type MemoryProvenance struct {
    SourceType         SourceType    // 来源类型
    Confidence         float64       // 置信度 (0.0-1.0)
    Sources            []string      // 源ID列表
    CreatedAt          time.Time     // 创建时间
    UpdatedAt          time.Time     // 更新时间
    Version            int           // 版本号
    IsExplicit         bool          // 是否显式创建
    CorroborationCount int           // 佐证数量
    LastAccessedAt     *time.Time    // 最后访问时间
    Tags               []string      // 标签
}

最终置信度 = 基础置信度 × 衰减因子 × 佐证提升 × 新鲜度权重

邮箱: john.doe@example.com → j*******@example.com
电话: 13812345678 → 138****5678
信用卡: 4532148803436464 → 4532********6464

邮箱: user@example.com → [EMAIL]
电话: 13812345678 → [CHINESE_PHONE]
信用卡: 4532148803436464 → [CREDIT_CARD]

任何 PII → [HASH:a3f58b1d...]

piiMiddleware := security.NewDefaultPIIMiddleware()
agent.AddMiddleware(piiMiddleware)

// 自动脱敏所有发往 LLM 的消息

func buildByteToRuneMap(text string) []int {
    // 处理 UTF-8 多字节字符（如中文）
    // 确保脱敏不会破坏多字节字符
}

type ConsolidationEngine struct {
    memory              *SemanticMemory
    strategy            ConsolidationStrategy
    llmProvider         LLMProvider
    config              ConsolidationConfig
}

输入:
- "User prefers dark mode"
- "User likes dark theme"
- "User wants dark mode UI"

输出:
- "User prefers dark mode theme for the UI"

输入:
- "User likes coffee" (置信度 0.6)
- "User actually prefers tea" (置信度 0.9)

输出:
- "User prefers tea (previously mentioned liking coffee)"

输入 (5条记忆):
- "User lives in New York"
- "User works at Tech Corp"
- "User has 5 years experience"
- "User specializes in AI"
- "User graduated from MIT"

输出:
- "User is an AI specialist with 5 years of experience,
   graduated from MIT, currently working at Tech Corp in New York"

You are a memory consolidation assistant.
The following memory entries are redundant (saying similar things).
Please merge them into a single, concise memory that captures all the important information.

Instructions:
- Merge the information into one clear, concise statement
- Preserve all important details
- Remove redundancy
- Keep the same tone and style
- Output only the merged memory, without explanation

You are a memory conflict resolution assistant.
The following memory entries contain conflicting information.
Please analyze them and create a single, accurate memory.

Instructions:
- Analyze the conflicts carefully
- Prefer information from higher confidence sources
- If information is contradictory, indicate uncertainty
- Provide a balanced, objective statement

consolidated.Provenance.Sources = [
    "original-memory-1",
    "original-memory-2",
    "original-memory-3",
]
consolidated.Provenance.CorroborationCount = 3

Application Layer
    ├─ Agent
    └─ Middleware
        └─ PII Redaction Middleware

Memory Layer
    ├─ Semantic Memory
    ├─ Working Memory
    └─ Consolidation Engine

Storage Layer
    ├─ Vector Store (pgvector)
    ├─ Provenance Store
    └─ Lineage Graph

User Input
    ↓
PII Detection & Redaction
    ↓
Embedding Generation
    ↓
Provenance Creation
    ↓
Lineage Tracking
    ↓
Vector Store

Query
    ↓
Embedding Generation
    ↓
Vector Search
    ↓
Confidence Filtering
    ↓
Freshness Ranking
    ↓
Results

Trigger (Auto/Manual)
    ↓
Similarity Clustering
    ↓
Strategy Selection
    ↓
LLM Consolidation
    ↓
Provenance Merging
    ↓
Save & Cleanup

// 避免重复计算
cache := make(map[string]float64)

// 一次调用处理多条记忆
vecs, err := embedder.EmbedText(ctx, texts)

// 并发处理不相关的记忆组
for _, group := range groups {
    go consolidate(group)
}

package main

import (
    "context"
    "github.com/astercloud/aster/pkg/agent"
    "github.com/astercloud/aster/pkg/memory"
    "github.com/astercloud/aster/pkg/security"
)

func main() {
    ctx := context.Background()

    // 1. 创建语义内存（启用 Provenance）
    semanticMemory := memory.NewSemanticMemory(memory.SemanticMemoryConfig{
        Store:                vectorStore,
        Embedder:             embedder,
        EnableProvenance:     true,
        ConfidenceCalculator: memory.NewConfidenceCalculator(memory.ConfidenceConfig{
            DecayHalfLife: 7 * 24 * time.Hour,
        }),
        LineageManager:       memory.NewLineageManager(),
    })

    // 2. 创建 PII 脱敏中间件
    piiMiddleware := security.NewDefaultPIIMiddleware()

    // 3. 创建合并引擎
    consolidationEngine := memory.NewConsolidationEngine(
        semanticMemory,
        memory.NewRedundancyStrategy(0.85),
        llmProvider,
        memory.DefaultConsolidationConfig(),
    )

    // 4. 创建 Agent
    agent := agent.NewAgent(agent.Config{
        Name:   "my-agent",
        Memory: semanticMemory,
    })

    // 5. 添加中间件
    agent.AddMiddleware(piiMiddleware)

    // 6. 定期自动合并
    go func() {
        ticker := time.NewTicker(1 * time.Hour)
        defer ticker.Stop()

        for range ticker.C {
            if consolidationEngine.ShouldAutoConsolidate() {
                result, _ := consolidationEngine.Consolidate(ctx)
                log.Printf("Consolidated %d memories", result.MergedCount)
            }
        }
    }()

    // 7. 运行 Agent
    agent.Run(ctx)
}