鉴于官方文档对KnowledgeGraphRAGRetriever类的说明有限,本文从源码进行解析。 KnowledgeGraphRAGRetriever实际上是一个检索器对象,将问题或任务作为输入,对知识图谱执行子图谱 RAG 的检索,将检索结果(节点)返回,作为下游的LLM或者query engine使用。 KnowledgeGraphRAGRetriever的工作原理如下: 从用户自然语言的查询字符串中获...
3. Knowledge Graph+RAG 在RAG(Retrieval-Augmented Generation)框架中,我们将文档切成小段(chunk),然后通过检索模块找到与查询相关的文档片段。这种方法可以提高生成文本的准确性和相关性,同时保持生成文本的时效性和专业性。 然而,在实际文本中,chunk与chunk之间是存在关联的,而RAG技术并未充分考虑到这种关联性。为了...
query处理 总结 itext2kg(创建知识图谱) ragflow graphrag prompt_tune生成prompt index_cli创建graph index 总结 KAG(VectorRAG+GraphRAG) HybridRAG(VectorRAG+GraphRAG) LazyGraphRAG GraphReader PGraphRAG(个性化的知识图谱) DB-GPT(graph tookit) 总结 参考文献 akaihaoshuai:LLM之Rag学习1:RAG->GraphRAG...
classKnowledgeGraphQueryEngine(BaseQueryEngine):"""Knowledge graph query engine.Query engine to call a knowledge graph.Args:service_context(Optional[ServiceContext]):A service context to use.storage_context(Optional[StorageContext]):A storage context to use.refresh_schema(bool):Whether to refresh the ...
GraphRAG工作的第一步,是将输入的文档集合,按一定的策略拆分成一个一个chunks,然后解析每个chunks,将chunk中所关注的实体(entity)和关系(relation)解析出来,以此构建知识图谱。 那问题来了,GraphRAG是如何抽取文本中的实体及其间的关系,是像以前NLP任务那样,通过标注文本词性的方式,来训练一个LSTM/GRU网络来实现吗?
On the left side below is the first approach, which has two elements: the documents in Elasticsearch and the graph database. In this approach, when a user performed a query, it goes through two different phases. The first one is the classical textual search query that is performed entirely...
git clone https://github.com/yourusername/graph-rag-implementation.git cd graph-rag-implementation2️⃣ Install dependenciespip install -r requirements.txt💻 Usagefrom graph_rag import GraphRAG # Initialize the RAG model rag = GraphRAG() # Process your query results = rag.query("Your ...
Knowledge Graph RAG Project Environment Setup Execute the following commands to install the required dependencies. If you don't have a conda environment, please skip the first two steps: # Create and activate conda environment (optional) conda create --name kg_env python=3.10 conda activate kg_...
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The RobustQA benchmark evaluated eight different RAG approaches on 50,000 questions across eight domains and over 32 million documents, reflecting real-world complexity and variations in query phrasing. Writer Knowledge Graph uses a specialized large language model (LLM) to create semantic relationships...