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...
论文提出了一种新的Graph RAG方法,结合了知识图谱生成、检索增强生成(RAG)和查询聚焦摘要(QFS),以支持对整个文本集合的人类感知。通过构建基于实体的知识图谱,并使用社区检测算法将图分割成模块化社区,论文方法能够在处理全局性问题时,通过社区摘要生成更全面和多样化的答案。 具体方法包括: 文本分块(Source Documents...
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?
Enhanced RAG Workflow with Knowledge Graphs:The user query first interacts with the knowledge graph to enrich context before moving to retrieval and generation. Practical Examples Example 1: Career Recommendation System Scenario: A user asks the system: ...
opens in new tabGraphAcademy Research Center Case Studies Executive Insights Events Calendar GraphSummit Connections Webinars opens in new tabGraphRAG Partners Find a Partner Become a Partner Solution Partners OEM Partners Technology Partners opens in new tabPartner Portal Login ...
GraphRAG是一种结构化、分层的RAG方法,通过从原始文本中提取知识图谱、构建社区层次结构、生成社区总结等步骤,增强大型语言模型在私有数据集上的推理能力。 核心思路步骤: >> 索引:将输入语料切分为TextUnits;使用大型语言模型从TextUnits中提取实体、关系和关键声明;使用Leiden技术对图谱进行分层聚类;从底层到顶层生成每...
I am always intrigued by new approaches to implementing Retrieval-Augmented Generation (RAG) over graphs, often called GraphRAG. However, it seems that everyone has a different implementation in mind when they hear the term GraphRAG. In this blog post, we will dive deep into the “From Local...
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取实体。 从图存储中以知识序列(Knowledge Sequence)的形式获取子图谱(Subgra...
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_...
What would you like to see? I would like to see the support for adding Knowledge Graph with RAG . something similar to this .