第9章：多模态生成式检索

文本 --> 文本编码器 --> 文本嵌入 --\
                                    |--> 相似度计算 --> 排序
图像 --> 图像编码器 --> 图像嵌入 --/

                    ┌─────────────────┐
                    │  Cross-Modal    │
                    │   Transformer   │
                    └────────▲────────┘
                             │
                    ┌────────┴────────┐
                    │   Fusion Layer  │
                    └────────▲────────┘
                             │
              ┌──────────────┼──────────────┐
              │                              │
     ┌────────▼────────┐           ┌────────▼────────┐
     │ Vision Encoder  │           │  Text Encoder   │
     └────────▲────────┘           └────────▲────────┘
              │                              │
         [Image Input]                  [Text Input]

# 伪代码示例
hidden_visual = vision_encoder(image)
hidden_text = text_encoder(text)
hidden_fused = fusion_layer(hidden_visual, hidden_text)
doc_ids = decoder(hidden_fused)  # 生成文档标识符

图像文档 --> [IMG_2341_7856_9012]
文本文档 --> [TXT_2341_7856_9012]  
视频文档 --> [VID_2341_7856_9012]

动物/哺乳类/猫科/家猫 --> [1, 12, 125, 1257]
动物/哺乳类/猫科/狮子 --> [1, 12, 125, 1258]

# 伪代码
class VQLayer:
    def __init__(self, num_embeddings, embedding_dim):
        self.embedding = nn.Embedding(num_embeddings, embedding_dim)

    def forward(self, z_e):
        # 计算到所有码本向量的距离
        distances = torch.cdist(z_e, self.embedding.weight)
        # 选择最近的码本索引
        indices = distances.argmin(dim=-1)
        # 获取量化后的向量
        z_q = self.embedding(indices)
        return z_q, indices

Level 1: [场景类型] --> 室内(0) / 室外(1)
Level 2: [主体类别] --> 人物(00) / 物体(01) / 风景(10)
Level 3: [细粒度类别] --> 具体的256个子类别
Level 4: [实例标识] --> 具体的实例ID

                    根节点
                   /      \
              模态分支    模态分支
              /    \        /    \
         类别节点  类别节点  类别节点  类别节点
           / \      / \      / \      / \
        实例 实例  实例 实例  实例 实例  实例 实例

# 映射表示例
cross_modal_map = {
    'IMG_1234': ['TXT_5678', 'TXT_9012'],  # 图像对应的文本
    'TXT_5678': ['IMG_1234', 'IMG_3456'],  # 文本对应的图像
}

def generate_compatible_id(new_doc, existing_ids):
    # 提取特征
    features = extract_features(new_doc)
    # 找到最相似的现有文档
    similar_id = find_most_similar(features, existing_ids)
    # 生成新的标识符
    new_id = modify_id(similar_id, features)
    return new_id

┌─────────────┐         ┌─────────────┐
│Image Encoder│         │Text Encoder │
└──────┬──────┘         └──────┬──────┘
       │                        │
   [I₁,I₂,...,Iₙ]          [T₁,T₂,...,Tₙ]
       │                        │
       └────────┬───────────────┘
                │
        Cosine Similarity
                │
        ┌───────▼────────┐
        │  N×N Matrix    │
        │  ┌─┬─┬─┬─┐    │
        │  ├─┼─┼─┼─┤    │
        │  ├─┼─┼─┼─┤    │
        │  └─┴─┴─┴─┘    │
        └────────────────┘

def clip_to_generative(clip_embedding):
    # 方法1：直接量化
    quantized_ids = vector_quantize(clip_embedding)

    # 方法2：通过解码器生成
    decoder_hidden = mlp(clip_embedding)
    doc_ids = autoregressive_decode(decoder_hidden)

    # 方法3：检索最近邻作为种子
    nearest_docs = retrieve_knn(clip_embedding, doc_embeddings)
    doc_ids = rerank_and_select(nearest_docs)

    return doc_ids

查询 --> CLIP编码 --> Top-K候选 --> 生成式重排 --> 最终结果

class CLIPGenerativeRetriever:
    def __init__(self):
        self.clip_model = load_clip()
        self.id_decoder = TransformerDecoder()

    def encode_query(self, query):
        if is_image(query):
            features = self.clip_model.encode_image(query)
        else:
            features = self.clip_model.encode_text(query)
        return features

    def generate_doc_ids(self, query_features):
        # 使用CLIP特征初始化解码器
        decoder_input = self.projection(query_features)
        doc_ids = self.id_decoder.generate(decoder_input)
        return doc_ids

          ┌─────────────────────┐
          │      Query          │
          └──────┬──┬───────────┘
                 │  │
        ┌────────┘  └────────┐
        ▼                    ▼
   CLIP Path            Generative Path
        │                    │
   Dense Retrieval      ID Generation
        │                    │
        └────────┬───────────┘
                 │
           Fusion & Rerank
                 │
                 ▼
            Final Results

def cascaded_retrieval(query, top_k=100, final_k=10):
    # 第一阶段：CLIP检索
    clip_features = encode_with_clip(query)
    candidates = clip_retrieve(clip_features, top_k)

    # 第二阶段：生成式精排
    refined_ids = []
    for candidate in candidates:
        score = generative_model.score(query, candidate)
        refined_ids.append((candidate, score))

    # 返回最终结果
    refined_ids.sort(key=lambda x: x[1], reverse=True)
    return refined_ids[:final_k]

def initialize_from_clip(model, clip_checkpoint):
    # 加载CLIP权重
    clip_state = torch.load(clip_checkpoint)

    # 初始化视觉编码器
    model.vision_encoder.load_state_dict(
        clip_state['visual'], strict=False)

    # 初始化文本编码器
    model.text_encoder.load_state_dict(
        clip_state['text'], strict=False)

    # 冻结部分层
    for param in model.vision_encoder.parameters():
        param.requires_grad = False

curriculum = [
    {'stage': 1, 'task': 'exact_match', 'epochs': 10},
    {'stage': 2, 'task': 'semantic_similar', 'epochs': 20},
    {'stage': 3, 'task': 'cross_modal', 'epochs': 30},
    {'stage': 4, 'task': 'compositional', 'epochs': 40}
]

def mine_hard_negatives(query, positives, all_docs):
    # 使用CLIP编码
    query_emb = clip_encode(query)
    doc_embs = [clip_encode(d) for d in all_docs]

    # 计算相似度
    similarities = cosine_similarity(query_emb, doc_embs)

    # 选择难负样本（相似但不匹配）
    hard_negatives = []
    for idx, sim in enumerate(similarities):
        if all_docs[idx] not in positives and sim > threshold:
            hard_negatives.append(all_docs[idx])

    return hard_negatives

class MultiTaskLoss:
    def __init__(self):
        self.tasks = {
            'generation': GenerationLoss(),
            'contrastive': ContrastiveLoss(),
            'matching': MatchingLoss(),
            'ranking': RankingLoss()
        }

    def compute(self, outputs, targets):
        total_loss = 0
        for task_name, task_loss in self.tasks.items():
            loss = task_loss(outputs[task_name], targets[task_name])
            total_loss += self.weights[task_name] * loss
        return total_loss

Layer 1: 高容量模态压缩
         V_compressed = Compress(V, ratio=0.1)

Layer 2: 容量匹配
         V_matched = Match(V_compressed, C_text)

Layer 3: 语义对齐
         V_aligned, T_aligned = Align(V_matched, T)

Layer 4: 深度融合
         F = DeepFusion(V_aligned, T_aligned)

# Sinkhorn迭代
for iteration in range(max_iters):
    # 更新行归一化
    u = a / (K @ v)
    # 更新列归一化
    v = b / (K.T @ u)

# 最优传输方案
gamma = diag(u) @ K @ diag(v)

场景 --> 物体 --> 属性
  │        │        │
  └────────┼────────┘
           ▼
         文本描述

class TemporalCausalAttention:
    def forward(self, video_frames, text_tokens):
        # 因果掩码确保只能看到过去的信息
        causal_mask = torch.tril(torch.ones(T, T))

        # 计算时序注意力
        attn = self.attention(
            Q=text_tokens,
            K=video_frames, 
            V=video_frames,
            mask=causal_mask
        )
        return attn

用户上传图片 --> 人工/自动标注 --> 倒排索引 --> 关键词匹配

图片 --> ResNet-152 --> 2048维特征 --> LSH索引 --> KNN检索

┌─────────────────────────┐
│   Visual Tower (ViT)    │
└───────────┬─────────────┘
            │
      Shared Space
            │
┌───────────┴─────────────┐
│    Text Tower (BERT)    │
└─────────────────────────┘

查询 --> 多模态编码器 --> ID生成器 --> Pin标识符序列

# 层次化标识符生成流程
def generate_hierarchical_id(pin_features):
    # Level 1: 主题类别（16个大类）
    category = classify_category(pin_features)  # 4 bits

    # Level 2: 子类别（256个子类）
    subcategory = classify_subcategory(pin_features, category)  # 8 bits

    # Level 3: 视觉聚类（4096个聚类中心）
    cluster = find_nearest_cluster(pin_features, subcategory)  # 12 bits

    # Level 4: 实例哈希（保证唯一性）
    instance_hash = hash_instance(pin_features)  # 16 bits

    return [category, subcategory, cluster, instance_hash]  # 总计40 bits

# INT8量化减少计算开销
quantized_model = quantize_model(
    original_model,
    calibration_data=sample_queries,
    backend='FBGEMM'
)

# 多级缓存架构
class MultiLevelCache:
    def __init__(self):
        self.l1_cache = LRUCache(size=10000)  # 热门查询
        self.l2_cache = RedisCache()  # 分布式缓存
        self.l3_cache = CDNCache()  # 边缘缓存

# 动态批处理提高GPU利用率
batch_size = adaptive_batching(
    current_qps=qps,
    target_latency=100,
    gpu_util=gpu_utilization
)

# 多语言编码器
class MultilingualEncoder:
    def __init__(self):
        self.base_model = XLMRoberta()
        self.lang_adapters = {
            'en': EnglishAdapter(),
            'es': SpanishAdapter(),
            'zh': ChineseAdapter(),
            # ... 更多语言
        }

    def encode(self, text, lang):
        base_encoding = self.base_model(text)
        if lang in self.lang_adapters:
            return self.lang_adapters[lang](base_encoding)
        return base_encoding  # 零样本处理未见语言

class IncrementalIndexer:
    def update_index(self, new_pins):
        # 1. 批量特征提取
        features = self.extract_features_batch(new_pins)

        # 2. 分配兼容ID
        new_ids = []
        for feat in features:
            # 找到最近的现有聚类
            nearest_cluster = self.find_nearest_cluster(feat)
            # 在聚类内分配新ID
            new_id = self.allocate_id_in_cluster(nearest_cluster, feat)
            new_ids.append(new_id)

        # 3. 异步更新索引
        self.async_update_shards(new_ids, features)

        # 4. 触发模型增量训练
        if len(new_pins) > threshold:
            self.trigger_incremental_training(new_pins)

class OptimizedDecoder:
    def __init__(self):
        # 预计算的前缀树加速
        self.prefix_tree = build_prefix_tree(all_valid_ids)
        # 缓存的beam states
        self.beam_cache = {}

    def decode(self, query_encoding):
        # 使用前缀树约束解码空间
        constrained_vocab = self.prefix_tree.get_valid_continuations()

        # 并行beam search
        beams = parallel_beam_search(
            query_encoding,
            vocab=constrained_vocab,
            beam_size=5,
            max_length=4  # 层次ID长度
        )

        return beams[0]  # 返回最佳路径

# 使用混合精度加速训练
from apex import amp

model, optimizer = amp.initialize(
    model, optimizer,
    opt_level="O2",  # 大部分操作使用FP16
    keep_batchnorm_fp32=True
)

# 训练循环
for batch in dataloader:
    with amp.scale_loss(loss, optimizer) as scaled_loss:
        scaled_loss.backward()

         Load Balancer
              │
    ┌─────────┼─────────┐
    │         │         │
 GPU Pod 1  GPU Pod 2  GPU Pod 3
    │         │         │
  Cache     Cache     Cache

class FeatureReuser:
    def __init__(self):
        self.visual_features = {}  # Pin ID -> 视觉特征
        self.text_features = {}    # 文本 -> 文本特征

    def get_features(self, query):
        # 检查缓存
        if query in self.cache:
            return self.cache[query]

        # 复用部分计算
        if is_similar_query(query, self.recent_queries):
            base_features = self.get_similar_features(query)
            delta_features = self.compute_delta(query, base_features)
            features = base_features + delta_features
        else:
            features = self.compute_from_scratch(query)

        self.cache[query] = features
        return features

# 融合用户历史的生成式检索
def personalized_generation(query, user_history):
    # 用户兴趣编码
    user_encoding = encode_user_history(user_history)

    # 查询编码
    query_encoding = encode_query(query)

    # 个性化融合
    fused = attention_fusion(query_encoding, user_encoding)

    # 生成个性化结果
    return generate_ids(fused)

阶段1：A/B测试5%流量
阶段2：扩展到25%流量，收集反馈
阶段3：优化后扩展到50%
阶段4：全量上线，保留降级方案

def hybrid_search(query):
    # 生成式检索
    gen_results = generative_search(query)

    # 传统检索作为补充
    if confidence(gen_results) < threshold:
        trad_results = traditional_search(query)
        results = merge_results(gen_results, trad_results)
    else:
        results = gen_results

    return results