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Types & classes51 in github.com/Enfoirer/Text2GraphRAG

⨍ Functions 365◇ Types & classes 51
↓ 6 callersClassGraphQuery
图查询结构
rag_modules/graph_rag_retrieval.py:27
↓ 5 callersClassLLMRecipeAgent
OpenAI菜谱解析AI Agent
agent/recipe_ai_agent.py:61
↓ 5 callersClassRetrievalResult
检索结果数据结构
rag_modules/hybrid_retrieval.py:20
↓ 4 callersClassRecipeKnowledgeGraphBuilder
菜谱知识图谱构建器 - 支持分批保存和断点续传
agent/recipe_ai_agent.py:309
↓ 3 callersClassKnowledgeSubgraph
知识子图结构
rag_modules/graph_rag_retrieval.py:47
↓ 2 callersClassGraphNode
图节点数据结构
rag_modules/graph_data_preparation.py:15
↓ 2 callersClassQueryAnalysis
查询分析结果
rag_modules/intelligent_query_router.py:25
↓ 2 callersClassQueryParam
nano_graphrag/base.py:10
↓ 2 callersClassRecipeInfo
菜谱信息
agent/recipe_ai_agent.py:37
↓ 2 callersClassRelationship
nano_graphrag/entity_extraction/module.py:87
↓ 2 callersClassTokenizerWrapper
nano_graphrag/_utils.py:123
↓ 2 callersClassTypedEntityRelationshipExtractor
nano_graphrag/entity_extraction/module.py:261
↓ 2 callersClassTypedEntityRelationshipExtractorException
nano_graphrag/entity_extraction/module.py:236
↓ 1 callersClassAdvancedGraphRAGSystem
图RAG系统 核心特性： 1. 智能路由：自动选择最适合的检索策略 2. 双引擎检索：传统混合检索 + 图RAG检索 3. 图结构推理：多跳遍历、子图提取、关系推理 4. 查询复杂度分析：深度理解用户意图 5. 自适应学习：基于反馈
main.py:33
↓ 1 callersClassAmountNormalizer
用量标准化器
agent/amount_normalizer.py:10
↓ 1 callersClassCookingStep
烹饪步骤
agent/recipe_ai_agent.py:28
↓ 1 callersClassEmbeddingFunc
nano_graphrag/_utils.py:266
↓ 1 callersClassEntityKeyValue
实体键值对
rag_modules/graph_indexing.py:19
↓ 1 callersClassGenerationIntegrationModule
生成集成模块 - 负责答案生成
rag_modules/generation_integration.py:15
↓ 1 callersClassGraphDataPreparationModule
图数据库数据准备模块 - 从Neo4j读取数据并转换为文档
rag_modules/graph_data_preparation.py:30
↓ 1 callersClassGraphIndexingModule
图索引模块 核心功能： 1. 为实体创建键值对（名称作为唯一索引键） 2. 为关系创建键值对（多个索引键，包含全局主题） 3. 去重和优化图操作 4. 支持增量更新
rag_modules/graph_indexing.py:38
↓ 1 callersClassGraphPath
图路径结构
rag_modules/graph_rag_retrieval.py:38
↓ 1 callersClassGraphRAG
nano_graphrag/graphrag.py:53
↓ 1 callersClassGraphRAGConfig
基于图数据库的RAG系统配置类
config.py:9
↓ 1 callersClassGraphRAGRetrieval
真正的图RAG检索系统 核心特点： 1. 查询意图理解：识别图查询模式 2. 多跳图遍历：深度关系探索 3. 子图提取：相关知识网络 4. 图结构推理：基于拓扑的推理 5. 动态查询规划：自适应遍历策略
rag_modules/graph_rag_retrieval.py:55
↓ 1 callersClassHybridRetrievalModule
混合检索模块 核心特点： 1. 双层检索范式（实体级 + 主题级） 2. 关键词提取和匹配 3. 图结构+向量检索结合 4. 一跳邻居扩展 5. Round-robin轮询合并策略
rag_modules/hybrid_retrieval.py:29
↓ 1 callersClassIngredientInfo
食材信息
agent/recipe_ai_agent.py:19
↓ 1 callersClassIntelligentQueryRouter
智能查询路由器 核心能力： 1. 查询复杂度分析：识别简单查找 vs 复杂推理 2. 关系密集度评估：判断是否需要图结构优势 3. 策略自动选择：路由到最适合的检索引擎 4. 结果质量监控：基于反馈优化路由决策
rag_modules/intelligent_query_router.py:35
↓ 1 callersClassMilvusIndexConstructionModule
Milvus索引构建模块 - 负责向量化和Milvus索引构建
rag_modules/milvus_index_construction.py:16
↓ 1 callersClassQueryType
查询类型枚举
rag_modules/graph_rag_retrieval.py:18
↓ 1 callersClassRecipeTextToGraphBuilder
Build a recipe knowledge graph directly from raw markdown files. The builder first relies on :class:`nano_graphrag.graphrag.GraphRAG` to
rag_modules/text_to_graph_ingestor.py:115
↓ 1 callersClassRelationKeyValue
关系键值对
rag_modules/graph_indexing.py:28
↓ 1 callersClassSearchStrategy
搜索策略枚举
rag_modules/intelligent_query_router.py:18
↓ 1 callersClassSeparatorSplitter
nano_graphrag/_splitter.py:3
↓ 1 callersClass_Neo4jRecipeGraphWriter
Helper that converts the extracted NetworkX graph into Neo4j nodes/edges.
rag_modules/text_to_graph_ingestor.py:239
ClassAssessRelationships
Assess the similarity between gold and predicted relationships: 1. Match relationships based on src_id and tgt_id pairs, allowing for slight
nano_graphrag/entity_extraction/metric.py:5
ClassBaseGraphStorage
nano_graphrag/base.py:117
ClassBaseKVStorage
nano_graphrag/base.py:93
ClassBaseVectorStorage
nano_graphrag/base.py:78
ClassCombinedExtraction
Given a text document that is potentially relevant to this activity and a list of entity types, identify all entities of those types from the
nano_graphrag/entity_extraction/module.py:117
ClassCommunitySchema
nano_graphrag/base.py:51
ClassCritiqueCombinedExtraction
Critique the current extraction of entities and relationships from a given text. Focus on completeness, accuracy, and adherence to the provid
nano_graphrag/entity_extraction/module.py:162
ClassEntity
nano_graphrag/entity_extraction/module.py:65
ClassGraphRelation
图关系数据结构
rag_modules/graph_data_preparation.py:23
ClassHNSWVectorStorage
nano_graphrag/_storage/vdb_hnswlib.py:15
ClassJsonKVStorage
nano_graphrag/_storage/kv_json.py:11
ClassNanoVectorDBStorage
nano_graphrag/_storage/vdb_nanovectordb.py:12
ClassNeo4jStorage
nano_graphrag/_storage/gdb_neo4j.py:20
ClassNetworkXStorage
nano_graphrag/_storage/gdb_networkx.py:20
ClassRefineCombinedExtraction
Refine the current extraction of entities and relationships based on the provided critique. Improve completeness, accuracy, and adherence to
nano_graphrag/entity_extraction/module.py:196
ClassStorageNameSpace
nano_graphrag/base.py:60