"""
React 模式节点模块 - 带超时和重试功能
包含：
- react_reason_node: 使用 intent.py 进行推理
- error_handling_node: 错误处理节点
- init_state_node: 初始化状态节点

注意：为了兼容 LangGraph 的同步接口，我们保留了同步的 react_reason 调用
但内部会根据情况使用规则推理或尝试异步调用
"""

import sys
from typing import Dict, Any, Optional
from datetime import datetime

# 导入我们的 intent.py
from app.core.intent import (
    react_reason,
    react_reason_async,
    get_route_by_reasoning,
    ReasoningAction,
    ReasoningResult
)
from app.core.state_base import StateUtils
from app.main_graph.state import MainGraphState, ErrorRecord, ErrorSeverity
from app.main_graph.utils.retry_utils import (
    RetryConfig,
    SUBGRAPH_RETRY_CONFIG
)
from app.logger import info


# ========== 1. React 推理节点 ==========

async def react_reason_node(state: MainGraphState, config: Optional[Dict[str, Any]] = None) -> MainGraphState:
    """
    React 模式推理节点：判断下一步做什么（异步版本）
    
    Returns: 更新后的状态
    """
    state.current_phase = "react_reasoning"
    state.reasoning_step += 1
    
    info(f"[react_reason] 第 {state.reasoning_step} 次推理开始")
    
    # 检查是否超过最大步数
    if state.reasoning_step > state.max_steps:
        state.current_phase = "max_steps_exceeded"
        state.final_result = (
            f"❌ 推理步数超过限制（最大 {state.max_steps} 步），"
            f"已执行 {state.reasoning_step - 1} 步。"
            f"请简化您的问题或分批提问。"
        )
        state.success = False
        return state
    
    # 准备上下文
    context = {
        "retrieved_docs": state.rag_docs,
        "previous_actions": [h.get("action") for h in state.reasoning_history],
        "messages": state.messages,
        "errors": state.errors
    }
    
    # 使用 intent.py 进行推理（现在直接用异步版本）
    result: ReasoningResult = await react_reason_async(state.user_query, context)
    
    info(f"[react_reason] 推理结果: action={result.action.name}, confidence={result.confidence}")
    if result.reasoning:
        info(f"[react_reason] 推理过程: {result.reasoning}")
    
    # 关键修复：直接发送自定义事件给 agent_service，而不是通过 state
    if config:
        try:
            from langchain_core.callbacks.manager import adispatch_custom_event
            
            callbacks = config.get("callbacks")
            if callbacks:
                info(f"[react_reason] 直接发送推理事件 #{state.reasoning_step}")
                await adispatch_custom_event(
                    "react_reasoning",
                    {
                        "step": state.reasoning_step,
                        "action": result.action.name,
                        "confidence": result.confidence,
                        "reasoning": result.reasoning
                    },
                    callbacks=callbacks
                )
        except Exception as e:
            info(f"[react_reason] 无法发送自定义事件: {e}")
    
    # 记录推理历史
    state.reasoning_history.append({
        "step": state.reasoning_step,
        "action": result.action.name,
        "confidence": result.confidence,
        "reasoning": result.reasoning,
        "timestamp": datetime.now().isoformat()
    })
    
    # 更新状态
    state.debug_info["last_reasoning"] = {
        "action": result.action.name,
        "confidence": result.confidence,
        "reasoning": result.reasoning
    }
    
    # 保存推理结果到状态
    state.debug_info["reasoning_result"] = result
    
    # 确定下一步动作
    state.last_action = result.action.name
    
    # 关键修复：不再设置 latest_reasoning，避免 agent_service 重复读取
    if "latest_reasoning" in state.debug_info:
        del state.debug_info["latest_reasoning"]
    
    return state


# ========== 2. 联网搜索节点 ==========

async def web_search_node(state: MainGraphState, config: Optional[Dict[str, Any]] = None) -> MainGraphState:
    """
    联网搜索节点：执行搜索并将结果保存到状态
    """
    state.current_phase = "web_searching"
    
    # 发送开始事件
    if config:
        try:
            from langchain_core.callbacks.manager import adispatch_custom_event
            callbacks = config.get("callbacks")
            if callbacks:
                await adispatch_custom_event(
                    "react_reasoning",
                    {
                        "step": state.reasoning_step,
                        "action": "web_search_start",
                        "confidence": 1.0,
                        "reasoning": "开始执行联网搜索..."
                    },
                    callbacks=callbacks
                )
        except Exception as e:
            info(f"[web_search_node] 无法发送开始事件: {e}")
    
    # 获取搜索查询
    reasoning_result = state.debug_info.get("reasoning_result")
    search_query = reasoning_result.metadata.get("search_query", state.user_query) if reasoning_result else state.user_query
    
    try:
        from app.core import web_search
        
        print(f"[WebSearch] 搜索: {search_query}")
        search_result = web_search(search_query, max_results=5)
        
        # 保存搜索结果到状态
        if not hasattr(state, "web_search_results"):
            state.web_search_results = []
        state.web_search_results.append(search_result)
        
        # 将搜索结果添加到 rag_context，供 LLM 使用
        if state.rag_context:
            state.rag_context = f"{state.rag_context}\n\n---\n\n## 🌐 联网搜索结果:\n{search_result}"
        else:
            state.rag_context = f"## 🌐 联网搜索结果:\n{search_result}"
        
        state.success = True
        print(f"[WebSearch] 搜索完成")
        
        # 发送完成事件
        if config:
            try:
                from langchain_core.callbacks.manager import adispatch_custom_event
                callbacks = config.get("callbacks")
                if callbacks:
                    await adispatch_custom_event(
                        "react_reasoning",
                        {
                            "step": state.reasoning_step,
                            "action": "web_search_complete",
                            "confidence": 1.0,
                            "reasoning": f"联网搜索完成，找到 {len(search_result) if isinstance(search_result, list) else 1} 条结果"
                        },
                        callbacks=callbacks
                    )
            except Exception as e:
                info(f"[web_search_node] 无法发送完成事件: {e}")
        
    except Exception as e:
        from app.main_graph.state import ErrorRecord, ErrorSeverity
        from datetime import datetime
        
        error_record = ErrorRecord(
            error_type="WebSearchError",
            error_message=str(e),
            severity=ErrorSeverity.WARNING,
            source="web_search_node",
            timestamp=datetime.now().isoformat(),
            retry_count=0,
            max_retries=2,
            context={"search_query": search_query}
        )
        state.errors.append(error_record)
        state.current_error = error_record
        state.current_phase = "error_handling"
        state.success = False
        
        # 发送错误事件
        if config:
            try:
                from langchain_core.callbacks.manager import adispatch_custom_event
                callbacks = config.get("callbacks")
                if callbacks:
                    await adispatch_custom_event(
                        "react_reasoning",
                        {
                            "step": state.reasoning_step,
                            "action": "web_search_error",
                            "confidence": 1.0,
                            "reasoning": f"联网搜索失败: {str(e)}"
                        },
                        callbacks=callbacks
                    )
            except Exception as e:
                info(f"[web_search_node] 无法发送错误事件: {e}")
    
    return state


# ========== 3. 错误处理节点 ==========

def error_handling_node(state: MainGraphState) -> MainGraphState:
    """
    错误处理节点：处理子图/工具调用错误
    
    返回结构化错误信息，格式如下：
    {
        "tool/node": "...",
        "status": "failed",
        "error": "...",
        "retries_exceeded": true/false,
        "suggestion": "..."
    }
    """
    state.current_phase = "error_handling"

    if not state.current_error:
        state.current_phase = "react_reasoning"
        return state

    error = state.current_error

    # 更新错误状态
    state.error_message = f"{error.error_type}: {error.error_message}"

    # 记录结构化错误信息
    structured_error = {
        "tool": error.source,
        "status": "failed",
        "error": error.error_message,
        "retries_exceeded": error.retry_count >= error.max_retries,
        "retry_count": error.retry_count,
        "max_retries": error.max_retries
    }

    # 根据错误类型添加建议
    if "RAG" in error.error_type:
        structured_error["suggestion"] = "尝试重新表述问题或直接询问"
    elif "subgraph" in error.source or "contact" in error.source:
        structured_error["suggestion"] = "子图执行失败，请尝试简化查询"
    elif "timeout" in error.error_message.lower():
        structured_error["suggestion"] = "请求超时，请稍后再试"
    else:
        structured_error["suggestion"] = "请尝试其他方式提问"

    state.debug_info["structured_error"] = structured_error

    # 策略1: 检查是否可以重试
    can_retry = (
        error.severity in [ErrorSeverity.WARNING, ErrorSeverity.ERROR]
        and error.retry_count < error.max_retries
    )

    if can_retry:
        error.retry_count += 1
        state.retry_action = error.source
        state.debug_info["retry_count"] = error.retry_count

        if "RAG" in error.error_type:
            state.last_action = "RE_RETRIEVE_RAG"
        elif "subgraph" in error.source:
            state.last_action = "DIRECT_RESPONSE"
        else:
            state.last_action = "REASON"

        state.current_phase = "retrying"
        return state

    # 策略2: 无法重试，尝试降级方案
    if error.severity != ErrorSeverity.FATAL:
        state.final_result = (
            f"⚠️  遇到一些问题：\n"
            f"```json\n{structured_error}\n```\n"
            f"但我会尽力用现有信息回答您。"
        )
        state.success = True
        state.current_phase = "finalizing"
        return state

    # 策略3: 致命错误
    state.final_result = (
        f"❌ 服务暂时不可用，请稍后再试。\n"
        f"```json\n{structured_error}\n```"
    )
    state.success = False
    state.current_phase = "finalizing"

    return state


# ========== 4. 初始化状态节点 ==========

def init_state_node(state: MainGraphState) -> MainGraphState:
    """
    初始化状态节点：在流程开始时设置初始值
    """
    state.current_phase = "initializing"
    state.reasoning_step = 0
    state.start_time = datetime.now().isoformat()

    # 从 messages 中提取用户查询
    if not state.user_query and state.messages:
        last_msg = state.messages[-1]
        state.user_query = getattr(last_msg, "content", str(last_msg))

    return state


# ========== 5. 条件路由函数 ==========

def route_by_reasoning(state: MainGraphState) -> str:
    """
    根据推理结果决定下一步路由
    
    Returns: 路由标识，对应 graph_builder.py 中的边
    """
    # 先检查特殊情况
    if state.current_phase == "max_steps_exceeded":
        return "llm_call"
    if state.current_phase == "error_handling" or state.current_error:
        return "handle_error"
    if state.current_phase == "finalizing" or state.current_phase == "done":
        return "llm_call"
    if state.current_phase == "retrying":
        if state.retry_action and "rag" in state.retry_action.lower():
            return "rag_retrieve"
        return "react_reason"
    
    # ========== 关键修复：优先检查当前推理结果 ==========
    reasoning_result: Optional[ReasoningResult] = state.debug_info.get("reasoning_result")
    if reasoning_result and reasoning_result.action == ReasoningAction.DIRECT_RESPONSE:
        info(f"[route_by_reasoning] 当前推理结果=DIRECT_RESPONSE，直接去 llm_call")
        return "llm_call"
    
    # ========== 然后检查历史和状态 ==========
    previous_actions = [h.get("action") for h in state.reasoning_history]
    
    # 检查是否已经执行过子图
    if "subgraph_completed" in previous_actions or state.final_result:
        return "llm_call"
    
    # 检测 RAG 重复循环 - 如果发现"RETRIEVE_RAG"出现超过1次，直接去 LLM
    rag_count = previous_actions.count("RETRIEVE_RAG")
    if rag_count >= 2:
        info(f"[route_by_reasoning] 检测到 RAG 重复循环（{rag_count}次），直接去 llm_call")
        return "llm_call"
    
    # 如果已经有 rag_docs 或 rag_context，说明已经检索过了，直接去 LLM
    if (state.rag_docs and len(state.rag_docs) > 0) or (state.rag_context and len(state.rag_context) > 0):
        info(f"[route_by_reasoning] 检测到已存在 RAG 检索结果，直接去 llm_call")
        return "llm_call"
    
    # 限制最多 3 次推理，避免无限循环
    if len(previous_actions) >= 3:
        info(f"[route_by_reasoning] 已达到最大推理次数 ({len(previous_actions)})，直接去 llm_call")
        return "llm_call"
    
    # ========== 最后处理其他推理结果 ==========
    if not reasoning_result:
        return "llm_call"
    
    # 使用 intent.py 提供的路由函数
    route = get_route_by_reasoning(reasoning_result)
    
    # 映射到我们的节点名称
    # 注意：这些名称必须与 main_graph_builder.py 中定义的节点名称一致
    route_mapping = {
        "direct_response": "llm_call",
        "retrieve_rag": "rag_retrieve",
        "re_retrieve_rag": "rag_retrieve",
        "web_search": "web_search",
        "clarify": "llm_call",
        "call_tool": "llm_call",
        "contact": "contact_subgraph",
        "dictionary": "dictionary_subgraph",
        "news_analysis": "news_analysis_subgraph",
    }
    
    info(f"[route_by_reasoning] 推理结果={reasoning_result.action.name}, 路由={route_mapping.get(route, 'llm_call')}, 历史动作={previous_actions}")
    return route_mapping.get(route, "llm_call")


# ========== 导出 ==========

__all__ = [
    "init_state_node",
    "react_reason_node",
    "web_search_node",
    "error_handling_node",
    "route_by_reasoning"
]