Semantica 去重模块实战指南知识图谱实体查重与合并的完整技术方案【免费下载链接】semanticaGraph-Native Infrastructure for Context and Accountable AI Systems项目地址: https://gitcode.com/GitHub_Trending/sema/semantica导读本文是 Semantica 开源项目中semantica.deduplication模块的完整使用指南围绕知识图谱构建中的实体重复问题系统讲解从多因子相似度计算、重复检测、实体合并到聚类批处理的完整技术链路。读完本文你将掌握如何使用SimilarityCalculator、DuplicateDetector、EntityMerger、ClusterBuilder等核心组件通过纯 Python dict 数据结构无需 ORM 或 schema完成知识图谱的清洗与单一事实来源single source of truth维护并学会通过方法注册表扩展自定义算法。模块定位与核心组件semantica.deduplication是 Semantica 框架中负责语义级实体去重的子模块用于检测并合并来自多个数据源的重复实体保证知识图谱的干净与一致性。根据模块文档semantica/deduplication/deduplication_usage.md与包导出定义semantica/deduplication/__init__.py模块对外暴露以下核心组件组件职责SimilarityCalculator多因子相似度计算引擎字符串、属性、关系、嵌入向量DuplicateDetector重复检测引擎支持 pairwise / batch / incremental / group 四种模式EntityMerger实体合并引擎支持多种合并策略与冲突消解MergeStrategyManager合并策略管理支持属性级合并规则ClusterBuilder聚类构建器用于大规模数据的批量去重MethodRegistry/method_registry方法注册表支持自定义方法的动态注册detect_duplicates()/merge_entities()/calculate_similarity()/build_clusters()便捷函数wrapper从模块源码看模块内部算法体系覆盖了 Levenshtein 编辑距离、Jaro/Jaro-Winkler 相似度、余弦相似度、Jaccard 关系交集、并查集Union-Find、层次聚类、多因子加权聚合等经典算法且所有工作流均以普通字典作为输入输出接入成本极低。一、相似度计算多因子评分引擎1.1 基础多因子相似度SimilarityCalculator是相似度计算的入口类其默认权重针对实体解析场景做了优化字符串相似度 0.6、属性相似度 0.2、关系相似度 0.2见similarity_calculator.py构造函数。注意构造时还支持传入embedding_weight默认 0.0权重不要求和恰好为 1.0——计算时引擎会自动归一化。from semantica.deduplication import SimilarityCalculator # 默认权重面向实体解析优化 # string0.6 (Jaro-Winkler), property0.2, relationship0.2 calculator SimilarityCalculator() # 自定义权重例如融合嵌入向量 calculator_custom SimilarityCalculator( string_weight0.6, property_weight0.2, embedding_weight0.2 ) entity1 {name: Apple Inc., type: Company} entity2 {name: Apple, type: Company} result calculator.calculate_similarity(entity1, entity2) print(fSimilarity score: {result.score:.2f}) print(fComponents: {result.components})calculate_similarity()返回一个SimilarityResult数据类包含score0~1 的综合得分、method使用的计算方法、components各分量得分键为string/property/relationship/embedding与metadata实际使用的权重、可选的分值明细。1.2 多种相似度方法通过便捷函数calculate_similarity()见methods.py可以显式指定计算方法from semantica.deduplication.methods import calculate_similarity entity1 {name: Apple Inc., type: Company} entity2 {name: Apple, type: Company} exact_result calculate_similarity(entity1, entity2, methodexact) # 精确匹配 lev_result calculate_similarity(entity1, entity2, methodlevenshtein) # 编辑距离 jaro_result calculate_similarity(entity1, entity2, methodjaro_winkler)# Jaro-Winkler prop_result calculate_similarity(entity1, entity2, methodproperty) # 属性比较 rel_result calculate_similarity(entity1, entity2, methodrelationship)# 关系 Jaccard multi_result calculate_similarity(entity1, entity2, methodmulti_factor)# 多因子默认支持的方法清单exact、levenshtein、jaro_winkler、cosine、property、relationship、embedding、multi_factor默认。1.3 字符串相似度算法细节calculate_string_similarity(str1, str2, method...)是字符串层面的核心接口默认方法为jaro_winkler。从源码实现看similarity_calculator.pyLevenshtein动态规划求解编辑距离相似度 1 - 距离/最大长度Jaro-Winkler在 Jaro 匹配窗口算法基础上对前 4 个字符的前缀匹配给予 0.1 权重的奖励加成Cosine基于字符 bigram 的交集/并集计算余弦相似度。from semantica.deduplication import SimilarityCalculator calculator SimilarityCalculator() lev_score calculator.calculate_string_similarity(Apple Inc., Apple, methodlevenshtein) jaro_score calculator.calculate_string_similarity(Apple Inc., Apple, methodjaro_winkler) cosine_score calculator.calculate_string_similarity(Apple Inc., Apple, methodcosine)性能提示batch_calculate_similarity()内部实现了分块blocking策略与短路short-circuit优化——包括前缀分块、blocking_v2/hybrid_v2候选生成策略、声码Soundex语音分块、max_candidates_per_entity候选对截断等见similarity_calculator.py用于降低 O(n²) 全量比较的开销适配大规模实体集。此外还有_prefilter_pair()两级预过滤类型不匹配直接拒绝、名称长度比与 token 重叠率低于阈值快速淘汰明显不相似的实体对。二、重复检测从候选对到重复组2.1 成对检测Pairwisefrom semantica.deduplication.methods import detect_duplicates entities [ {id: 1, name: Apple Inc.}, {id: 2, name: Apple}, {id: 3, name: Microsoft}, ] # 两两比较O(n²) candidates detect_duplicates( entities, methodpairwise, similarity_threshold0.8, confidence_threshold0.7 ) for candidate in candidates: print(fDuplicate: {candidate.entity1[name]} - {candidate.entity2[name]}) print(f Similarity: {candidate.similarity_score:.2f}) print(f Confidence: {candidate.confidence:.2f})detect_duplicates()便捷函数支持pairwise默认O(n²)、batch批量相似度计算、incrementalO(n×m)新实体 vs 存量实体、group并查集聚类成组四种检测方法。2.2 分组检测Groupfrom semantica.deduplication.methods import detect_duplicates # 并查集算法A≈B 且 B≈C 时三者归于同一组 groups detect_duplicates( entities, methodgroup, similarity_threshold0.8 ) for group in groups: print(fGroup with {len(group.entities)} entities:) print(f Confidence: {group.confidence:.2f}) print(f Representative: {group.representative[name] if group.representative else None}) for entity in group.entities: print(f - {entity[name]})detect_duplicate_groups()内部先检测候选对再用并查集构造传递闭包分组并为每组计算置信度、挑选代表实体属性与关系最多的实体见duplicate_detector.py与_select_representative实现。分组模式适合合并场景而成对模式只返回两两候选不具备传递性。2.3 增量检测Incrementalfrom semantica.deduplication.methods import detect_duplicates existing_entities [ {id: 1, name: Apple Inc.}, {id: 2, name: Microsoft}, ] new_entities [ {id: 3, name: Apple}, {id: 4, name: Google}, ] # 增量检测O(n×m)每个新实体只与存量实体比较 candidates detect_duplicates( new_entities, methodincremental, existing_entitiesexisting_entities, similarity_threshold0.8 ) for candidate in candidates: print(fNew entity {candidate.entity1[name]} duplicates existing {candidate.entity2[name]})2.4 直接使用 DuplicateDetectorfrom semantica.deduplication import DuplicateDetector detector DuplicateDetector( similarity_threshold0.8, confidence_threshold0.7, use_clusteringTrue ) candidates detector.detect_duplicates(entities) # 候选对 groups detector.detect_duplicate_groups(entities) # 重复组 new_candidates detector.incremental_detect(new_entities, existing_entities) # 增量DuplicateDetector构造函数还支持更多控制参数见duplicate_detector.py参数默认值说明similarity_threshold0.7判定重复的最低相似度先调这个再调置信度confidence_threshold0.6候选对的最低置信度use_clusteringTrue是否使用聚类辅助分组max_resultsNone全局候选对数量硬上限top_k_per_entityNone每个实体最多保留的候选对数量min_similarityNone排序后附加的相似度下限sort_byconfidence排序字段confidence或similarity_score2.5 置信度与类型守卫源码级行为_create_duplicate_candidate()duplicate_detector.py展示了置信度的多因子计算逻辑基础值等于相似度得分精确名称匹配 0.1每个匹配属性 0.05类型相同 0.05最终封顶 1.0。关键守卫两个实体若都显式声明了type且类型不同则无论相似度多高都会被判定为type_mismatch、置信度归零——这一行为在测试test_different_types_are_never_duplicates、test_zero_threshold_still_excludes_type_mismatch等用例中被严格验证见 tests/deduplication/test_deduplication.py。三、实体合并策略化冲突消解3.1 基础合并from semantica.deduplication.methods import merge_entities duplicate_entities [ {id: 1, name: Apple Inc., type: Company, founded: 1976}, {id: 2, name: Apple, type: Company, founded: 1976}, ] operations merge_entities( duplicate_entities, methodkeep_most_complete, preserve_provenanceTrue ) for op in operations: print(fMerged entity: {op.merged_entity[name]}) print(f Source entities: {len(op.source_entities)}) print(f Conflicts: {len(op.merge_result.conflicts)})注意merge_entities()与EntityMerger.merge_duplicates()返回的是List[MergeOperation]而非实体 dict 列表必须通过op.merged_entity取合并结果。3.2 五种合并策略result1 merge_entities(entities, methodkeep_first) # 保留第一个 result2 merge_entities(entities, methodkeep_last) # 保留最后一个 result3 merge_entities(entities, methodkeep_most_complete) # 保留最完整属性关系最多 result4 merge_entities(entities, methodkeep_highest_confidence) # 保留置信度最高 result5 merge_entities(entities, methodmerge_all) # 全部属性合并冲突转为列表策略对应的基座实体选择逻辑在merge_strategy.py的_select_base_entity()中实现keep_most_complete按属性数关系数取最大keep_highest_confidence按.confidence字段取最大。3.3 直接使用 EntityMergerfrom semantica.deduplication import EntityMerger, MergeStrategy merger EntityMerger(preserve_provenanceTrue) operations merger.merge_duplicates( entities, strategyMergeStrategy.KEEP_MOST_COMPLETE ) history merger.get_merge_history() # 合并历史返回副本防止外部修改 print(fTotal merge operations: {len(history)}) for op in operations: merged op.merged_entity print(fMerged: {merged[name]}) if merger.preserve_provenance: print(f Sources: {merged.get(_merged_from, [])})开启preserve_provenanceTrue时合并结果会在metadata.provenance下记录merged_from来源实体的 id、name、source 列表与merge_count合并数量形成可审计的溯源链见entity_merger.py的_add_provenance()。合并结果的顶层结构还包括merged_from、merge_strategy字段便于下游追踪。3.4 属性级自定义规则MergeStrategyManager支持为不同属性配置不同的合并策略与冲突消解函数这在处理同一属性在不同来源取值不同时非常实用from semantica.deduplication import MergeStrategyManager, MergeStrategy manager MergeStrategyManager(default_strategykeep_most_complete) # 属性级规则 manager.add_property_rule(name, MergeStrategy.KEEP_FIRST) manager.add_property_rule(description, MergeStrategy.MERGE_ALL) # 自定义冲突消解取最长值 def resolve_conflict(values): return max(values, keylen) manager.add_property_rule( description, MergeStrategy.CUSTOM, conflict_resolutionresolve_conflict ) result manager.merge_entities(entities) print(fMerged entity: {result.merged_entity}) print(fConflicts: {result.conflicts})源码中_resolve_property_conflict()merge_strategy.py展示了完整的冲突消解优先级先查属性专属规则的conflict_resolution自定义函数其次按规则策略keep_first / keep_last / merge_all最后回落到默认策略。merge_all会将冲突值合并为列表。关系合并时取并集去重并支持semantic_v2模式的谓词同义词归一与字面量规范化predicate_synonym_map、literal_normalization_enabled配置项。四、聚类大规模批量去重的前置步骤4.1 图聚类并查集from semantica.deduplication.methods import build_clusters entities [{id: str(i), name: fEntity {i}} for i in range(100)] result build_clusters( entities, methodgraph_based, # 并查集连通分量 similarity_threshold0.8, min_cluster_size2, max_cluster_size50 ) print(fFound {len(result.clusters)} clusters) print(fUnclustered entities: {len(result.unclustered)}) print(fQuality metrics: {result.quality_metrics}) for cluster in result.clusters: print(fCluster {cluster.cluster_id}: {len(cluster.entities)} entities) print(f Quality score: {cluster.quality_score:.2f})4.2 层次聚类from semantica.deduplication.methods import build_clusters # 面向大规模数据集的凝聚式自底向上聚类 result build_clusters( entities, methodhierarchical, similarity_threshold0.8 ) print(fFound {len(result.clusters)} clusters using hierarchical method)4.3 直接使用 ClusterBuilderfrom semantica.deduplication import ClusterBuilder builder ClusterBuilder( similarity_threshold0.8, min_cluster_size2, max_cluster_size50, use_hierarchicalFalse ) result builder.build_clusters(entities) for cluster in result.clusters: print(fCluster: {len(cluster.entities)} entities) if cluster.centroid: print(f Centroid: {cluster.centroid[name]}) print(f Quality: {cluster.quality_score:.2f})ClusterResult携带clusters、unclustered、quality_metrics平均大小、平均质量、总簇数、高质量簇数等指标三个核心字段见cluster_builder.py。实践中的标准打法先聚类缩小比较范围再在簇内做去重与合并从而把全量 O(n²) 比较降为簇内比较显著提升大规模数据处理效率。五、方法与注册表按需取用、自由扩展5.1 获取可用方法from semantica.deduplication.methods import list_available_methods, get_deduplication_method all_methods list_available_methods() print(Available methods:) for task, methods in all_methods.items(): print(f {task}: {methods}) similarity_methods list_available_methods(similarity) print(fSimilarity methods: {similarity_methods}) levenshtein_method get_deduplication_method(similarity, levenshtein) if levenshtein_method: result levenshtein_method(entity1, entity2) print(fLevenshtein similarity: {result.score:.2f})内置方法按四类任务组织similarity8 种、detection4 种、merging5 种、clustering2 种。5.2 注册自定义方法方法注册表采用任务task→ 方法名 → 函数的字典式组织见registry.py支持运行时动态注册与 O(1) 查找from semantica.deduplication.registry import method_registry from semantica.deduplication import SimilarityResult # 自定义相似度方法基于词重叠 def custom_similarity(entity1, entity2, **kwargs): name1 entity1.get(name, ).lower() name2 entity2.get(name, ).lower() words1 set(name1.split()) words2 set(name2.split()) if not words1 or not words2: return SimilarityResult(score0.0, methodcustom) overlap len(words1 words2) / len(words1 | words2) return SimilarityResult(scoreoverlap, methodcustom) # 注册 method_registry.register(similarity, word_overlap, custom_similarity) # 通过 get_deduplication_method 使用 custom_method get_deduplication_method(similarity, word_overlap) result custom_method(entity1, entity2) print(fCustom similarity: {result.score:.2f})from semantica.deduplication.registry import method_registry all_methods method_registry.list_all() # 全部注册方法 similarity_methods method_registry.list_all(similarity) # 按任务过滤注册表还提供unregister()与clear()方法用于注销与清空。便捷分发函数在调用时会先查注册表命中自定义方法则直接调用并带有一致性身份守卫identity guard防止自我引用导致的无限递归见methods.py中的注释说明。六、配置管理三层配置来源6.1 编程式配置Config Managerfrom semantica.deduplication.config import dedup_config # 读取配置支持默认值 threshold dedup_config.get(similarity_threshold, default0.7) confidence dedup_config.get(confidence_threshold, default0.6) # 写入配置 dedup_config.set(similarity_threshold, 0.8) dedup_config.set(confidence_threshold, 0.7) # 方法级配置 dedup_config.set_method_config(levenshtein, case_sensitiveFalse) levenshtein_config dedup_config.get_method_config(levenshtein) all_config dedup_config.get_all() # 返回 {config: ..., method_configs: ...}6.2 环境变量export DEDUP_SIMILARITY_THRESHOLD0.8 export DEDUP_CONFIDENCE_THRESHOLD0.7 export DEDUP_USE_CLUSTERINGtrue export DEDUP_PRESERVE_PROVENANCEtrueDeduplicationConfig支持的环境变量映射见config.pyDEDUP_SIMILARITY_THRESHOLD、DEDUP_CONFIDENCE_THRESHOLD、DEDUP_USE_CLUSTERING、DEDUP_PRESERVE_PROVENANCE、DEDUP_DEFAULT_STRATEGY、DEDUP_MIN_CLUSTER_SIZE、DEDUP_MAX_CLUSTER_SIZE。布尔值接受true/1/yes/on等写法非法值会被记录警告并跳过。6.3 配置文件YAML / JSON / TOML# config.yaml deduplication: similarity_threshold: 0.8 confidence_threshold: 0.7 use_clustering: true preserve_provenance: true deduplication_methods: levenshtein: case_sensitive: false multi_factor: string_weight: 0.4 property_weight: 0.3 embedding_weight: 0.3from semantica.deduplication.config import DeduplicationConfig config DeduplicationConfig(config_fileconfig.yaml) threshold config.get(similarity_threshold)配置文件解析同样支持.json与.toml格式。配置的解析优先级为配置文件 → 环境变量 → 默认值get()内部实现 fallback 链。仓库中的 cookbook/introduction/config.yaml 展示了 Semantica 全局配置文件的组织方式可作为多模块联合配置的参考。七、进阶实战完整去重流水线7.1 聚类 → 组检测 → 合并三步流水线from semantica.deduplication import ( DuplicateDetector, EntityMerger, MergeStrategy, ClusterBuilder ) # Step 1: 聚类批量处理的第一步 builder ClusterBuilder(similarity_threshold0.8) cluster_result builder.build_clusters(entities) # Step 2: 在每个簇内检测重复组 detector DuplicateDetector(similarity_threshold0.8) all_groups [] for cluster in cluster_result.clusters: groups detector.detect_duplicate_groups(cluster.entities) all_groups.extend(groups) # Step 3: 合并重复实体 merger EntityMerger(preserve_provenanceTrue) all_entities [e for cluster in cluster_result.clusters for e in cluster.entities] merge_operations merger.merge_duplicates( all_entities, strategyMergeStrategy.KEEP_MOST_COMPLETE ) print(fProcessed {len(cluster_result.clusters)} clusters) print(fFound {len(all_groups)} duplicate groups) print(fPerformed {len(merge_operations)} merge operations)7.2 融合嵌入向量的语义相似度from semantica.deduplication import SimilarityCalculator entity1 { name: Apple Inc., type: Company, embedding: [0.1, 0.2, 0.3, 0.4] # 向量嵌入 } entity2 { name: Apple, type: Company, embedding: [0.12, 0.21, 0.29, 0.38] # 相似嵌入 } calculator SimilarityCalculator( embedding_weight0.5, string_weight0.3, property_weight0.2 ) result calculator.calculate_similarity(entity1, entity2) print(fSimilarity: {result.score:.2f}) print(fEmbedding component: {result.components.get(embedding, 0):.2f})源码提示只有当两个实体都包含embedding键时才会计算嵌入分量嵌入相似度基于余弦相似度并归一化到 0~1 区间(cosine1)/2见similarity_calculator.py。若实体本身由 Semantica 的 embeddings 模块生成向量可直接与去重模块衔接。7.3 批量相似度计算from semantica.deduplication import SimilarityCalculator calculator SimilarityCalculator() similarity_pairs calculator.batch_calculate_similarity( entities, threshold0.7 ) print(fFound {len(similarity_pairs)} similar pairs above threshold) for entity1, entity2, score in similarity_pairs: print(f{entity1[name]} - {entity2[name]}: {score:.2f})batch_calculate_similarity()返回(entity1, entity2, score)三元组列表内部使用块索引block index生成候选对并支持候选对数量上限控制。7.4 合并历史追踪与质量校验from semantica.deduplication import EntityMerger merger EntityMerger(preserve_provenanceTrue) operations1 merger.merge_duplicates(entities1) operations2 merger.merge_duplicates(entities2) history merger.get_merge_history() print(fTotal merge operations: {len(history)}) for op in history: print(fMerge: {len(op.source_entities)} - 1) print(f Strategy: {op.merge_result.metadata.get(strategy)}) print(f Conflicts: {len(op.merge_result.conflicts)}) # 校验单个合并操作的质量 for op in operations1: validation merger.validate_merge_quality(op) print(fValid: {validation[valid]}, Quality: {validation[quality_score]:.2f})validate_merge_quality()返回{valid, issues, quality_score}检查合并结果是否缺少 name/type、是否存在未消解冲突见entity_merger.py与merge_strategy.py的validate_merge。7.5 属性差异化合并规则from semantica.deduplication import MergeStrategyManager manager MergeStrategyManager(default_strategykeep_most_complete) manager.add_property_rule(name, keep_first) manager.add_property_rule(description, merge_all) manager.add_property_rule(founded, keep_highest_confidence) # 日期类冲突取最新 def resolve_date_conflict(dates): return max(dates) manager.add_property_rule( last_updated, custom, conflict_resolutionresolve_date_conflict ) result manager.merge_entities(entities) print(fMerged entity: {result.merged_entity})add_property_rule()既接受MergeStrategy枚举也接受字符串策略名并支持priority优先级参数见merge_strategy.py。7.6 增量去重工作流流式场景from semantica.deduplication import DuplicateDetector, EntityMerger existing_entities load_initial_entities() detector DuplicateDetector(similarity_threshold0.8) merger EntityMerger(preserve_provenanceTrue) def process_new_entities(new_entities): # 1. 新实体 vs 存量实体增量检测 candidates detector.incremental_detect(new_entities, existing_entities) if candidates: duplicate_entities [c.entity1 for c in candidates] operations merger.merge_duplicates(duplicate_entities) # 2. 用合并结果替换旧的重复实体 for op in operations: existing_entities [ e for e in existing_entities if e not in op.source_entities ] existing_entities.append(op.merged_entity) # 3. 非重复的新实体直接入库 duplicate_ids {c.entity1.get(id) for c in candidates} new_non_duplicates [ e for e in new_entities if e.get(id) not in duplicate_ids ] existing_entities.extend(new_non_duplicates) return existing_entities # 流式处理 for batch in stream_new_entities(): existing_entities process_new_entities(batch)除了手工编排EntityMerger还内置了incremental_merge(new_entities, existing_entities)方法自动完成增量检测 → 逐对合并 → 去重防重复合并的完整流程见entity_merger.py。八、最佳实践与调优建议综合原文档与源码实现以下是经过验证的实践要点先定相似度阈值再调置信度阈值相似度阈值决定哪些实体对进入候选similarity_threshold0.7起步置信度阈值对候选进一步过滤。追求精确率用 0.8~0.9追求召回率用 0.6~0.7。提高相似度阈值是降低误报false positives最直接的手段。大规模数据先聚类再去重使用ClusterBuilder构建簇再在簇内执行检测与合并避免全量 O(n²) 比较。仓库参考文档 docs/reference/deduplication.md 同时提到 v2 候选生成策略blocking_v2、hybrid_v2、semantic_v2可用于进一步加速大规模实体集处理。开启溯源provenancepreserve_provenanceTrue会在合并结果中记录merged_from与merge_count为审计与回溯提供完整证据链可衔接框架的 provenance 模块。流式数据用增量检测incremental_detect/incremental_merge将比较复杂度从 O(n²) 降为 O(n×m)适合持续到达的新数据批次。属性级规则提升合并质量不同字段name、description、日期等往往需要不同的合并与冲突消解策略MergeStrategyManager的按属性规则是最佳落点。合并后必须校验通过get_merge_history()与validate_merge_quality()检查合并历史与未消解冲突保证质量可控。按数据特性调权重多因子相似度的 string/property/relationship/embedding 权重应依据数据特征调整——例如以名称为主的实体集加大string_weight有向量嵌入时引入embedding_weight。去重前先归一化Apple Inc.与apple inc可能因大小写差异低于阈值建议先使用 normalize 模块 的EntityNormalizer或TextNormalizer做规范化再进入去重流程。类型不同的实体永远不是重复源码与测试均确认显式类型不一致的实体对会被结构性排除type_mismatch无需依赖阈值调节。用detect_duplicate_groups()准备合并group 模式基于并查集形成传递闭包A≈B 且 B≈C 时三者同组更适合直接衔接merge_duplicates()。结语Semantica 的去重模块以多因子相似度 → 多模式检测 → 策略化合并 → 聚类批处理为主线通过纯 dict 接口、可插拔的方法注册表与三层配置体系为知识图谱的实体清洗提供了完整而灵活的解决方案。其核心实现均位于 semantica/deduplication/ 目录对应测试覆盖于 tests/deduplication/test_deduplication.py 与 tests/deduplication/test_cluster_merge_coverage.py可作为深入阅读与二次开发的起点。【免费下载链接】semanticaGraph-Native Infrastructure for Context and Accountable AI Systems项目地址: https://gitcode.com/GitHub_Trending/sema/semantica创作声明：本文部分内容由AI辅助生成（AIGC），仅供参考