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以下资源提供了在 Neo4j 中使用 LLM 和向量索引的实践教程：

  • GraphAcademy → LLM 和 Neo4j 基础知识

  • GraphAcademy → 向量索引和非结构化数据简介

  • GenAI 文档 → 嵌入和向量索引教程

Neo4j 向量索引由Apache Lucene索引和搜索库提供支持。 [ 1 ]

示例图

本页上的示例使用Neo4j 电影推荐数据集，重点关注节点的plot和属性。属性由和属性组合而成的 1536 维向量嵌入组成。embeddingMovieembeddingplottitle

该图包含 28863 个节点和 332522 个关系。

要重新创建图表，请下载此转储文件并将其导入空的 Neo4j 数据库（运行 5.13 或更高版本）。可以为Aura和本地实例导入转储文件。

用于加载数据集的转储文件包含使用模型由OpenAItext-embedding-ada-002生成的嵌入。

Neo4j 中的向量和嵌入

向量索引允许您从大型数据集中查询向量嵌入。嵌入是数据对象（例如文本、图像或文档）的数值表示。文本中的每个单词或标记通常表示为高维向量，其中每个维度代表单词含义的某个方面。

特定数据对象的嵌入可以由专有（例如Vertex AI或OpenAI）和开源（例如sentence-transformers）嵌入生成器创建，它们可以生成维度为 256、768、1536 和 3072 的向量嵌入。在 Neo4j 中，向量嵌入存储为LIST<INTEGER | FLOAT>节点或关系上的属性。

有关如何生成嵌入并将其存储为属性的信息，请参阅：

  • GenAI 集成

  • GenAI 文档 → 嵌入和向量索引教程

例如电影《教父》有以下内容plot："The aging patriarch of an organized crime dynasty transfers control of his clandestine empire to his reluctant son." 这是它的 1536 维embedding属性，其中每个元素LIST代表情节含义的某个特定方面：

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语义上相似的单词通常由在这个向量空间中彼此接近的向量表示。这使得加法和减法等数学运算能够传达语义。例如，“国王”减去“男人”加上“女人”的向量表示应该接近“女王”的向量表示。换句话说，向量嵌入是特定数据对象的数值表示。

向量索引允许您根据节点或关系的嵌入属性与查询中指定的属性之间的相似性来检索节点或关系的邻域。

创建向量索引

向量索引是节点的单标签、单属性索引或关系的单关系类型、单属性索引。它可用于通过LIST<INTEGER | FLOAT>对索引的维度和向量相似度函数有效的属性来索引节点或关系。请注意，可用的向量索引提供程序（vector-2.0（默认）和vector-1.0）支持不同的索引架构、属性值类型和向量维度。有关更多信息，请参阅兼容性的向量索引提供程序。

使用CREATE VECTOR INDEX命令创建向量索引，建议在创建索引时指定名称，如果创建时未指定名称，则会分配一个随机名称。从 Neo4j 5.16 开始，索引名称也可以作为参数指定：CREATE VECTOR INDEX $name …​。

索引名称在索引和约束中必须是唯一的。
新创建的索引不会立即可用，而是在后台创建。
创建索引需要 权限CREATE INDEX。
为属性Movie上的节点创建向量索引embedding在 5.15 中引入 
CREATE VECTOR INDEX moviePlots IF NOT EXISTS
FOR (m:Movie)
ON m.embedding
OPTIONS {indexConfig: {
 `vector.dimensions`: 1536,
 `vector.similarity_function`: 'cosine'
}}
该CREATE VECTOR INDEX命令是可选的幂等命令。这意味着，如果尝试两次创建相同的索引，则其默认行为是抛出错误。使用IF NOT EXISTS，如果已存在具有相同名称、架构或两者的索引，则不会抛出任何错误，也不会发生任何事情。如果存在具有相同名称的约束，它仍可能会抛出错误。从 Neo4j 5.17 开始，当什么都没有发生时会返回信息通知，显示阻止创建的现有索引。
该OPTIONS映射是必需的，因为如果不设置向量维度和相似度函数，就无法创建向量索引。在此示例中，向量维度设置为1536，向量相似度函数为cosine，这通常是文本嵌入的首选相似度函数。要了解有关可用相似度函数的更多信息，请参阅余弦和欧几里得相似度函数。
在 Neo4j 5.15 之前，节点向量索引是使用该db.index.vector.createNodeIndex过程创建的。

您还可以使用以下语法为给定属性上具有特定类型的关系创建向量索引：

为单个属性上的关系类型创建向量索引在 5.18 中引入 
CREATE VECTOR INDEX name IF NOT EXISTS
FOR ()-[r:REL_TYPE]-() ON (r.embedding)
OPTIONS {indexConfig: {
 `vector.dimensions`: $dimension,
 `vector.similarity_function`: $similarityFunction
}}

查询向量索引

要查询节点向量索引，请使用该db.index.vector.queryNodes过程。

签名db.index.vector.queryNodes 
db.index.vector.queryNodes(indexName :: STRING, numberOfNearestNeighbours :: INTEGER, query :: ANY) :: (node :: NODE, score :: FLOAT)

  • 指indexName的是要查询的向量索引的唯一名称。

  • 指numberOfNearestNeighbours的是要返回的最近邻居的数量。

  • 该query向量指的是在LIST<INTEGER | FLOAT>其中搜索邻域。

该过程返回节点的邻域及其各自的相似性得分，并按得分排序。得分介于0和之间1，得分越接近1，索引向量与查询向量越相似。

找出与《教父》情节最相似的 5 部电影 
MATCH (m:Movie {title: 'Godfather, The'})
CALL db.index.vector.queryNodes('moviePlots', 5, m.embedding)
YIELD node AS movie, score
RETURN movie.title AS title, movie.plot AS plot, score
结果 
<span style="color:#5e636a"><span style="background-color:#ffffff"><span style="color:#1a1b1d"><code class="language-none">+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| title                      | plot                                                                                                                                                                                                                     | score              |
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| "Godfather, The"           | "The aging patriarch of an organized crime dynasty transfers control of his clandestine empire to his reluctant son."                                                                                                    | 1.0                |
| "Godfather: Part III, The" | "In the midst of trying to legitimize his business dealings in New York and Italy in 1979, aging Mafia don Michael Corleone seeks to avow for his sins while taking a young protégé under his wing."                     | 0.9648237228393555 |
| "Godfather: Part II, The"  | "The early life and career of Vito Corleone in 1920s New York is portrayed while his son, Michael, expands and tightens his grip on his crime syndicate stretching from Lake Tahoe, Nevada to pre-revolution 1958 Cuba." | 0.9547788500785828 |
| "Scarface"                 | "An ambitious and near insanely violent gangster climbs the ladder of success in the mob, but his weaknesses prove to be his downfall."                                                                                  | 0.9367183446884155 |
| "Jane Austen's Mafia!"     | "Takeoff on the Godfather with the son of a mafia king taking over for his dying father"                                                                                                                                 | 0.9366795420646667 |
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+</code></span></span></span>

请注意，返回的所有电影都围绕犯罪家族组织展开情节。结果按降序score排列，其中最匹配的结果条目放在第一位（在本例中，相似度得分为，这是可以预料的，因为索引是使用此特定属性查询的）。如果不需要查询向量本身，则添加谓词会删除相同的向量。The Godfather1.0WHERE score < 1

要查询关系向量索引，请使用该db.index.vector.queryRelationships过程。

签名db.index.vector.queryRelationships 在 5.18 中引入 
db.index.vector.queryRelationships(indexName :: STRING, numberOfNearestNeighbours :: INTEGER, query :: ANY) :: (relationship :: RELATIONSHIP, score :: FLOAT)

db.index.vector.queryRelationships具有与 相同的参数描述db.index.vector.queryNodes。

使用向量函数计算两个特定向量对之间的相似度得分，而无需使用向量索引。

性能建议

向量索引可以利用孵化的 Java 20 Vector API 来显著提高速度。如果您使用的是兼容版本的 Java，则可以将以下设置添加到配置设置中：

配置设置 
server.jvm.additional=--add-modules jdk.incubator.vector

显示向量索引

要列出数据库中的所有向量索引，请使用该SHOW VECTOR INDEXES命令。此SHOW命令与其他索引的命令相同，但索引类型筛选为VECTOR。

列出索引需要 权限SHOW INDEX。
例 1. 显示所有向量索引 使用默认返回列显示向量索引 
SHOW VECTOR INDEXES
结果 
<span style="color:#5e636a"><span style="background-color:#ffffff"><span style="color:#1a1b1d"><code class="language-none">+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| id | name         | state    | populationPercent | type     | entityType | labelsOrTypes | properties        | indexProvider | owningConstraint | lastRead                 | readCount |
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 2  | "moviePlots" | "ONLINE" | 100.0             | "VECTOR" | "NODE"     | ["Movie"]     | ["embedding"]     | "vector-2.0"  | NULL             | 2024-05-07T09:19:09.225Z | 47        |
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+</code></span></span></span>

有关所有返回列的完整描述，请参阅搜索结果索引 → 列表索引的结果列。

示例 2.显示具有完整或筛选详细信息的向量索引

要返回完整的向量索引详细信息，请使用YIELD *。

显示所有向量索引及所有返回列 
SHOW VECTOR INDEXES YIELD *
结果 
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| id | name         | state    | populationPercent | type     | entityType | labelsOrTypes | properties        | indexProvider | owningConstraint | lastRead                 | readCount | trackedSince             | options                                                                                                         | failureMessage | createStatement                                                                                                                                                                                  |
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 2  | "moviePlots" | "ONLINE" | 100.0             | "VECTOR" | "NODE"     | ["Movie"]     | ["embedding"]     | "vector-2.0"  | NULL             | 2024-05-07T09:19:09.225Z | 47        | 2024-05-07T08:26:19.072Z | {indexConfig: {`vector.dimensions`: 1536, `vector.similarity_function`: "COSINE"}, indexProvider: "vector-2.0"} | ""             | "CREATE VECTOR INDEX `moviePlots` FOR (n:`Movie`) ON (n.`embedding`) OPTIONS {indexConfig: {`vector.dimensions`: 1536,`vector.similarity_function`: 'COSINE'}, indexProvider: 'vector-2.0'}"     |
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

要仅返回特定的详细信息，请在子句后指定所需的列名YIELD。

显示所有带有过滤返回列的向量索引 
SHOW VECTOR INDEXES YIELD name, type, entityType, labelsOrTypes, properties
结果 
+----------------------------------------------------------------------+
| name         | type     | entityType | labelsOrTypes | properties    |
+----------------------------------------------------------------------+
| "moviePlots" | "VECTOR" | "NODE"     | ["Movie"]     | ["embedding"] |
+----------------------------------------------------------------------+

删除向量索引

使用与其他索引相同的命令删除向量索引。DROP INDEX从 Neo4j 5.16 开始，删除索引时也可以将索引名称作为参数给出： DROP INDEX $name。

删除索引需要 权限DROP INDEX。
删除向量索引 
DROP INDEX moviePlots

用于兼容性的向量索引提供程序

从 Neo4j 5.18 开始，默认和首选的向量索引提供程序是vector-2.0。以前创建的vector-1.0索引将继续起作用。vector-1.0如果在映射中指定了提供程序，仍然可以使用该提供程序创建新索引OPTIONS。

余弦和欧几里得相似函数

相似度函数的选择会影响哪些索引向量被视为相似，哪些是有效的。向量的语义含义本身可能决定选择哪个相似度函数。请参阅您正在使用的特定向量嵌入模型的文档，因为它可能会建议对某些相似度函数的偏好。否则，能够区分各种相似度函数可以帮助做出更明智的决定。

表 1. 相似度函数
姓名不区分大小写的参数关键相似特征

余弦

"cosine"

角度

欧几里得

"euclidean"

距离

-标准化向量（单位向量），余弦和欧几里得相似函数产生相同的相似性顺序。

向量索引程序

用法程序描述

创建节点向量索引。

db.index.vector.createNodeIndex

使用给定的相似度函数为具有给定向量维度的指定标签和属性创建向量索引。由命令替换CREATE VECTOR INDEX。

使用节点向量索引。

db.index.vector.queryNodes

查询给定的节点向量索引。返回请求的近似最近邻节点数量及其相似度得分，按得分排序。

使用关系向量索引。

db.index.vector.queryRelationships

查询给定的关系向量索引。返回请求的近似最近邻关系数量及其相似性得分，按得分排序。在 5.18 中引入

设置节点矢量属性。

db.create.setNodeVectorProperty

使用给定的向量以比直接使用更节省空间的方式更新给定的节点属性SET。替换db.create.setVectorProperty。测试版 在 5.13 中引入

设置节点矢量属性。

db.create.setVectorProperty

取而代之db.create.setNodeVectorProperty。已弃用 测试版

设置关系向量属性。

db.create.setRelationshipVectorProperty

使用给定的向量以比直接使用更节省空间的方式更新给定的关系属性SET。测试版 在 5.18 中引入

限制和已知问题

从 Neo4j 5.13 开始，向量索引不再是测试版功能。但是，它仍然存在一些限制和已知问题。

本文标签： 向量索引Neo4j

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