HKGAI V3 重磅发布：香港超级智能体时代来临，Token 压缩率提升 10 倍

# HKGAI V3 硬件抽象层（简化示意）
class HardwareAdapter:
    """统一的硬件抽象接口"""
    
    SUPPORTED_CHIPS = {
        "nvidia_a100": {"vendor": "NVIDIA", "tflops": 312},
        "nvidia_h100": {"vendor": "NVIDIA", "tflops": 989},
        "huawei_ascend_910c": {"vendor": "Huawei", "tflops": 256},
        "amd_mi300x": {"vendor": "AMD", "tflops": 530}
    }
    
    def __init__(self, chip_type: str):
        if chip_type not in self.SUPPORTED_CHIPS:
            raise ValueError(f"Unsupported chip: {chip_type}")
        self.chip_type = chip_type
        self.config = self._load_chip_config(chip_type)
    
    def _load_chip_config(self, chip_type: str) -> dict:
        """加载芯片特定配置"""
        return {
            "memory_bandwidth": self._get_bandwidth(chip_type),
            "optimal_batch_size": self._get_optimal_batch(chip_type),
            "quantization_precisions": self._get_supported_precisions(chip_type)
        }
    
    def prepare_model_weights(self, model_path: str) -> bytes:
        """根据目标芯片转换模型权重"""
        if self.chip_type.startswith("nvidia"):
            return self._convert_to_cuda(model_path)
        elif self.chip_type.startswith("huawei"):
            return self._convert_to_ascend(model_path)
        return self._convert_to_onnx(model_path)
    
    def optimize_inference(self, model: "Model") -> "OptimizedModel":
        """针对特定芯片进行推理优化"""
        if self.chip_type == "huawei_ascend_910c":
            return HuaweiAscendOptimizer(model).apply()
        return GenericOptimizer(model).apply()

import asyncio
from dataclasses import dataclass
from typing import List, Dict, Optional
import jieba
import jieba.analyse
import opencc

@dataclass
class HongKongTextSample:
    """香港本地文本样本"""
    content: str
    language: str  # 'zh-Hant', 'zh-Hans', 'en', 'yue'
    domain: str   # 'legal', 'finance', 'government', 'news', 'social'
    cultural_tags: List[str]
    toxicity_score: float
    source: str

class LocalDataProcessor:
    """香港本地数据处理器"""
    
    def __init__(self):
        # 初始化繁体转简体转换器
        self.converter_s2t = opencc.OpenCC('s2t')
        self.converter_t2s = opencc.OpenCC('t2s')
        
        # 加载香港本地词典
        jieba.set_dictionary('/models/dict/hk_variant.txt')
        jieba.initialize()
        
        # 加载本地词汇（港式表达）
        self.hk_slang = self._load_hk_slang()
    
    def _load_hk_slang(self) -> Dict[str, str]:
        """加载香港本地俚语词典"""
        return {
            "打完斋不要和尚": "过河拆桥",
            "大只佬": "健壮的男性",
            "收工会": "下班",
            "俾面": "给面子",
            "出事": "出现问题",
            "有心": "有心意/感谢",
            "知啦": "知道了",
            "搞掂": "完成/搞定",
            "好嘢": "太好了",
            "hea": "敷衍/应付"
        }
    
    async def process_text(self, text: str) -> HongKongTextSample:
        """处理单条文本样本"""
        # 1. 语言检测与标准化
        lang = self.detect_language(text)
        normalized = self.normalize(text, lang)
        
        # 2. 繁简转换（如需要）
        if lang == 'zh-Hant':
            normalized = self.converter_s2t.convert(normalized)
        
        # 3. 关键词提取
        keywords = jieba.analyse.extract_tags(
            normalized, topK=20, withWeight=True
        )
        
        # 4. 文化标签标注
        cultural_tags = self.extract_cultural_tags(normalized)
        
        # 5. 领域分类
        domain = self.classify_domain(normalized)
        
        # 6. 合规检查
        toxicity = self.check_toxicity(normalized)
        
        return HongKongTextSample(
            content=normalized,
            language=lang,
            domain=domain,
            cultural_tags=cultural_tags,
            toxicity_score=toxicity,
            source="hk_corpora_v3"
        )
    
    async def build_training_dataset(
        self, 
        raw_sources: List[str],
        min_quality_score: float = 0.7
    ) -> List[HongKongTextSample]:
        """构建高质量训练数据集"""
        samples = []
        
        async for source_url in self._crawl_sources(raw_sources):
            async for text in self._extract_texts(source_url):
                sample = await self.process_text(text)
                if sample.toxicity_score < 0.1:  # 低毒性过滤
                    samples.append(sample)
                    
                if len(samples) % 1000 == 0:
                    await self._log_progress(len(samples))
        
        # 质量评分与排序
        scored_samples = await self._quality_score(samples)
        return [
            s for s in scored_samples 
            if s['quality_score'] >= min_quality_score
        ]

package agentcore

import (
    "context"
    "fmt"
    "log"
    "sync"
    "time"
    
    "github.com/HKGAI-V3/pkg/orchestrator"
    "github.com/HKGAI-V3/pkg/memory"
    "github.com/HKGAI-V3/pkg/toolregistry"
)

// SuperAgentConfig 超级智能体配置
type SuperAgentConfig struct {
    MaxExecutionTime  time.Duration // 最大执行时间（默认48小时）
    CheckpointInterval time.Duration // 检查点保存间隔
    MaxRetries        int           // 单步最大重试次数
    ContextWindow    int           // 上下文窗口大小
}

// SuperAgent 超级智能体核心
type SuperAgent struct {
    config       SuperAgentConfig
    llm          LLMInterface
    memory       *memory.LongTermMemory
    tools        *toolregistry.ToolRegistry
    orchestrator *orchestrator.TaskOrchestrator
    
    mu           sync.RWMutex
    state        AgentState
    checkpoints  []*Checkpoint
}

// AgentState 智能体运行状态
type AgentState struct {
    CurrentTask     string
    TaskHistory     []TaskRecord
    ToolCalls       []ToolInvocation
    LLMInteractions int
    TotalTokens     int64
    StartTime       time.Time
    LastCheckpoint  time.Time
}

// TaskRecord 任务执行记录
type TaskRecord struct {
    TaskID      string
    Description string
    Status      TaskStatus
    StartTime   time.Time
    EndTime     time.Time
    Result      string
    Error       error
}

// 核心执行循环
func (sa *SuperAgent) Execute(ctx context.Context, goal string) (*ExecutionResult, error) {
    sa.mu.Lock()
    sa.state = AgentState{
        CurrentTask: goal,
        StartTime:   time.Now(),
    }
    sa.mu.Unlock()
    
    // 任务分解
    subTasks, err := sa.orchestrator.Decompose(ctx, goal)
    if err != nil {
        return nil, fmt.Errorf("任务分解失败: %w", err)
    }
    
    log.Printf("任务已分解为 %d 个子任务", len(subTasks))
    
    // 逐个执行子任务
    for i, task := range subTasks {
        select {
        case <-ctx.Done():
            return nil, ctx.Err()
        default:
        }
        
        log.Printf("正在执行子任务 [%d/%d]: %s", i+1, len(subTasks), task.Description)
        
        // 步骤执行
        result, err := sa.executeTask(ctx, task)
        if err != nil {
            // 错误恢复逻辑
            if task.RetryCount < sa.config.MaxRetries {
                task.RetryCount++
                log.Printf("任务执行失败，尝试重试 (%d/%d): %v", 
                    task.RetryCount, sa.config.MaxRetries, err)
                i-- // 重试当前任务
                continue
            }
            return nil, fmt.Errorf("任务 [%s] 执行失败: %w", task.ID, err)
        }
        
        // 检查点保存
        if err := sa.saveCheckpoint(ctx); err != nil {
            log.Printf("检查点保存失败: %v", err)
        }
        
        // 任务记录
        sa.recordTaskCompletion(task, result)
    }
    
    // 生成最终报告
    finalResult := sa.generateReport(ctx)
    
    return &ExecutionResult{
        Success:      true,
        TotalTime:    time.Since(sa.state.StartTime),
        TasksDone:    len(sa.state.TaskHistory),
        TokensUsed:   sa.state.TotalTokens,
        Output:       finalResult,
    }, nil
}

// executeTask 执行单个任务
func (sa *SuperAgent) executeTask(ctx context.Context, task *Task) (*TaskResult, error) {
    // 构建 prompt
    prompt := sa.buildPrompt(task)
    
    // 调用 LLM
    response, err := sa.llm.Chat(ctx, &LLMRequest{
        Messages: sa.buildMessages(prompt),
        MaxTokens: 4096,
        Temperature: 0.7,
    })
    if err != nil {
        return nil, err
    }
    
    // 更新 token 计数
    sa.mu.Lock()
    sa.state.TotalTokens += int64(response.Usage.TotalTokens)
    sa.state.LLMInteractions++
    sa.mu.Unlock()
    
    // 解析工具调用
    toolCalls := sa.parseToolCalls(response.Content)
    
    // 执行工具调用
    for _, call := range toolCalls {
        toolResult, err := sa.tools.Execute(ctx, call)
        if err != nil {
            return nil, fmt.Errorf("工具 [%s] 执行失败: %w", call.Name, err)
        }
        
        // 更新记忆
        sa.memory.Store(&memory.MemoryEntry{
            Type:       memory.ToolResult,
            Content:    toolResult,
            Timestamp:  time.Now(),
        })
    }
    
    return &TaskResult{
        TaskID:  task.ID,
        Output:  response.Content,
        Tools:   toolCalls,
    }, nil
}

// saveCheckpoint 定期保存检查点（支持断点续跑）
func (sa *SuperAgent) saveCheckpoint(ctx context.Context) error {
    sa.mu.RLock()
    stateCopy := sa.state
    sa.mu.RUnlock()
    
    checkpoint := &Checkpoint{
        ID:        fmt.Sprintf("cp_%d", time.Now().Unix()),
        Timestamp: time.Now(),
        State:     stateCopy,
    }
    
    // 持久化到存储
    return sa.persistCheckpoint(ctx, checkpoint)
}

// 检查点恢复
func (sa *SuperAgent) Restore(ctx context.Context, checkpointID string) error {
    checkpoint, err := sa.loadCheckpoint(ctx, checkpointID)
    if err != nil {
        return fmt.Errorf("加载检查点失败: %w", err)
    }
    
    sa.mu.Lock()
    sa.state = checkpoint.State
    sa.mu.Unlock()
    
    log.Printf("已从检查点 [%s] 恢复，执行时间: %s", 
        checkpointID, time.Since(sa.state.StartTime))
    
    return nil
}

import asyncio
from datetime import datetime, timedelta
from typing import Dict, List, Optional
from dataclasses import dataclass, field
import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

@dataclass
class AutonomousTask:
    """自主任务描述"""
    goal: str
    constraints: Dict[str, any] = field(default_factory=dict)
    deadline: Optional[datetime] = None
    callback_url: Optional[str] = None

@dataclass
class TaskProgress:
    """任务进度追踪"""
    task_id: str
    current_phase: str
    completed_steps: List[str]
    pending_steps: List[str]
    elapsed_time: timedelta
    estimated_remaining: Optional[timedelta]
    intermediate_results: Dict[str, any]

class AutonomousRunner:
    """无干预自主运行器"""
    
    def __init__(self, agent, checkpoint_manager, notifier):
        self.agent = agent
        self.checkpoint_manager = checkpoint_manager
        self.notifier = notifier
        self.max_single_run = timedelta(hours=28)
        self.checkpoint_interval = timedelta(minutes=30)
    
    async def run_autonomous(
        self, 
        task: AutonomousTask,
        progress_callback: Optional[callable] = None
    ) -> Dict:
        """
        启动自主运行模式
        
        这是一个典型的"放权"场景：用户给出目标后，系统将：
        1. 自动规划执行路径
        2. 按计划逐步执行
        3. 遇到问题自主决策（重试/跳过/调整策略）
        4. 完成后自动汇报
        
        全程无需用户干预。
        """
        start_time = datetime.now()
        progress = TaskProgress(
            task_id=task.goal[:32],
            current_phase="规划",
            completed_steps=[],
            pending_steps=[],
            elapsed_time=timedelta(0),
            estimated_remaining=None,
            intermediate_results={}
        )
        
        logger.info(f"🟢 自主任务启动: {task.goal}")
        
        try:
            # 阶段1: 深度规划
            logger.info("📋 阶段1: 深度任务规划...")
            execution_plan = await self._deep_planning(task.goal)
            progress.current_phase = "执行"
            progress.pending_steps = [s['description'] for s in execution_plan]
            
            # 阶段2: 分步执行（核心循环）
            for step_idx, step in enumerate(execution_plan):
                # 检查是否超时
                if datetime.now() - start_time > self.max_single_run:
                    logger.warning("⚠️ 接近最大运行时长，保存检查点")
                    await self._emergency_checkpoint(progress, task)
                    break
                
                logger.info(f"🔄 执行步骤 [{step_idx+1}/{len(execution_plan)}]: {step['description']}")
                
                try:
                    # 执行当前步骤
                    step_result = await self._execute_step(step)
                    
                    # 进度更新
                    progress.completed_steps.append(step['description'])
                    progress.pending_steps.pop(0)
                    progress.intermediate_results[step['id']] = step_result
                    progress.elapsed_time = datetime.now() - start_time
                    
                    # 定期检查点
                    if progress.elapsed_time % self.checkpoint_interval < timedelta(minutes=1):
                        await self.checkpoint_manager.save(progress)
                        logger.info(f"💾 检查点已保存 (已运行 {progress.elapsed_time})")
                    
                    # 用户回调（如配置）
                    if progress_callback:
                        await progress_callback(progress)
                        
                except Exception as e:
                    logger.error(f"❌ 步骤执行失败: {e}")
                    # 智能恢复策略
                    recovery_action = await self._intelligent_recovery(step, e)
                    if recovery_action == "skip":
                        progress.completed_steps.append(f"{step['description']} [已跳过]")
                        logger.warning(f"⏭️ 已跳过失败步骤")
                    elif recovery_action == "retry":
                        step_idx -= 1  # 重试当前步骤
                        logger.info("🔁 重试当前步骤")
            
            # 阶段3: 结果整合
            logger.info("📊 阶段3: 整合分析结果...")
            final_report = await self._compile_report(progress.intermediate_results)
            
            # 阶段4: 通知与归档
            await self._finalize(task, final_report)
            
            elapsed = datetime.now() - start_time
            logger.info(f"✅ 任务完成！总耗时: {elapsed}")
            
            return {
                "status": "completed",
                "total_time": elapsed,
                "steps_completed": len(progress.completed_steps),
                "report": final_report
            }
            
        except Exception as e:
            logger.error(f"💥 严重错误: {e}")
            await self._handle_critical_failure(task, progress, e)
            raise

    async def _deep_planning(self, goal: str) -> List[Dict]:
        """深度任务规划：让 LLM 自主设计执行路径"""
        planning_prompt = f"""
你是一个专业的任务规划专家。用户的目标是：

{goal}

请设计一个详细的多步骤执行计划。每个步骤应该：
1. 有明确的具体目标
2. 可以独立验证完成度
3. 产出可供后续步骤使用的中间结果

请以 JSON 格式返回计划，格式如下：
{{
    "steps": [
        {{
            "id": "step_1",
            "description": "具体步骤描述",
            "tools_needed": ["搜索", "分析", "写作"],
            "estimated_time": "30分钟",
            "validation": "如何验证此步骤完成"
        }}
    ],
    "total_estimated_time": "预估总时间",
    "key_milestones": ["关键里程碑"]
}}
"""
        response = await self.agent.chat(planning_prompt)
        import json
        return json.loads(response.content)['steps']
    
    async def _execute_step(self, step: Dict) -> Dict:
        """执行单个步骤"""
        execution_prompt = f"""
当前任务步骤: {step['description']}

请执行此步骤，使用必要的工具（如果有），并返回：
1. 详细的执行结果
2. 发现的关键信息
3. 遇到的问题（如果有）

记住：你正在以一个专业的'数字员工'身份工作，需要主动思考和解决问题。
"""
        result = await self.agent.chat(execution_prompt)
        return {"content": result.content, "step_id": step['id']}
    
    async def _intelligent_recovery(self, step: Dict, error: Exception) -> str:
        """智能恢复策略"""
        if "超时" in str(error):
            return "retry"  # 超时可以重试
        elif "数据不可用" in str(error):
            return "skip"   # 数据问题可以跳过
        return "retry"

import numpy as np
from typing import List, Tuple, Optional
from dataclasses import dataclass

@dataclass
class CompressionConfig:
    """压缩配置"""
    target_ratio: float = 0.1  # 目标压缩比：10:1
    min_token_length: int = 2
    use_semantic_clustering: bool = True
    preserve_entities: bool = True  # 保留实体名称

class TokenCompressor:
    """
    HKGAI V3 Token 压缩引擎
    
    核心技术：
    1. 语义哈希：相似含义的文本映射到同一哈希
    2. 动态词汇表：根据领域自适应调整
    3. 实体保护：确保专有名词不被误压缩
    4. 层级压缩：句子→段落→文档的多级压缩
    """
    
    def __init__(self, config: CompressionConfig):
        self.config = config
        self.semantic_vectors = self._load_semantic_model()
        self.entity_recognizer = self._init_entity_recognizer()
        self.dynamic_vocab = self._build_dynamic_vocab()
    
    def compress(self, text: str, context: Optional[dict] = None) -> str:
        """
        主压缩入口
        
        压缩策略：
        1. 实体识别与保护
        2. 语义聚类与哈希
        3. 动态词汇替换
        4. 上下文感知的指代消解
        """
        # Step 1: 实体识别
        entities = self.entity_recognizer.extract(text)
        protected_ranges = [(e.start, e.end, e.type) for e in entities]
        
        # Step 2: 语义哈希
        semantic_hash = self._compute_semantic_hash(text)
        
        # Step 3: 动态词汇压缩
        compressed = self._dynamic_vocab_compress(text, context or {})
        
        # Step 4: 指代消解与压缩
        compressed = self._resolve_and_compress_pronouns(
            compressed, context or {}
        )
        
        # Step 5: 恢复实体
        compressed = self._restore_entities(compressed, entities)
        
        return compressed
    
    def _compute_semantic_hash(self, text: str) -> str:
        """
        语义哈希：将相似含义的文本映射到相同哈希
        
        原理：
        - 使用预训练的语义嵌入模型获取文本向量
        - 对向量进行量化，生成固定长度的哈希值
        - 相似的文本会有相同或相近的哈希
        """
        # 获取语义向量
        embedding = self.semantic_vectors.encode(text)
        
        # 量化压缩（将浮点向量转为离散哈希）
        # 使用 product quantization 进一步压缩
        compressed = self._product_quantize(embedding, self.config.target_ratio)
        
        # 生成哈希字符串
        hash_str = self._vector_to_hash(compressed)
        
        return hash_str
    
    def _dynamic_vocab_compress(
        self, 
        text: str, 
        context: dict
    ) -> str:
        """
        动态词汇表压缩
        
        思想：高频共现的词对可以合并为一个 token
        例如："香港"+"特别行政区" -> "香港特区"
        """
        compressed = text
        
        # 构建当前上下文相关的词汇表
        context_vocab = self._build_context_vocab(context)
        
        # 贪婪替换最长的匹配
        for phrase, replacement in sorted(
            context_vocab.items(), 
            key=lambda x: len(x[0]), 
            reverse=True
        ):
            if phrase in compressed and len(phrase) > self.config.min_token_length:
                compressed = compressed.replace(phrase, replacement)
        
        return compressed
    
    def _resolve_and_compress_pronouns(
        self,
        text: str,
        context: dict
    ) -> str:
        """
        指代消解与压缩
        
        将代词还原为实际指代对象，
        然后用更紧凑的表达替换
        """
        # 识别指代链
        referents = self._resolve_referents(text, context)
        
        compressed = text
        for pronoun, entity in referents.items():
            # 使用上下文感知的简短表达
            short_form = self._get_shortest_reference(entity, context)
            compressed = compressed.replace(pronoun, short_form)
        
        return compressed

class HierarchicalCompressor:
    """
    层级压缩器：句子 → 段落 → 文档的多级压缩
    
    核心思想：信息在层级间的冗余可以被消除
    """
    
    def __init__(self, base_compressor: TokenCompressor):
        self.base = base_compressor
    
    def compress_document(
        self, 
        document: str,
        preserve_structure: bool = True
    ) -> str:
        """
        文档级压缩
        
        策略：
        1. 句子级摘要（提取关键信息）
        2. 段落级凝练（消除段落内冗余）
        3. 文档级整合（提取核心主题）
        """
        paragraphs = document.split('\n\n')
        compressed_paragraphs = []
        
        for para in paragraphs:
            if len(para.strip()) < 50:
                continue
            
            # 段落压缩
            compressed_para = self._compress_paragraph(para)
            compressed_paragraphs.append(compressed_para)
        
        if preserve_structure:
            return '\n\n'.join(compressed_paragraphs)
        return ' '.join(compressed_paragraphs)
    
    def _compress_paragraph(self, paragraph: str) -> str:
        """段落级压缩"""
        sentences = self._split_sentences(paragraph)
        
        # 识别核心句子（主题句）
        core_sentences = self._identify_core_sentences(sentences)
        
        # 对非核心句子进行摘要
        compressed = []
        for sent in sentences:
            if sent in core_sentences:
                compressed.append(sent)  # 保留核心句
            else:
                # 压缩非核心句
                summary = self._summarize_sentence(sent)
                compressed.append(summary)
        
        return ' '.join(compressed)

package agentruntime

import (
    "context"
    "runtime"
    "sync/atomic"
    "time"
    
    "github.com/HKGAI-V3/pkg/memory"
    "github.com/HKGAI-V3/pkg/resumable"
)

// RuntimeOptimizer 运行时长优化器
type RuntimeOptimizer struct {
    memoryManager   *memory.SmartMemoryManager
    checkpointMgr   *resumable.CheckpointManager
    resourceLimiter *ResourceLimiter
    
    // 运行时指标
    totalRuns       int64
    successfulRuns  int64
    avgRunTime      time.Duration
    maxAchievedTime time.Duration
}

// OptimizeForLongRun 针对长时间运行的优化
func (o *RuntimeOptimizer) OptimizeForLongRun(ctx context.Context, cfg *RuntimeConfig) {
    // 1. 内存管理优化
    o.optimizeMemoryManagement()
    
    // 2. 资源限制调整
    o.adjustResourceLimits(cfg)
    
    // 3. 缓存策略优化
    o.optimizeCacheStrategy(cfg.MaxDuration)
    
    // 4. 监控阈值调整
    o.tuneMonitoringThresholds(cfg)
}

func (o *RuntimeOptimizer) optimizeMemoryManagement() {
    // 智能内存管理：定期压缩历史数据
    // 近期数据保持高精度，远期数据降采样
    o.memoryManager.SetPolicy(&memory.Policy{
        RecentWindow:    24 * time.Hour,      // 24小时内数据全精度
        MediumWindow:    7 * 24 * time.Hour,  // 7天内数据50%精度
        LongTermWindow:  30 * 24 * time.Hour, // 30天内数据10%精度
        CompressionRatio: 0.1,                 // 长期数据压缩到10%
    })
    
    // 主动垃圾回收调度
    go o.scheduleProactiveGC()
}

func (o *RuntimeOptimizer) scheduleProactiveGC() {
    ticker := time.NewTicker(5 * time.Minute)
    defer ticker.Stop()
    
    for range ticker.C {
        // 在内存使用率达到70%时触发主动GC
        var m runtime.MemStats
        runtime.ReadMemStats(&m)
        
        usagePercent := float64(m.Alloc) / float64(m.Sys) * 100
        if usagePercent > 70 {
            // 记录当前状态
            log.Printf("内存使用率: %.1f%%, 触发主动GC", usagePercent)
            
            // 分批释放内存
            o.memoryManager.Compact()
            
            // 建议runtime进行GC
            runtime.GC()
        }
    }
}

# 政务场景：法规解读与政策匹配
class GovernmentDocAgent:
    """
    港府法规智能助手
    
    功能：
    1. 解读香港法例条文
    2. 匹配企业适用的优惠政策
    3. 生成合规建议报告
    """
    
    def __init__(self, hkgai_model):
        self.model = hkgai_model
        self.legal_knowledge = self._load_legal_kb()
        self.policy_db = self._load_policy_db()
    
    async def process_enterprise_request(self, enterprise_info: dict) -> dict:
        """
        处理企业咨询请求
        
        输入：企业基本信息（行业、规模、业务范围等）
        输出：适用的政策法规清单 + 合规建议
        """
        # 1. 法规检索
        relevant_laws = await self._search_relevant_regulations(
            enterprise_info['industry'],
            enterprise_info['business_scope']
        )
        
        # 2. 政策匹配
        applicable_policies = await self._match_policies(
            enterprise_info,
            self.policy_db
        )
        
        # 3. 合规风险评估
        risk_assessment = await self._assess_compliance_risks(
            enterprise_info,
            relevant_laws
        )
        
        # 4. 生成报告
        report = await self._generate_compliance_report(
            enterprise_info=enterprise_info,
            regulations=relevant_laws,
            policies=applicable_policies,
            risks=risk_assessment
        )
        
        return report

# 金融场景：市场分析报告生成
class FinanceReportAgent:
    """
    金融市场分析智能体
    
    特点：
    - 可连续运行数小时进行深度分析
    - 自动抓取多源数据
    - 生成结构化分析报告
    """
    
    async def generate_market_report(
        self, 
        market: str = "hk_stock",
        period: str = "1y",
        focus_areas: List[str] = None
    ) -> str:
        """
        生成市场分析报告
        
        整个过程可持续运行数小时，
        期间无需人工干预
        """
        if focus_areas is None:
            focus_areas = ["趋势分析", "板块轮动", "风险提示"]
        
        # 阶段1：数据采集
        market_data = await self._collect_market_data(market, period)
        news_data = await self._collect_news_sentiment(market)
        macro_data = await self._collect_macro_indicators(market)
        
        # 阶段2：多维度分析
        analyses = {}
        for area in focus_areas:
            analyses[area] = await self._analyze_dimension(
                area, 
                market_data, 
                news_data,
                macro_data
            )
        
        # 阶段3：综合研判
        synthesis = await self._synthesize_findings(analyses)
        
        # 阶段4：报告生成
        report = self._format_report(
            title=f"{market}市场分析报告",
            period=period,
            analyses=analyses,
            synthesis=synthesis,
            data_sources=[...],
            generated_at=datetime.now()
        )
        
        return report

// ClawNet: 超级智能体开源框架
// 企业可以在此基础上构建定制化的 AI Agent

package clawnet

import (
    "context"
    "fmt"
    
    "github.com/HKGAI-V3/clawnet/pkg/agent"
    "github.com/HKGAI-V3/clawnet/pkg/tools"
    "github.com/HKGAI-V3/clawnet/pkg/memory"
)

// AgentFactory 智能体工厂
type AgentFactory struct {
    toolRegistry *tools.Registry
    memoryStore  *memory.Store
    llmProvider  LLMProvider
}

// NewCustomAgent 创建自定义智能体
func (f *AgentFactory) NewCustomAgent(cfg *AgentConfig) (*agent.Agent, error) {
    ag := agent.New(agent.Options{
        LLM:          f.llmProvider.GetLLM(cfg.Model),
        Tools:        f.toolRegistry,
        Memory:       f.memoryStore,
        MaxSteps:     cfg.MaxSteps,
        Checkpoint:   cfg.EnableCheckpoint,
    })
    
    // 添加自定义技能
    for _, skill := range cfg.CustomSkills {
        if err := ag.AddSkill(skill); err != nil {
            return nil, fmt.Errorf("添加技能失败 [%s]: %w", skill.Name, err)
        }
    }
    
    return ag, nil
}

// Example: 构建企业知识库问答 Agent
func Example_EnterpriseKB() {
    factory := NewAgentFactory()
    
    // 创建配置
    cfg := &AgentConfig{
        Model:            "hkgai-v3",
        MaxSteps:         50,
        EnableCheckpoint: true,
        CustomSkills: []Skill{
            // 知识库检索技能
            {
                Name:        "kb_search",
                Description: "搜索企业知识库",
                Handler:     tools.KBSearchHandler,
            },
            // 文档处理技能
            {
                Name:        "doc_process",
                Description: "处理各类企业文档",
                Handler:     tools.DocProcessHandler,
            },
        },
    }
    
    // 创建智能体
    myAgent, err := factory.NewCustomAgent(cfg)
    if err != nil {
        panic(err)
    }
    
    // 运行
    result, err := myAgent.Run(context.Background(), 
        "请总结本周公司重要公告，并指出需要我关注的事项")
    if err != nil {
        panic(err)
    }
    
    fmt.Println(result)
}

# 香港 AI 双轨策略实现框架
class HongKongAIStrategy:
    """
    香港 AI 发展战略框架
    
    轨道一：AI 产业化
    - 培育本土 AI 企业
    - 建立 AI 产业园区
    - 完善 AI 创业生态
    
    轨道二：产业 AI 化
    - 推动传统行业数字化转型
    - 建设 AI 公共服务平台
    - 推广 AI 在金融、贸易、物流等领域的应用
    """
    
    def __init__(self):
        self.industries = [
            "金融科技",
            "国际贸易",
            "物流航运",
            "专业服务",
            "医疗健康",
            "创意产业"
        ]
        
        self.ai_platforms = {
            "HKChat": "市民 AI 助手（港话通）",
            "HKPilot": "政务文档处理（港文通）",
            "HKMeeting": "智能会议助手（港会通）",
            "HKLaw": "法律合规问答（港法通）",
            "HKEnv": "环保监测（港环通）",
            "HKMusic": "音乐创作（港乐通）"
        }
    
    def assess_industry_readiness(self, industry: str) -> dict:
        """评估行业 AI 成熟度"""
        indicators = {
            "金融科技": {"数字化程度": 85, "AI 采用率": 60, "人才储备": 70},
            "国际贸易": {"数字化程度": 65, "AI 采用率": 35, "人才储备": 50},
            "物流航运": {"数字化程度": 75, "AI 采用率": 45, "人才储备": 55},
        }
        return indicators.get(industry, {})
    
    def recommend_ai_solutions(self, industry: str) -> List[str]:
        """推荐适合该行业的 AI 解决方案"""
        solutions = {
            "金融科技": [
                "智能投顾",
                "风险控制模型",
                "反欺诈系统",
                "智能客服"
            ],
            "国际贸易": [
                "智能报关",
                "供应链优化",
                "贸易合规检查",
                "多语言合同分析"
            ],
            "物流航运": [
                "智能调度",
                "路径优化",
                "港口自动化",
                "冷链监控"
            ]
        }
        return solutions.get(industry, [])

# 主权 AI 能力评估框架
class SovereignAICapability:
    """
    主权 AI 能力评估
    
    评估维度：
    1. 算力自主度：本土芯片占比
    2. 数据自主度：数据本地化程度
    3. 模型自主度：本土大模型能力
    4. 人才自主度：AI 人才储备
    5. 治理自主度：AI 监管能力
    """
    
    def evaluate(self, region: str) -> dict:
        """评估某地区的主权 AI 能力"""
        
        # 模拟评估结果
        if region == "Hong Kong":
            return {
                "compute_independence": 65,  # 部分依赖
                "data_sovereignty": 80,      # 较高
                "model_capability": 70,      # 中等偏上
                "talent_pool": 75,           # 良好
                "governance": 85,            # 完善
                "overall_score": 75,
                "recommendations": [
                    "加强本土芯片研发",
                    "扩大 AI 人才培养",
                    "深化与大湾区算力合作"
                ]
            }
        
        return {}
    
    def benchmark_regions(self) -> List[dict]:
        """对标全球主要地区"""
        return [
            {"region": "USA", "score": 95, "strength": "技术领先"},
            {"region": "China", "score": 85, "strength": "产业完整"},
            {"region": "EU", "score": 75, "strength": "治理规范"},
            {"region": "Singapore", "score": 70, "strength": "国际化"},
            {"region": "Hong Kong", "score": 75, "strength": "跨境枢纽"}
        ]