# MCP 分层会话快照 + 差分更新核心算法
# 适配协议 JSON-RPC 消息层扩展，新增 StateVersion、DeltaFlag、SnapshotType 等元数据字段
class MCPSessionState:
    def __init__(self):
        self.session_id: str          # 全局唯一会话标识
        self.state_version: int       # 会话状态版本号（全局单调递增）
        self.core_state: dict         # 核心态：推理链、决策参数、语义锚点（持久化）
        self.incremental_state: dict  # 增量态：单轮交互更新数据（内存+定时落盘）
        self.delta_record: dict       # 差分变更记录：仅存储本轮变更字段
        self.timestamp: int           # 状态更新时间戳
        self.shard_mapping: dict      # 上下文分片-状态版本映射关系

# 1. 状态分层分类与冗余过滤
def state_layer_classify(prev_snapshot: MCPSessionState, current_raw_data: dict) -> tuple[dict, dict]:
    """
    区分核心态、增量态、冗余态，过滤无效重复数据
    return: new_core_state, new_incremental_state
    """
    # 核心态继承+局部更新，不做全量覆盖
    new_core = prev_snapshot.core_state.copy()
    core_update_keys = ["reason_chain", "decision_param", "semantic_anchor", "session_config"]
    for key in core_update_keys:
        if key in current_raw_data:
            new_core[key] = current_raw_data[key]
    
    # 增量态仅保留本轮新增、变更的非核心交互数据
    new_increment = {k:v for k,v in current_raw_data.items() if k not in core_update_keys}
    # 剔除冗余重复语义数据
    new_increment = filter_redundant_data(new_increment, prev_snapshot.incremental_state)
    return new_core, new_increment

# 2. 核心差分更新算法
def mcp_delta_update(prev_snapshot: MCPSessionState, current_raw_data: dict) -> MCPSessionState:
    # 状态分层解析
    new_core, new_increment = state_layer_classify(prev_snapshot, current_raw_data)
    # 计算精准差分数据（仅变更字段）
    delta_data = calculate_delta(prev_snapshot.core_state | prev_snapshot.incremental_state, new_core | new_increment)
    
    # 版本迭代与协议字段绑定
    new_snapshot = MCPSessionState()
    new_snapshot.session_id = prev_snapshot.session_id
    new_snapshot.state_version = prev_snapshot.state_version + 1
    new_snapshot.core_state = new_core
    new_snapshot.incremental_state = new_increment
    new_snapshot.delta_record = delta_data
    new_snapshot.timestamp = get_current_timestamp()
    # 绑定上下文分片ID，实现分片与状态版本联动
    new_snapshot.shard_mapping = bind_shard_version(current_raw_data["shard_list"], new_snapshot.state_version)
    
    # 长会话快照压缩合并
    new_snapshot = snapshot_compress_merge(prev_snapshot, new_snapshot)
    return new_snapshot

# 3. 快照回滚接口（支撑异常恢复）
def snapshot_rollback(snapshot_list: list[MCPSessionState], target_version: int) -> MCPSessionState:
    """基于版本号精准回滚历史会话状态"""
    for snap in snapshot_list:
        if snap.state_version == target_version:
            return snap
    raise VersionNotFoundError("MCP会话状态版本不存在")

# 分布式MCP网关状态同步与仲裁核心逻辑
from typing import List, Dict

class MCPGatewayGlobalState:
    def __init__(self):
        self.global_session_map: Dict[str, MCPSessionState]  # 会话ID-全局基准状态映射
        self.agent_node_offset: Dict[str, float]             # 各Agent节点状态偏移量
        self.sync_threshold: float = 0.15                   # 状态偏移阈值（可配置）

# 1. 多节点状态冲突仲裁（时间戳+版本优先级策略）
def state_arbitration(gateway: MCPGatewayGlobalState, session_id: str, node_state_list: List[MCPSessionState]) -> MCPSessionState:
    """
    对多Agent上报的本地状态进行仲裁，生成全局唯一基准状态
    规则：高版本优先、同版本最新时间戳优先
    """
    if not node_state_list:
        raise SyncDataEmptyError("无Agent节点状态上报")
    
    # 按版本号、时间戳双重排序
    sorted_states = sorted(
        node_state_list,
        key=lambda x: (x.state_version, x.timestamp),
        reverse=True
    )
    # 筛选最优全局基准状态
    best_global_state = sorted_states[0]
    # 更新网关全局状态
    gateway.global_session_map[session_id] = best_global_state
    return best_global_state

# 2. 节点状态偏移检测与强制同步
def node_state_sync_check(gateway: MCPGatewayGlobalState, session_id: str, agent_id: str, local_state: MCPSessionState) -> bool:
    """检测单Agent节点状态偏移，超限则触发强制同步"""
    global_state = gateway.global_session_map.get(session_id)
    if not global_state:
        return False
    
    # 计算本地状态与全局基准的偏移量
    offset = calculate_state_offset(global_state, local_state)
    gateway.agent_node_offset[agent_id] = offset
    
    # 偏移超限，触发状态修复与同步
    if offset > gateway.sync_threshold:
        force_sync_agent_state(agent_id, global_state)
        return True
    return False

# 3. 分布式分片并行同步
def shard_parallel_sync(global_state: MCPSessionState, agent_list: List[str]):
    """按语义分片拆分，并行同步至各Agent节点，提升同步效率"""
    shard_version_map = global_state.shard_mapping
    for shard_id, version in shard_version_map.items():
        shard_data = get_shard_data(shard_id, version)
        # 多节点并行推送分片状态
        parallel_push_shard(agent_list, shard_id, shard_data, version)

# MCP 推理断点续推与分级状态恢复算法
class InferenceBreakPoint:
    def __init__(self):
        self.session_id: str
        self.state_version: int       # 关联会话状态版本
        self.breakpoint_type: str     # 节点类型：推理中间节点/分片切换节点/轮次结束节点
        self.infer_progress: float    # 推理进度0-100%
        self.shard_decode_status: dict# 分片解码完成状态
        self.param_snapshot: dict     # 当前推理参数快照
        self.create_time: int

# 异常类型分级
EXCEPTION_LEVEL = {
    "LIGHT": ["network_fluctuate", "infer_timeout"],    # 轻度异常
    "HEAVY": ["node_crash", "cache_clear", "service_restart"] # 重度异常
}

# 1. 全链路断点植入与日志记录
def implant_breakpoint(session_state: MCPSessionState, infer_status: dict) -> InferenceBreakPoint:
    """在推理关键节点自动植入断点锚点，记录完整现场"""
    bp = InferenceBreakPoint()
    bp.session_id = session_state.session_id
    bp.state_version = session_state.state_version
    bp.breakpoint_type = infer_status["node_type"]
    bp.infer_progress = infer_status["progress"]
    bp.shard_decode_status = infer_status["shard_decode"]
    bp.param_snapshot = infer_status["current_param"]
    bp.create_time = get_current_timestamp()
    # 协议层留存断点日志
    save_breakpoint_log(bp)
    return bp

# 2. 分级状态恢复策略
def hierarchical_recovery(session_id: str, exception_type: str) -> MCPSessionState:
    """根据异常等级执行不同恢复策略"""
    # 获取最新断点与状态快照
    latest_bp = get_latest_breakpoint(session_id)
    if exception_type in EXCEPTION_LEVEL["LIGHT"]:
        # 轻度异常：内存级快速恢复
        local_increment_snap = get_memory_cache_snapshot(session_id)
        resume_state = local_increment_snap
        resume_infer_progress(latest_bp)
    elif exception_type in EXCEPTION_LEVEL["HEAVY"]:
        # 重度异常：网关全局持久化快照重建
        global_latest_snap = gateway_get_persist_snapshot(session_id, latest_bp.state_version)
        resume_state = global_latest_snap
        rebuild_session_context(resume_state)
    else:
        raise UnknownExceptionError("未知异常类型")
    return resume_state

# 3. 重试幂等性校验（防状态冲突）
def retry_idempotent_check(session_id: str, target_version: int) -> bool:
    """通过版本锁+分片校验，保证重试无状态重复、无数据冲突"""
    if get_state_version_lock(session_id, target_version):
        # 校验分片解码幂等性
        shard_idempotent = check_shard_repeat(session_id, target_version)
        return shard_idempotent
    return False