services/supabase_logger_examples.py

"""
Supabase 日志使用示例

演示如何在项目中使用 Supabase 日志记录器
"""
from services.supabase_logger import (
    get_supabase_logger,
    log_api_access,
    log_model_performance,
    LogPerformance,
    log_debug,
    log_error
)
from functools import wraps
import time


# ============================================
# 示例 1: 基本日志记录
# ============================================

def example_basic_logging():
    """基本日志记录示例"""
    logger = get_supabase_logger()

    # 记录 API 访问
    logger.log_api_access(
        endpoint="/api/v1/chat",
        method="POST",
        session_id="user_123",
        response_time_ms=150,
        status_code=200
    )

    # 记录调试信息
    logger.log_debug(
        level="INFO",
        logger="intelligence_analyzer",
        message="Scenario detected: policy_inquiry",
        session_id="user_123",
        data={
            "scenario": "policy_inquiry",
            "confidence": 0.95,
            "keywords": ["年假", "多少"]
        }
    )

    # 记录错误
    logger.log_error(
        error_type="model_load",
        error_message="Failed to load sentiment model",
        error_stack="Traceback...\n  File ...",
        endpoint="/api/v1/evaluate"
    )


# ============================================
# 示例 2: 使用装饰器
# ============================================

@log_api_access("/api/v1/chat")
def process_chat_request(question: str, session_id: str):
    """
    使用 API 日志装饰器
    自动记录 API 调用和响应时间
    """
    # 模拟处理
    time.sleep(0.1)
    return {"answer": "这是回复内容"}


@log_model_performance("sbert-hr-v2", "sbert")
def encode_with_sbert(text: str):
    """
    使用模型性能装饰器
    自动记录模型推理性能
    """
    # 模拟编码
    time.sleep(0.05)
    return [0.1, 0.2, 0.3]


# ============================================
# 示例 3: 使用上下文管理器
# ============================================

def example_context_manager():
    """使用上下文管理器追踪性能"""
    with LogPerformance("chat_processing", "deepseek-chat", "llm"):
        # 执行耗时操作
        result = call_llm_api("年假有多少天？")
        return result


def call_llm_api(prompt: str):
    """模拟 LLM API 调用"""
    time.sleep(0.2)
    return "根据规定，年假天数与工龄挂钩..."


# ============================================
# 示例 4: 在 intelligence_analyzer 中集成
# ============================================

def analyze_with_logging(employee_input: str, session_id: str):
    """
    在分析器中集成日志记录
    """
    logger = get_supabase_logger()

    # 记录输入
    logger.log_debug(
        level="INFO",
        logger="intelligence_analyzer",
        message=f"Processing input: {employee_input[:50]}...",
        session_id=session_id
    )

    try:
        # 场景识别
        start_time = time.time()
        scenario = detect_scenario(employee_input)

        logger.log_debug(
            level="INFO",
            logger="intelligence_analyzer",
            message="Scenario detected",
            session_id=session_id,
            data={"scenario": scenario, "confidence": 0.9}
        )

        # 记录用户行为
        logger.log_user_behavior(
            session_id=session_id,
            action_type="chat",
            action_detail="scenario_detection",
            input_text=employee_input,
            scenario_detected=scenario,
            success=True
        )

        return scenario

    except Exception as e:
        # 记录错误
        logger.log_error(
            error_type="analysis_error",
            error_message=str(e),
            session_id=session_id,
            user_input=employee_input
        )
        raise


def detect_scenario(text: str) -> str:
    """模拟场景检测"""
    time.sleep(0.02)
    return "policy_inquiry"


# ============================================
# 示例 5: 在 FastAPI 路由中集成
# ============================================

from fastapi import Request


async def log_request_middleware(request: Request, call_next):
    """
    FastAPI 中间件 - 记录所有请求
    """
    logger = get_supabase_logger()
    start_time = time.time()

    # 处理请求
    response = await call_next(request)

    # 计算响应时间
    process_time = int((time.time() - start_time) * 1000)

    # 记录日志
    logger.log_api_access(
        endpoint=request.url.path,
        method=request.method,
        client_ip=request.client.host if request.client else None,
        user_agent=request.headers.get("user-agent"),
        session_id=request.headers.get("x-session-id"),
        response_time_ms=process_time,
        status_code=response.status_code
    )

    # 添加响应头
    response.headers["X-Process-Time"] = str(process_time)
    return response


# ============================================
# 示例 6: 批量日志记录
# ============================================

def batch_log_examples():
    """批量日志记录示例"""
    logger = get_supabase_logger()

    # 批量记录用户行为
    behaviors = [
        {
            "session_id": "user_1",
            "action_type": "chat",
            "scenario_detected": "policy_inquiry",
            "success": True
        },
        {
            "session_id": "user_1",
            "action_type": "chat",
            "scenario_detected": "leave_application",
            "success": True
        },
        {
            "session_id": "user_2",
            "action_type": "evaluate",
            "success": False,
            "error_message": "Invalid dialogue format"
        }
    ]

    for behavior in behaviors:
        logger.log_user_behavior(**behavior)


# ============================================
# 示例 7: 错误追踪
# ============================================

def example_with_error_tracking():
    """带错误追踪的示例"""
    logger = get_supabase_logger()

    try:
        # 模拟错误
        result = 1 / 0
    except Exception as e:
        import traceback

        # 记录详细错误信息
        logger.log_error(
            error_type="calculation_error",
            error_message=str(e),
            error_stack=traceback.format_exc(),
            session_id="debug_session",
            request_data={"operation": "division", "operands": [1, 0]}
        )


# ============================================
# 示例 8: 模型性能追踪
# ============================================

def track_all_models():
    """追踪所有模型的性能"""
    logger = get_supabase_logger()

    models = {
        "sbert": "KarenYYH/sbert-hr-v2",
        "sentiment": "KarenYYH/sentiment-hr",
        "llm": "deepseek-chat"
    }

    for model_type, model_name in models.items():
        with LogPerformance(f"load_{model_type}", model_name, model_type):
            # 模拟模型加载
            time.sleep(0.1)


# ============================================
# 简化的日志记录
# ============================================

def example_simplified_logging():
    """使用简化函数记录日志"""
    # 调试日志
    log_debug("intelligence_analyzer", "处理用户输入", level="INFO",
              session_id="user_123", data={"input_length": 20})

    # 错误日志
    log_error("api_error", "请求超时", endpoint="/api/v1/chat",
              session_id="user_123", response_time_ms=5000)


# ============================================
# 运行所有示例
# ============================================

if __name__ == "__main__":
    print("运行 Supabase 日志示例...")

    example_basic_logging()
    print("✓ 基本日志记录")

    process_chat_request("年假有多少天？", "user_123")
    print("✓ API 日志装饰器")

    encode_with_sbert("测试文本")
    print("✓ 模型性能装饰器")

    example_context_manager()
    print("✓ 上下文管理器")

    analyze_with_logging("年假有多少天？", "user_456")
    print("✓ 分析器集成")

    batch_log_examples()
    print("✓ 批量日志")

    example_with_error_tracking()
    print("✓ 错误追踪")

    track_all_models()
    print("✓ 模型性能追踪")

    example_simplified_logging()
    print("✓ 简化日志")

    print("\n所有示例运行完成！")