如前所述,典型的 RAG 应用都假设有一个底层的 VectorDB,并存储了所有嵌入的知识库。然而,当涉及到 GraphRAG 时,这种方法稍有不同。 事实上,GraphRAG 在“检索”阶段发挥作用,利用图结构的灵活性来存储知识库,目标是检索到更相关的文档片段,然后将其增强为上下文(你可以在这里阅读微软关于GraphRAG的第一个实验(...
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实体记忆: ConversationEntityMemory 用于跟踪对话中提到的特定实体。 向量存储记忆: VectorStoreRetrieverMemory 利用向量数据库进行大规模信息存储和检索。 键值记忆: ConversationKVMemory 用于快速访问特定信息。 组合记忆: CombinedMemory 允许同时使用多种记忆类型。 只读记忆: ReadOnlySharedMemory 提供只读的共享信息访问。
Integrating Neo4j With LangChain4j for GraphRAG Vector Stores and Retrievers 15 min read Cypher & GQL Developer Text2Cypher: The Impact of Difficult Example Selection 6 min read Cypher & GQL Developer The Impact of Schema Representation in the Text2Cypher Task ...
钱,我前文中说的要LLM参与的几个点,那没一个是省token的,而且是预加载过程,也就是说只要你想玩GraphRAG,那这钱是省不了的,基于VectorDB和BM25的都没不需要这个。 新的数据加入,图这玩意儿不同于向量数据库,你愿意新增几条,大可新增几条,只要performance够就行,你每次加新数据,如果和多条边或者节点相关,就...
defretriever(question:str):print(f"Search query: {question}") structured_data = structured_retriever(question) unstructured_data =[el.page_content for el in vector_index.similarity_search(question)] final_data =f"""Structured data:{structured_data}Unstructured data:{"#Document ". join(...
Embedded property graph database built for speed. Vector search and full-text search built in. Implements Cypher. - kuzudb/kuzu
Neo4j Vector Index and GraphCypherQAChain for optimizing the synthesis of information for informed response generation with Mistral-7b Information retrieval and knowledge extraction is a growing field, which has witnessed a notable shift with the advent of Large Language Models (LLMs) and knowledge ...
以及Graph RAG 与 Vector RAG 的结果对比,相比单独的向量搜索,有了知识图谱的 RAG 会更加精准。 向量检索 vs 关键词检索 vs 混合检索怎么选? 基于Wey 在 Llama Index 以及 LangChain 的 Graph + RAG 贡献,海外工程师 Wenqi Glantz 对所有 Graph + LLM、RAG 方法进行了全面的实验、评估、综述、总结和分析。《...
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