命令描述CREATE VECTOR INDEX [index_name] [IF NOT EXISTS] FOR (n:LabelName) ON (n.propertyName) OPTIONS "{" option: value[, ...] "}"在节点上创建矢量索引。选项映射是强制性的,因为在创建矢量索引时必须设置矢量维度和相似性函数CREATE VECTOR INDEX [index_name] [IF NOT EXISTS] FOR ()-”[...
elif query_engine_type == Neo4jQueryEngineType.RAW_VECTOR: # Raw vector index retrieval self.query_engine = vector_index.as_query_engine() elif query_engine_type == Neo4jQueryEngineType.RAW_VECTOR_KG_COMBO: from llama_index.query_engine import RetrieverQueryEngine # create neo4j custom retrieve...
index_name=VECTOR_INDEX_NAME, text_node_property=VECTOR_SOURCE_PROPERTY, embedding_node_property=VECTOR_EMBEDDING_PROPERTY, ) 构建关系:我们在图中的块之间建立关系,指示它们的顺序以及它们与父PDF文档的关联。 # Create a PDF nodecypher= """
Neo4j was and is an excellent fit foropens in new tabhandling structured information, but it struggled a bit with semantic search due to its brute-force approach. However, the struggle is in the past as Neo4j hasintroduced a new vector index in version 5.11designed to efficiently perform seman...
AddCREATE VECTOR INDEX ...command for node vector indexes. As the vector dimensions and similarity function is mandatory, they need to be specified as part of theindexConfigin theOPTIONSmap. Fixes issuehttps://github.com/neo4j/neo4j/issues/13077where some valid property names were not allowed ...
When you initialize the Neo4jVectorStore, you specify the embedding_dimension parameter. This dimension is used when creating a new vector index in the create_new_index method. If you have changed the embedding_dimension to 1024 for your custom embedding model, you need to ensure that the vecto...
Easy out-of-the-box setup empowers developers with numerous capabilities, such as effortless data loading and vector index population, enabling developers to seamlessly import data, create vector indices, embed questions and answers, and store them within the vector index. Developers can do enhanced...
The Neo4j integration covers both the vector store as well as query generation from natural language and knowledge graph construction. Functionality Includes Neo4jVector The Neo4j Vector integration supports a number of operations create vector from LlamaIndex documents ...
neo4j_vector = Neo4jVector.from_existing_graph( embedding=embeddings, index_name="keyelements", node_label="KeyElement", text_node_properties=["id"], embedding_node_property="embedding", retrieval_query="RETURN node.id AS text, score, {} AS metadata" ) def get_potential_nodes(question: str...