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免费做企业推广的网站,网站空间的申请,wordpress的修改后主题后台出现已损坏的修复,wordpress 共享MUVERA算法创新性地将多向量检索问题转化为单向量最大内积搜索#xff0c;通过固定维度编码(FDE)技术大幅降低内存占用(节省近80%)和提升检索效率(HNSW图节点缩减至1%)。该算法通过空间划分、降维、重复增强和最终投影四步实现#xff0c;在保持较高召回率的同时#xff0c;…MUVERA算法创新性地将多向量检索问题转化为单向量最大内积搜索通过固定维度编码(FDE)技术大幅降低内存占用(节省近80%)和提升检索效率(HNSW图节点缩减至1%)。该算法通过空间划分、降维、重复增强和最终投影四步实现在保持较高召回率的同时显著改善了数据导入速度特别适合内存敏感的大规模部署场景。在向量数据库和信息检索领域多向量嵌入模型如 ColBERT、ColPali凭借其强大的语义捕获能力正在成为主流选择。这类模型能够保留文本的词元级别含义或是识别图像不同部分的信息特征。然而它们也带来了显著的性能挑战庞大的内存占用和较慢的检索速度。Weaviate 在 1.31 版本中引入的 MUVERA 编码算法正是为了解决这些问题而生。多向量模型的优势与困境多向量嵌入的核心优势在于其细粒度的语义表达能力。相比单向量模型将整个文档压缩成一个固定长度的向量多向量模型为文档的每个词元或图像块生成独立的向量表示。这种设计使得模型能够捕捉更丰富的语义信息在检索任务中展现出更高的准确性。单向量与多向量对比但这种精细化表示的代价同样明显。假设要索引一百万个文档每个文档平均包含 100 个词元。使用传统的单向量模型768 维32 位浮点数大约需要 3.1GB 内存。而多向量模型96 维的内存消耗可能高达 40GB超过十倍的差距。这种内存压力在大规模部署场景下会转化为实实在在的成本负担。多向量嵌入内存对比性能瓶颈不仅体现在存储层面。在检索阶段多向量模型需要使用 MaxSim 运算符计算相似度。这个过程需要遍历查询的每个词元找出它与文档所有词元中的最佳匹配然后累加所有匹配得分。数学表达式如下这种非线性计算相比简单的点积运算复杂得多直接影响了查询响应速度和数据导入效率。单向量和多向量内存使用情况MUVERA 的核心思想MUVERAMulti-Vector Retrieval via Fixed Dimensional Encodings的设计哲学是将复杂的多向量检索问题转化为单向量最大内积搜索。算法的关键在于构建固定维度编码FDE将一组长度不定的向量集合压缩成单个固定长度的向量表示。整个转换过程可以用一个简洁的映射函数表示这里的核心目标是让编码后的单向量点积能够很好地近似原始多向量的 MaxSim 相似度MUVERA 高层概览这种转换带来的效率提升是显著的。对于包含 100 万个文档、每个文档 100 个向量的数据集传统方案需要索引 1 亿个向量而 MUVERA 只需处理 100 万个 FDE 向量将 HNSW 图的规模缩减到原来的 1%。算法实现细节MUVERA 通过四个精心设计的步骤完成编码转换空间划分、降维、重复增强和最终投影。每个步骤都有明确的数学基础和实际考量。空间划分策略第一步是将高维向量空间划分成若干个桶。算法采用 SimHash 技术实现这一过程这是一种基于局部敏感哈希的方法。具体来说算法会采样 个高斯向量然后通过计算输入向量与这些高斯向量的点积符号来确定桶编号这种划分方式的优势在于其与数据分布无关不需要预先训练也不会因为数据漂移而失效。划分完成后属于同一个桶的向量会被聚合成一个代表性向量。MUVERA 步骤 1 - 空间划分MUVERA 步骤 2 - 填充空簇对于文档编码每个桶的子向量计算方式为而查询编码则直接对属于同一桶的向量求和这种不对称处理恰好对应了 MaxSim 运算的特性。降维与重复空间划分后得到的向量维度是 其中 是桶的数量 是原始向量维度。为了进一步压缩表示MUVERA 使用随机投影矩阵进行降维这里的随机矩阵 元素取值为 遵循 Johnson-Lindenstrauss 引理能够在降维的同时保持向量间点积的相对关系。MUVERA 步骤 3 - 降维为了提高编码的鲁棒性算法会重复执行空间划分和降维步骤 次将得到的多个编码向量拼接起来。最终的 FDE 维度为 。性能评测与实际效果Weaviate 团队使用 LoTTE 基准测试数据集进行了详细的性能评估。该数据集包含约 11.9 万个文档使用 ColBERT v2.0 编码后生成了 1500 万个 128 维向量总内存占用约 8GB。启用 MUVERA 后参数设置为 每个文档被编码为 2560 维的单一向量。这使得总浮点数存储量从 19 亿降至 3.04 亿内存节省接近 80%。更重要的是HNSW 图的节点数从 1500 万降至 11.9 万这对于图遍历效率的提升是质的飞跃。未使用 MUVERA SQ 与 MUVERA SQ 时的堆内存分配数据导入速度的改善同样显著。基准场景下导入 11 万个对象需要 20 多分钟相当于每秒只能处理约 100 个对象。而使用 MUVERA 后这个时间缩短到 3-6 分钟。对于需要频繁更新索引的生产环境这种效率提升意义重大。性能权衡考量技术方案从来不是完美的MUVERA 也有其代价。最主要的妥协体现在召回率上。测试数据显示在相同的搜索参数下启用 MUVERA 会导致召回率下降。不过这个问题可以通过调整 HNSW 的ef参数来缓解。当ef值设置在 512 以上时召回率可以恢复到 80% 以上而在 2048 时甚至能超过 90%。但提高ef值意味着要检索更多的候选集这会降低查询吞吐量。因此实际应用中需要在召回质量和查询速度之间找到平衡点。MUVERA 对比Google Research 团队的实验结果进一步验证了 MUVERA 的效果。在 BEIR 基准测试中相比基于单向量启发式的 PLAID 系统MUVERA 在召回率平均提升 10% 的同时将延迟降低了 90%。这种性能提升在大规模部署中的价值不言而喻。适用场景分析MUVERA 并非万能方案它最适合以下几类应用场景。首先是内存成本敏感的大规模部署。当数据集规模达到千万甚至亿级时内存占用的降低可以直接转化为每年数万甚至数十万美元的成本节约。其次是对索引速度有较高要求的场景比如需要频繁更新的实时系统。另一个重要考量是对召回质量的容忍度。如果应用场景对检索精度有极致要求那么需要仔细权衡 MUVERA 带来的召回率下降是否可以接受。不过对于许多实际应用来说轻微的召回损失往往是可以承受的特别是考虑到可以通过调整搜索参数来部分恢复性能。从实现角度看Weaviate 的集成使得启用 MUVERA 变得非常简单只需要几行配置代码。用户可以设置的主要参数包括k_sim空间划分的细粒度、d_proj降维后的维度和r_reps重复次数。Weaviate 团队为这些参数提供了合理的默认值大多数场景下可以直接使用。值得注意的是MUVERA 的固定维度编码还可以结合标量量化Scalar Quantization等技术进一步压缩。Google 的研究表明通过乘积量化可以在几乎不影响检索质量的前提下将内存占用再减少 32 倍。这为超大规模应用提供了更多优化空间。实现https://github.com/sionic-ai/muvera-py/tree/master我在github上面找到一个MUVERA的python实现大家可以尝试一下import loggingimport timeimport numpy as npfrom dataclasses import dataclass, replacefrom enum import Enumfrom typing import Optional, Listclass EncodingType(Enum): DEFAULT_SUM 0 AVERAGE 1class ProjectionType(Enum): DEFAULT_IDENTITY 0 AMS_SKETCH 1dataclassclass FixedDimensionalEncodingConfig: dimension: int 128 num_repetitions: int 10 num_simhash_projections: int 6 seed: int 42 encoding_type: EncodingType EncodingType.DEFAULT_SUM projection_type: ProjectionType ProjectionType.DEFAULT_IDENTITY projection_dimension: Optional[int] None fill_empty_partitions: bool False final_projection_dimension: Optional[int] Nonedef _append_to_gray_code(gray_code: int, bit: bool) - int: return (gray_code 1) (int(bit) ^ (gray_code 1))def _gray_code_to_binary(num: int) - int: mask num 1 while mask ! 0: num num ^ mask mask 1 return numdef _simhash_matrix_from_seed( dimension: int, num_projections: int, seed: int) - np.ndarray: rng np.random.default_rng(seed) return rng.normal(loc0.0, scale1.0, size(dimension, num_projections)).astype( np.float32 )def _ams_projection_matrix_from_seed( dimension: int, projection_dim: int, seed: int) - np.ndarray: rng np.random.default_rng(seed) out np.zeros((dimension, projection_dim), dtypenp.float32) indices rng.integers(0, projection_dim, sizedimension) signs rng.choice([-1.0, 1.0], sizedimension) out[np.arange(dimension), indices] signs return outdef _apply_count_sketch_to_vector( input_vector: np.ndarray, final_dimension: int, seed: int) - np.ndarray: rng np.random.default_rng(seed) out np.zeros(final_dimension, dtypenp.float32) indices rng.integers(0, final_dimension, sizeinput_vector.shape[0]) signs rng.choice([-1.0, 1.0], sizeinput_vector.shape[0]) np.add.at(out, indices, signs * input_vector) return outdef _simhash_partition_index_gray(sketch_vector: np.ndarray) - int: partition_index 0 for val in sketch_vector: partition_index _append_to_gray_code(partition_index, val 0) return partition_indexdef _distance_to_simhash_partition( sketch_vector: np.ndarray, partition_index: int) - int: num_projections sketch_vector.size binary_representation _gray_code_to_binary(partition_index) sketch_bits (sketch_vector 0).astype(int) binary_array (binary_representation np.arange(num_projections - 1, -1, -1)) 1 return int(np.sum(sketch_bits ! binary_array))def _generate_fde_internal( point_cloud: np.ndarray, config: FixedDimensionalEncodingConfig) - np.ndarray: if point_cloud.ndim ! 2 or point_cloud.shape[1] ! config.dimension: raise ValueError( fInput data shape {point_cloud.shape} is inconsistent with config dimension {config.dimension}. ) if not (0 config.num_simhash_projections 32): raise ValueError( fnum_simhash_projections must be in [0, 31]: {config.num_simhash_projections} ) num_points, original_dim point_cloud.shape num_partitions 2**config.num_simhash_projections use_identity_proj config.projection_type ProjectionType.DEFAULT_IDENTITY projection_dim original_dim if use_identity_proj else config.projection_dimension if not use_identity_proj and (not projection_dim or projection_dim 0): raise ValueError( A positive projection_dimension is required for non-identity projections. ) final_fde_dim config.num_repetitions * num_partitions * projection_dim out_fde np.zeros(final_fde_dim, dtypenp.float32) for rep_num in range(config.num_repetitions): current_seed config.seed rep_num sketches point_cloud _simhash_matrix_from_seed( original_dim, config.num_simhash_projections, current_seed ) if use_identity_proj: projected_matrix point_cloud elif config.projection_type ProjectionType.AMS_SKETCH: ams_matrix _ams_projection_matrix_from_seed( original_dim, projection_dim, current_seed ) projected_matrix point_cloud ams_matrix rep_fde_sum np.zeros(num_partitions * projection_dim, dtypenp.float32) partition_counts np.zeros(num_partitions, dtypenp.int32) partition_indices np.array( [_simhash_partition_index_gray(sketches[i]) for i in range(num_points)] ) for i in range(num_points): start_idx partition_indices[i] * projection_dim rep_fde_sum[start_idx : start_idx projection_dim] projected_matrix[i] partition_counts[partition_indices[i]] 1 if config.encoding_type EncodingType.AVERAGE: for i in range(num_partitions): start_idx i * projection_dim if partition_counts[i] 0: rep_fde_sum[start_idx : start_idx projection_dim] / ( partition_counts[i] ) elif config.fill_empty_partitions and num_points 0: distances [ _distance_to_simhash_partition(sketches[j], i) for j in range(num_points) ] nearest_point_idx np.argmin(distances) rep_fde_sum[start_idx : start_idx projection_dim] ( projected_matrix[nearest_point_idx] ) rep_start_index rep_num * num_partitions * projection_dim out_fde[rep_start_index : rep_start_index rep_fde_sum.size] rep_fde_sum if config.final_projection_dimension and config.final_projection_dimension 0: return _apply_count_sketch_to_vector( out_fde, config.final_projection_dimension, config.seed ) return out_fdedef generate_query_fde( point_cloud: np.ndarray, config: FixedDimensionalEncodingConfig) - np.ndarray: Generates a Fixed Dimensional Encoding for a query point cloud (using SUM). if config.fill_empty_partitions: raise ValueError( Query FDE generation does not support fill_empty_partitions. ) query_config replace(config, encoding_typeEncodingType.DEFAULT_SUM) return _generate_fde_internal(point_cloud, query_config)def generate_document_fde( point_cloud: np.ndarray, config: FixedDimensionalEncodingConfig) - np.ndarray: Generates a Fixed Dimensional Encoding for a document point cloud (using AVERAGE). doc_config replace(config, encoding_typeEncodingType.AVERAGE) return _generate_fde_internal(point_cloud, doc_config)def generate_fde( point_cloud: np.ndarray, config: FixedDimensionalEncodingConfig) - np.ndarray: if config.encoding_type EncodingType.DEFAULT_SUM: return generate_query_fde(point_cloud, config) elif config.encoding_type EncodingType.AVERAGE: return generate_document_fde(point_cloud, config) else: raise ValueError(fUnsupported encoding type in config: {config.encoding_type})def generate_document_fde_batch( doc_embeddings_list: List[np.ndarray], config: FixedDimensionalEncodingConfig) - np.ndarray: Generates FDEs for a batch of documents using highly optimized NumPy vectorization. Fully compliant with C implementation including all projection types. batch_start_time time.perf_counter() num_docs len(doc_embeddings_list) if num_docs 0: logging.warning([FDE Batch] Empty document list provided) return np.array([]) logging.info(f[FDE Batch] Starting batch FDE generation for {num_docs} documents) # Input validation valid_docs [] for i, doc in enumerate(doc_embeddings_list): if doc.ndim ! 2: logging.warning( f[FDE Batch] Document {i} has invalid shape (ndim{doc.ndim}), skipping ) continue if doc.shape[1] ! config.dimension: raise ValueError( fDocument {i} has incorrect dimension: expected {config.dimension}, got {doc.shape[1]} ) if doc.shape[0] 0: logging.warning(f[FDE Batch] Document {i} has no vectors, skipping) continue valid_docs.append(doc) if len(valid_docs) 0: logging.warning([FDE Batch] No valid documents after filtering) return np.array([]) num_docs len(valid_docs) doc_embeddings_list valid_docs # Determine projection dimension (matching C logic) use_identity_proj config.projection_type ProjectionType.DEFAULT_IDENTITY if use_identity_proj: projection_dim config.dimension logging.info(f[FDE Batch] Using identity projection (dim{projection_dim})) else: if not config.projection_dimension or config.projection_dimension 0: raise ValueError( A positive projection_dimension must be specified for non-identity projections ) projection_dim config.projection_dimension logging.info( f[FDE Batch] Using {config.projection_type.name} projection: f{config.dimension} - {projection_dim} ) # Configuration summary num_partitions 2**config.num_simhash_projections logging.info( f[FDE Batch] Configuration: {config.num_repetitions} repetitions, f{num_partitions} partitions, projection_dim{projection_dim} ) # Document tracking doc_lengths np.array([len(doc) for doc in doc_embeddings_list], dtypenp.int32) total_vectors np.sum(doc_lengths) doc_boundaries np.insert(np.cumsum(doc_lengths), 0, 0) doc_indices np.repeat(np.arange(num_docs), doc_lengths) logging.info( f[FDE Batch] Total vectors: {total_vectors}, avg per doc: {total_vectors / num_docs:.1f} ) # Concatenate all embeddings concat_start time.perf_counter() all_points np.vstack(doc_embeddings_list).astype(np.float32) concat_time time.perf_counter() - concat_start logging.info(f[FDE Batch] Concatenation completed in {concat_time:.3f}s) # Pre-allocate output final_fde_dim config.num_repetitions * num_partitions * projection_dim out_fdes np.zeros((num_docs, final_fde_dim), dtypenp.float32) logging.info(f[FDE Batch] Output FDE dimension: {final_fde_dim}) # Process each repetition for rep_num in range(config.num_repetitions): # rep_start_time time.perf_counter() current_seed config.seed rep_num if rep_num % 5 0: # Log every 5 repetitions logging.info( f[FDE Batch] Processing repetition {rep_num 1}/{config.num_repetitions} ) # Step 1: SimHash projection simhash_start time.perf_counter() simhash_matrix _simhash_matrix_from_seed( config.dimension, config.num_simhash_projections, current_seed ) all_sketches all_points simhash_matrix simhash_time time.perf_counter() - simhash_start # Step 2: Apply dimensionality reduction if configured proj_start time.perf_counter() if use_identity_proj: projected_points all_points elif config.projection_type ProjectionType.AMS_SKETCH: ams_matrix _ams_projection_matrix_from_seed( config.dimension, projection_dim, current_seed ) projected_points all_points ams_matrix else: raise ValueError(fUnsupported projection type: {config.projection_type}) proj_time time.perf_counter() - proj_start # Step 3: Vectorized partition index calculation partition_start time.perf_counter() bits (all_sketches 0).astype(np.uint32) partition_indices np.zeros(total_vectors, dtypenp.uint32) # Vectorized Gray Code computation for bit_idx in range(config.num_simhash_projections): partition_indices (partition_indices 1) ( bits[:, bit_idx] ^ (partition_indices 1) ) partition_time time.perf_counter() - partition_start # Step 4: Vectorized aggregation agg_start time.perf_counter() # Initialize storage for this repetition rep_fde_sum np.zeros( (num_docs * num_partitions * projection_dim,), dtypenp.float32 ) partition_counts np.zeros((num_docs, num_partitions), dtypenp.int32) # Count vectors per partition per document np.add.at(partition_counts, (doc_indices, partition_indices), 1) # Aggregate vectors using flattened indexing for efficiency doc_part_indices doc_indices * num_partitions partition_indices base_indices doc_part_indices * projection_dim for d in range(projection_dim): flat_indices base_indices d np.add.at(rep_fde_sum, flat_indices, projected_points[:, d]) # Reshape for easier manipulation rep_fde_sum rep_fde_sum.reshape(num_docs, num_partitions, projection_dim) agg_time time.perf_counter() - agg_start # Step 5: Convert sums to averages (for document FDE) avg_start time.perf_counter() # Vectorized division where counts 0 non_zero_mask partition_counts 0 counts_3d partition_counts[:, :, np.newaxis] # Broadcasting for division # Safe division (avoid divide by zero) np.divide(rep_fde_sum, counts_3d, outrep_fde_sum, wherecounts_3d 0) # Fill empty partitions if configured empty_filled 0 if config.fill_empty_partitions: empty_mask ~non_zero_mask empty_docs, empty_parts np.where(empty_mask) for doc_idx, part_idx in zip(empty_docs, empty_parts): if doc_lengths[doc_idx] 0: continue # Get sketches for this document doc_start doc_boundaries[doc_idx] doc_end doc_boundaries[doc_idx 1] doc_sketches all_sketches[doc_start:doc_end] # Vectorized distance calculation binary_rep _gray_code_to_binary(part_idx) target_bits ( binary_rep np.arange(config.num_simhash_projections - 1, -1, -1) ) 1 distances np.sum( (doc_sketches 0).astype(int) ! target_bits, axis1 ) nearest_local_idx np.argmin(distances) nearest_global_idx doc_start nearest_local_idx rep_fde_sum[doc_idx, part_idx, :] projected_points[nearest_global_idx] empty_filled 1 avg_time time.perf_counter() - avg_start # Step 6: Copy results to output array rep_output_start rep_num * num_partitions * projection_dim out_fdes[ :, rep_output_start : rep_output_start num_partitions * projection_dim ] rep_fde_sum.reshape(num_docs, -1) # Log timing for first repetition if rep_num 0: logging.info([FDE Batch] Repetition timing breakdown:) logging.info(f - SimHash: {simhash_time:.3f}s) logging.info(f - Projection: {proj_time:.3f}s) logging.info(f - Partition indices: {partition_time:.3f}s) logging.info(f - Aggregation: {agg_time:.3f}s) logging.info(f - Averaging: {avg_time:.3f}s) if config.fill_empty_partitions: logging.info(f - Filled {empty_filled} empty partitions) # Step 7: Apply final projection if configured if config.final_projection_dimension and config.final_projection_dimension 0: logging.info( f[FDE Batch] Applying final projection: {final_fde_dim} - f{config.final_projection_dimension} ) final_proj_start time.perf_counter() # Process in chunks to avoid memory issues chunk_size min(100, num_docs) final_fdes [] for i in range(0, num_docs, chunk_size): chunk_end min(i chunk_size, num_docs) chunk_fdes np.array( [ _apply_count_sketch_to_vector( out_fdes[j], config.final_projection_dimension, config.seed ) for j in range(i, chunk_end) ] ) final_fdes.append(chunk_fdes) out_fdes np.vstack(final_fdes) final_proj_time time.perf_counter() - final_proj_start logging.info( f[FDE Batch] Final projection completed in {final_proj_time:.3f}s ) # Final statistics and validation total_time time.perf_counter() - batch_start_time logging.info(f[FDE Batch] Batch generation completed in {total_time:.3f}s) logging.info( f[FDE Batch] Average time per document: {total_time / num_docs * 1000:.2f}ms ) logging.info(f[FDE Batch] Throughput: {num_docs / total_time:.1f} docs/sec) logging.info(f[FDE Batch] Output shape: {out_fdes.shape}) # Validate output dimensions expected_dim ( final_fde_dim if not config.final_projection_dimension else config.final_projection_dimension ) assert out_fdes.shape (num_docs, expected_dim), ( fOutput shape mismatch: {out_fdes.shape} ! ({num_docs}, {expected_dim}) ) # doc_config replace(config, encoding_typeEncodingType.AVERAGE) return out_fdesif __name__ __main__: print(f\n{ * 20} SCENARIO 1: Basic FDE Generation { * 20}) base_config FixedDimensionalEncodingConfig( dimension128, num_repetitions2, num_simhash_projections4, seed42 ) query_data np.random.randn(32, base_config.dimension).astype(np.float32) doc_data np.random.randn(80, base_config.dimension).astype(np.float32) query_fde generate_query_fde(query_data, base_config) doc_fde generate_document_fde( doc_data, replace(base_config, fill_empty_partitionsTrue) ) expected_dim ( base_config.num_repetitions * (2**base_config.num_simhash_projections) * base_config.dimension ) print(fQuery FDE Shape: {query_fde.shape} (Expected: {expected_dim})) print(fDocument FDE Shape: {doc_fde.shape} (Expected: {expected_dim})) print(fSimilarity Score: {np.dot(query_fde, doc_fde):.4f}) assert query_fde.shape[0] expected_dim print(f\n{ * 20} SCENARIO 2: Inner Projection (AMS Sketch) { * 20}) ams_config replace( base_config, projection_typeProjectionType.AMS_SKETCH, projection_dimension16 ) query_fde_ams generate_query_fde(query_data, ams_config) expected_dim_ams ( ams_config.num_repetitions * (2**ams_config.num_simhash_projections) * ams_config.projection_dimension ) print(fAMS Sketch FDE Shape: {query_fde_ams.shape} (Expected: {expected_dim_ams})) assert query_fde_ams.shape[0] expected_dim_ams print(f\n{ * 20} SCENARIO 3: Final Projection (Count Sketch) { * 20}) final_proj_config replace(base_config, final_projection_dimension1024) query_fde_final generate_query_fde(query_data, final_proj_config) print( fFinal Projection FDE Shape: {query_fde_final.shape} (Expected: {final_proj_config.final_projection_dimension}) ) assert query_fde_final.shape[0] final_proj_config.final_projection_dimension print(f\n{ * 20} SCENARIO 4: Top-level generate_fde wrapper { * 20}) query_fde_2 generate_fde( query_data, replace(base_config, encoding_typeEncodingType.DEFAULT_SUM) ) doc_fde_2 generate_fde( doc_data, replace(base_config, encoding_typeEncodingType.AVERAGE) ) print( fWrapper-generated Query FDE is identical: {np.allclose(query_fde, query_fde_2)} ) print( fWrapper-generated Document FDE is identical: {np.allclose(doc_fde, doc_fde_2)} ) print(\nAll test scenarios completed successfully.)结语随着 ColBERT、ColPali 等多向量模型的进一步发展以及 MUVERA 这类优化算法的不断演进多向量检索的效率瓶颈正在逐步被克服。未来在推荐系统、搜索引擎、文档检索等场景中多向量技术很可能成为标准配置。而 MUVERA 所展示的将复杂问题简化为经典问题的思路也为其他领域的算法优化提供了有价值的参考。​最后我在一线科技企业深耕十二载见证过太多因技术卡位而跃迁的案例。那些率先拥抱 AI 的同事早已在效率与薪资上形成代际优势我意识到有很多经验和知识值得分享给大家也可以通过我们的能力和经验解答大家在大模型的学习中的很多困惑。我整理出这套 AI 大模型突围资料包✅AI大模型学习路线图✅Agent行业报告✅100集大模型视频教程✅大模型书籍PDF✅DeepSeek教程✅AI产品经理入门资料完整的大模型学习和面试资料已经上传带到CSDN的官方了有需要的朋友可以扫描下方二维码免费领取【保证100%免费】​​为什么说现在普通人就业/升职加薪的首选是AI大模型人工智能技术的爆发式增长正以不可逆转之势重塑就业市场版图。从DeepSeek等国产大模型引发的科技圈热议到全国两会关于AI产业发展的政策聚焦再到招聘会上排起的长队AI的热度已从技术领域渗透到就业市场的每一个角落。智联招聘的最新数据给出了最直观的印证2025年2月AI领域求职人数同比增幅突破200%远超其他行业平均水平整个人工智能行业的求职增速达到33.4%位居各行业榜首其中人工智能工程师岗位的求职热度更是飙升69.6%。AI产业的快速扩张也让人才供需矛盾愈发突出。麦肯锡报告明确预测到2030年中国AI专业人才需求将达600万人人才缺口可能高达400万人这一缺口不仅存在于核心技术领域更蔓延至产业应用的各个环节。​​资料包有什么①从入门到精通的全套视频教程⑤⑥包含提示词工程、RAG、Agent等技术点② AI大模型学习路线图还有视频解说全过程AI大模型学习路线③学习电子书籍和技术文档市面上的大模型书籍确实太多了这些是我精选出来的④各大厂大模型面试题目详解⑤ 这些资料真的有用吗?这份资料由我和鲁为民博士共同整理鲁为民博士先后获得了北京清华大学学士和美国加州理工学院博士学位在包括IEEE Transactions等学术期刊和诸多国际会议上发表了超过50篇学术论文、取得了多项美国和中国发明专利同时还斩获了吴文俊人工智能科学技术奖。目前我正在和鲁博士共同进行人工智能的研究。所有的视频教程由智泊AI老师录制且资料与智泊AI共享相互补充。这份学习大礼包应该算是现在最全面的大模型学习资料了。资料内容涵盖了从入门到进阶的各类视频教程和实战项目无论你是小白还是有些技术基础的这份资料都绝对能帮助你提升薪资待遇转行大模型岗位。智泊AI始终秉持着“让每个人平等享受到优质教育资源”的育人理念‌通过动态追踪大模型开发、数据标注伦理等前沿技术趋势‌构建起前沿课程智能实训精准就业的高效培养体系。课堂上不光教理论还带着学员做了十多个真实项目。学员要亲自上手搞数据清洗、模型调优这些硬核操作把课本知识变成真本事‌​​​​如果说你是以下人群中的其中一类都可以来智泊AI学习人工智能找到高薪工作一次小小的“投资”换来的是终身受益应届毕业生‌无工作经验但想要系统学习AI大模型技术期待通过实战项目掌握核心技术。零基础转型‌非技术背景但关注AI应用场景计划通过低代码工具实现“AI行业”跨界‌。业务赋能 ‌突破瓶颈传统开发者Java/前端等学习Transformer架构与LangChain框架向AI全栈工程师转型‌。获取方式有需要的小伙伴可以保存图片到wx扫描二v码免费领取【保证100%免费】**​