建Orchestrator
    try:
        orchestrator = create_orchestrator(schema_mgr, args)
    except Exception as e:
        logger.error(f"Orchestrator创建失败: {e}")
        sys.exit(1)

    # 根据参数选择模式
    if args.interactive or (not args.question and not args.batch):
        interactive_mode(orchestrator, args.dialect)
    elif args.batch:
        with open(args.batch, 'r', encoding='utf-8') as f:
            questions = [line.strip() for line in f if line.strip()]
        batch_mode(orchestrator, questions, args.dialect)
    elif args.question:
        single_query(orchestrator, args.question, args.dialect)
    else:
        parser.print_help()


if __name__ == "__main__":
    main()