Integration and Alignment of Multimodal AI: Cross-Modal Understanding from Text-Image to Video-Audio

package multimodal

import (
    "context"
    "fmt"
    "math"
    "sync"
    "time"
    
    "github.com/yourorg/multimodal/embedding"
    "github.com/yourorg/multimodal/types"
)

// AlignmentEngine 跨模态对齐引擎
type AlignmentEngine struct {
    // 各模态的编码器
    textEncoder   *TextEncoder
    imageEncoder  *ImageEncoder
    videoEncoder  *VideoEncoder
    audioEncoder  *AudioEncoder
    
    // 共享语义空间投影矩阵
    projectionMatrix *Matrix
    
    // 缓存管理
    cache *EmbeddingCache
    
    // 配置参数
    config *AlignmentConfig
}

// AlignmentConfig 对齐引擎配置
type AlignmentConfig struct {
    EmbeddingDim    int     // 嵌入向量维度
    Temperature     float64 // 对比学习温度参数
    TopK            int     // 检索返回的top-k结果
    UseCache        bool    // 是否使用缓存
    CacheTTL        time.Duration // 缓存过期时间
    BatchSize       int     // 批处理大小
    MaxConcurrent   int     // 最大并发数
}

// AlignmentResult 对齐结果
type AlignmentResult struct {
    QueryID     string
    Modality    string
    Matches     []MatchItem
    Latency     time.Duration
    Confidence  float64
}

// MatchItem 匹配项
type MatchItem struct {
    ID          string
    Modality    string
    Score       float64
    Metadata    map[string]interface{}
}

// NewAlignmentEngine 创建对齐引擎实例
func NewAlignmentEngine(config *AlignmentConfig) *AlignmentEngine {
    return &AlignmentEngine{
        config: config,
        cache:  NewEmbeddingCache(config.CacheTTL),
        // 初始化各编码器
        textEncoder:   NewTextEncoder(config.EmbeddingDim),
        imageEncoder:  NewImageEncoder(config.EmbeddingDim),
        videoEncoder:  NewVideoEncoder(config.EmbeddingDim),
        audioEncoder:  NewAudioEncoder(config.EmbeddingDim),
    }
}

// CrossModalSearch 跨模态搜索
// query: 查询内容
// targetModality: 目标模态类型
// ctx: 上下文，用于取消操作
func (e *AlignmentEngine) CrossModalSearch(
    ctx context.Context,
    query types.MultimodalQuery,
    targetModality string,
) (*AlignmentResult, error) {
    startTime := time.Now()
    
    // 1. 获取查询的嵌入向量
    queryEmbedding, err := e.encodeQuery(ctx, query)
    if err != nil {
        return nil, fmt.Errorf("query encoding failed: %w", err)
    }
    
    // 2. 投影到共享语义空间
    projectedQuery := e.projectToSharedSpace(queryEmbedding)
    
    // 3. 在目标模态中检索
    matches, err := e.searchInModality(ctx, projectedQuery, targetModality)
    if err != nil {
        return nil, fmt.Errorf("search failed: %w", err)
    }
    
    // 4. 计算置信度
    confidence := e.calculateConfidence(matches)
    
    return &AlignmentResult{
        QueryID:    query.ID,
        Modality:   targetModality,
        Matches:    matches,
        Latency:    time.Since(startTime),
        Confidence: confidence,
    }, nil
}

// encodeQuery 编码查询内容
func (e *AlignmentEngine) encodeQuery(
    ctx context.Context,
    query types.MultimodalQuery,
) (*embedding.Embedding, error) {
    // 根据查询类型选择合适的编码器
    switch query.Type {
    case types.TextQuery:
        return e.textEncoder.Encode(ctx, query.Content)
    case types.ImageQuery:
        return e.imageEncoder.Encode(ctx, query.Content)
    case types.VideoQuery:
        return e.videoEncoder.Encode(ctx, query.Content)
    case types.AudioQuery:
        return e.audioEncoder.Encode(ctx, query.Content)
    default:
        return nil, fmt.Errorf("unsupported query type: %s", query.Type)
    }
}

// projectToSharedSpace 投影到共享语义空间
func (e *AlignmentEngine) projectToSharedSpace(emb *embedding.Embedding) *embedding.Embedding {
    // 使用投影矩阵进行线性变换
    projected := &embedding.Embedding{
        Vector: make([]float64, e.config.EmbeddingDim),
    }
    
    for i := 0; i < e.config.EmbeddingDim; i++ {
        for j := 0; j < len(emb.Vector); j++ {
            projected.Vector[i] += e.projectionMatrix.Data[i][j] * emb.Vector[j]
        }
    }
    
    // L2归一化
    e.normalize(projected)
    
    return projected
}

// searchInModality 在指定模态中检索
func (e *AlignmentEngine) searchInModality(
    ctx context.Context,
    query *embedding.Embedding,
    targetModality string,
) ([]MatchItem, error) {
    // 获取目标模态的索引
    index, err := e.getIndex(targetModality)
    if err != nil {
        return nil, err
    }
    
    // 计算余弦相似度
    similarities := make([]struct {
        id    string
        score float64
    }, len(index.Items))
    
    var wg sync.WaitGroup
    sem := make(chan struct{}, e.config.MaxConcurrent)
    
    for i, item := range index.Items {
        wg.Add(1)
        go func(idx int, item IndexItem) {
            defer wg.Done()
            sem <- struct{}{}
            defer func() { <-sem }()
            
            score := e.cosineSimilarity(query.Vector, item.Embedding.Vector)
            similarities[idx] = struct {
                id    string
                score float64
            }{id: item.ID, score: score}
        }(i, item)
    }
    
    wg.Wait()
    
    // 排序并返回top-k
    e.sortByScore(similarities)
    
    matches := make([]MatchItem, 0, e.config.TopK)
    for i := 0; i < e.config.TopK && i < len(similarities); i++ {
        matches = append(matches, MatchItem{
            ID:       similarities[i].id,
            Modality: targetModality,
            Score:    similarities[i].score,
        })
    }
    
    return matches, nil
}

// cosineSimilarity 计算余弦相似度
func (e *AlignmentEngine) cosineSimilarity(a, b []float64) float64 {
    if len(a) != len(b) {
        return 0
    }
    
    var dotProduct, normA, normB float64
    for i := 0; i < len(a); i++ {
        dotProduct += a[i] * b[i]
        normA += a[i] * a[i]
        normB += b[i] * b[i]
    }
    
    if normA == 0 || normB == 0 {
        return 0
    }
    
    return dotProduct / (math.Sqrt(normA) * math.Sqrt(normB))
}

// normalize L2归一化
func (e *AlignmentEngine) normalize(emb *embedding.Embedding) {
    var norm float64
    for _, v := range emb.Vector {
        norm += v * v
    }
    norm = math.Sqrt(norm)
    
    if norm > 0 {
        for i := range emb.Vector {
            emb.Vector[i] /= norm
        }
    }
}

// calculateConfidence 计算检索结果的置信度
func (e *AlignmentEngine) calculateConfidence(matches []MatchItem) float64 {
    if len(matches) == 0 {
        return 0
    }
    
    // 基于top-1得分和得分分布计算置信度
    topScore := matches[0].Score
    
    if len(matches) > 1 {
        scoreGap := topScore - matches[1].Score
        // 得分差距越大，置信度越高
        return math.Min(1.0, topScore*(1+scoreGap))
    }
    
    return topScore
}

// BatchCrossModalSearch 批量跨模态搜索
func (e *AlignmentEngine) BatchCrossModalSearch(
    ctx context.Context,
    queries []types.MultimodalQuery,
    targetModality string,
) ([]*AlignmentResult, error) {
    results := make([]*AlignmentResult, len(queries))
    var wg sync.WaitGroup
    
    // 分批处理
    for i := 0; i < len(queries); i += e.config.BatchSize {
        end := i + e.config.BatchSize
        if end > len(queries) {
            end = len(queries)
        }
        
        for j := i; j < end; j++ {
            wg.Add(1)
            go func(idx int) {
                defer wg.Done()
                result, err := e.CrossModalSearch(ctx, queries[idx], targetModality)
                if err != nil {
                    // 记录错误，继续处理其他查询
                    results[idx] = &AlignmentResult{
                        QueryID:  queries[idx].ID,
                        Modality: targetModality,
                        Matches:  []MatchItem{},
                    }
                    return
                }
                results[idx] = result
            }(j)
        }
        
        wg.Wait()
    }
    
    return results, nil
}

package multimodal

import (
    "context"
    "math"
    "sync"
)

// TemporalAlignmentEngine 时序对齐引擎
type TemporalAlignmentEngine struct {
    // 时序编码器
    videoTemporalEncoder *VideoTemporalEncoder
    audioTemporalEncoder *AudioTemporalEncoder
    
    // 动态时间规整器
    dtw *DynamicTimeWarping
    
    config *TemporalConfig
}

// TemporalConfig 时序对齐配置
type TemporalConfig struct {
    FrameRate       int     // 视频帧率
    SampleRate      int     // 音频采样率
    WindowSize      int     // 对齐窗口大小
    AlignmentMethod string  // 对齐方法：dtw, attention, hybrid
}

// TemporalAlignmentResult 时序对齐结果
type TemporalAlignmentResult struct {
    Alignments  []TimeAlignment
    Confidence  float64
    TotalFrames int
    TotalSamples int
}

// TimeAlignment 时间对齐点
type TimeAlignment struct {
    VideoTimestamp float64 // 视频时间戳（秒）
    AudioTimestamp float64 // 音频时间戳（秒）
    Score         float64 // 对齐得分
}

// AlignVideoAudio 对齐视频和音频
func (e *TemporalAlignmentEngine) AlignVideoAudio(
    ctx context.Context,
    videoFrames []VideoFrame,
    audioSamples []AudioSample,
) (*TemporalAlignmentResult, error) {
    // 1. 提取视频时序特征
    videoFeatures := e.videoTemporalEncoder.ExtractFeatures(videoFrames)
    
    // 2. 提取音频时序特征
    audioFeatures := e.audioTemporalEncoder.ExtractFeatures(audioSamples)
    
    // 3. 动态时间规整对齐
    alignments, score := e.dtw.Align(videoFeatures, audioFeatures)
    
    // 4. 计算置信度
    confidence := e.calculateAlignmentConfidence(alignments, score)
    
    return &TemporalAlignmentResult{
        Alignments:  alignments,
        Confidence:  confidence,
        TotalFrames: len(videoFrames),
        TotalSamples: len(audioSamples),
    }, nil
}

// DynamicTimeWarping 动态时间规整实现
type DynamicTimeWarping struct {
    windowSize int
}

// Align 执行DTW对齐
func (dtw *DynamicTimeWarping) Align(
    videoFeatures []FeatureVector,
    audioFeatures []FeatureVector,
) ([]TimeAlignment, float64) {
    n := len(videoFeatures)
    m := len(audioFeatures)
    
    // 初始化代价矩阵
    cost := make([][]float64, n)
    for i := range cost {
        cost[i] = make([]float64, m)
    }
    
    // 计算欧氏距离矩阵
    for i := 0; i < n; i++ {
        for j := 0; j < m; j++ {
            cost[i][j] = euclideanDistance(videoFeatures[i], audioFeatures[j])
        }
    }
    
    // 动态规划计算累积代价
    dp := make([][]float64, n)
    for i := range dp {
        dp[i] = make([]float64, m)
    }
    
    dp[0][0] = cost[0][0]
    
    // 初始化边界
    for i := 1; i < n; i++ {
        dp[i][0] = dp[i-1][0] + cost[i][0]
    }
    for j := 1; j < m; j++ {
        dp[0][j] = dp[0][j-1] + cost[0][j]
    }
    
    // 填充DP矩阵（带窗口限制）
    for i := 1; i < n; i++ {
        start := max(0, i-dtw.windowSize)
        end := min(m, i+dtw.windowSize)
        for j := start; j < end; j++ {
            dp[i][j] = cost[i][j] + min(
                dp[i-1][j],   // 插入
                dp[i][j-1],   // 删除
                dp[i-1][j-1], // 匹配
            )
        }
    }
    
    // 回溯找到最优路径
    alignments := dtw.backtrack(dp, n-1, m-1)
    
    // 计算对齐得分（归一化后的累积代价）
    score := dp[n-1][m-1] / float64(len(alignments))
    
    return alignments, score
}

// backtrack 回溯找到最优对齐路径
func (dtw *DynamicTimeWarping) backtrack(
    dp [][]float64,
    i, j int,
) []TimeAlignment {
    var alignments []TimeAlignment
    
    for i > 0 || j > 0 {
        alignments = append([]TimeAlignment{
            {
                VideoTimestamp: float64(i),
                AudioTimestamp: float64(j),
                Score:          dp[i][j],
            },
        }, alignments...)
        
        if i == 0 {
            j--
        } else if j == 0 {
            i--
        } else {
            // 选择代价最小的方向
            minCost := min(dp[i-1][j], dp[i][j-1], dp[i-1][j-1])
            switch minCost {
            case dp[i-1][j]:
                i--
            case dp[i][j-1]:
                j--
            default:
                i--
                j--
            }
        }
    }
    
    // 添加起点
    alignments = append([]TimeAlignment{
        {
            VideoTimestamp: 0,
            AudioTimestamp: 0,
            Score:          dp[0][0],
        },
    }, alignments...)
    
    return alignments
}

// 辅助函数
func euclideanDistance(a, b FeatureVector) float64 {
    var sum float64
    for i := 0; i < len(a); i++ {
        diff := a[i] - b[i]
        sum += diff * diff
    }
    return math.Sqrt(sum)
}

func max(a, b int) int {
    if a > b {
        return a
    }
    return b
}

func min(values ...float64) float64 {
    minVal := values[0]
    for _, v := range values[1:] {
        if v < minVal {
            minVal = v
        }
    }
    return minVal
}

func QuantizeEmbedding(emb []float64) []int8 {
    quantized := make([]int8, len(emb))
    for i, v := range emb {
        // 将[-1,1]范围映射到[-128,127]
        quantized[i] = int8(v * 127.0)
    }
    return quantized
}

type HNSWIndex struct {
    levels       []int
    entryPoint   int
    maxLevel     int
    efConstruction int
    efSearch     int
}

func (e *AlignmentEngine) ProcessBatch(ctx context.Context, batch []Query) []Result {
    // 1. 批量编码
    embeddings := e.batchEncode(ctx, batch)
    
    // 2. 批量投影
    projected := e.batchProject(embeddings)
    
    // 3. 批量检索
    results := e.batchSearch(ctx, projected)
    
    return results
}

type MultiLevelCache struct {
    L1Cache map[string]*CacheEntry // 内存缓存
    L2Cache *RedisCache            // Redis缓存
    L3Cache *DiskCache             // 磁盘缓存
}

func (c *MultiLevelCache) Get(key string) (*CacheEntry, error) {
    // 优先从L1获取
    if entry, ok := c.L1Cache[key]; ok {
        return entry, nil
    }
    
    // L1未命中，从L2获取
    if entry, err := c.L2Cache.Get(key); err == nil {
        c.L1Cache[key] = entry // 回填L1
        return entry, nil
    }
    
    // L2未命中，从L3获取
    if entry, err := c.L3Cache.Get(key); err == nil {
        c.L2Cache.Set(key, entry) // 回填L2
        c.L1Cache[key] = entry    // 回填L1
        return entry, nil
    }
    
    return nil, ErrCacheMiss
}

func (e *TemporalAlignmentEngine) AlignAsync(ctx context.Context, job *AlignmentJob) <-chan *ProgressUpdate {
    updates := make(chan *ProgressUpdate, 100)
    
    go func() {
        defer close(updates)
        
        // 分阶段处理并发送进度更新
        updates <- &ProgressUpdate{Stage: "extracting", Progress: 0.0}
        
        // 提取特征
        features := e.extractFeatures(job)
        updates <- &ProgressUpdate{Stage: "extracting", Progress: 0.3}
        
        // 执行对齐
        alignments := e.performAlignment(features)
        updates <- &ProgressUpdate{Stage: "aligning", Progress: 0.7}
        
        // 后处理
        result := e.postProcess(alignments)
        updates <- &ProgressUpdate{Stage: "complete", Progress: 1.0, Result: result}
    }()
    
    return updates
}

type MetricsCollector struct {
    // 性能指标
    queryLatency    *prometheus.Histogram
    alignmentScore  *prometheus.Gauge
    cacheHitRate    *prometheus.Counter
    
    // 资源指标
    gpuUtilization  *prometheus.Gauge
    memoryUsage     *prometheus.Gauge
}

func (c *MetricsCollector) RecordQuery(queryType string, latency time.Duration) {
    c.queryLatency.WithLabelValues(queryType).Observe(latency.Seconds())
    
    // 检测性能退化
    if latency > 500*time.Millisecond {
        alertManager.SendAlert("High latency detected", map[string]string{
            "query_type": queryType,
            "latency":    latency.String(),
        })
    }
}