向量索引

向量索引通过将节点或属性表示为多维空间中的向量，从而实现相似性搜索和复杂的分析查询。

以下资源提供了在 Neo4j 中使用大语言模型（LLM）和向量索引的实践教程：

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

示例图

本页面中的示例使用 Neo4j 电影推荐数据集，重点关注 Movie 节点的 plot（情节）和 embedding（嵌入）属性。embedding 属性由 plot 和 title 属性组合而成的 1536 维向量嵌入组成。

Graph example connecting movie to person nodes via acted in and directed relationships

该图谱包含 28863 个节点和 332522 条关系。

要重新创建该图谱，请下载此 dump 文件并将其导入到空的 Neo4j 数据库中。Dump 文件既可以导入 Aura 实例，也可以导入 本地（on-prem） 实例。

用于加载数据集的 dump 文件包含由 OpenAI 使用 text-embedding-ada-002 模型生成的嵌入。

Neo4j 中的向量与嵌入

向量索引允许您查询来自大型数据集的向量嵌入。嵌入是数据对象（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一个高维向量，其中每个维度代表单词含义的某个特定方面。

特定数据对象的嵌入可以通过专有（如 Vertex AI 或 OpenAI）和开源（如 sentence-transformers）嵌入生成器创建，这些生成器可以产生 256、768、1536 和 3072 等维度的向量嵌入。

向量嵌入作为节点或关系上的 LIST<INTEGER | FLOAT> 属性存储。从 Neo4j 2025.10 版本开始，它们也可以使用 Cypher® 25 更高效地存储为 VECTOR 属性。

向量索引在 Neo4j 企业版和社区版中均可用。社区版可以对存储为 LIST<INTEGER | FLOAT> 属性的嵌入进行索引。将嵌入存储为原生的 VECTOR 属性需要使用 块格式（block format），这在企业版和 Aura 中可用。

有关如何生成嵌入并将其存储为属性的信息，请参阅：

例如，电影《教父》(The Godfather) 拥有以下 plot：“一个有组织犯罪王朝年迈的家长将他秘密帝国的控制权移交给他不情愿的儿子。”这是它 1536 维的 embedding 属性，其中 LIST 或 VECTOR 中的每个坐标都代表了情节含义的特定方面。

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语义相似的单词通常由在该向量空间中彼此接近的向量表示。这允许加法和减法等数学运算具有语义含义。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嵌入属性之间的相似性，来检索节点或关系的邻域。

创建向量索引

使用 CREATE VECTOR INDEX [index_name] 命令创建向量索引。建议在创建时为索引命名。如果未指定名称，将分配一个随机名称。索引名称也可以作为参数给出：CREATE VECTOR INDEX $name …​。

各 Neo4j 版本的向量索引功能
Neo4j 版本 功能

5.13 (正式发布)

支持节点或关系的单标签、单属性索引，以及单关系类型、单属性索引。向量存储为 LIST<INTEGER | FLOAT> 属性。

2025.10

增加对 VECTOR 值属性的支持，并根据索引维度和配置的向量相似度函数进行验证。

2026.01

增加对多标签和多关系类型向量索引的支持，并使用额外属性进行过滤。这些属性稍后可以在 SEARCH …​ WHERE 中用于过滤，但它们不能是向量属性。每个节点或关系只能存储一个向量属性的向量索引。
索引名称在所有索引和约束中必须是唯一的。
新创建的索引不会立即生效，而是在后台创建。
创建索引需要 CREATE INDEX 权限。
为 Movie 节点的 embedding 属性创建向量索引
CREATE VECTOR INDEX moviePlots IF NOT EXISTS (1)
FOR (m:Movie)
ON m.embedding
OPTIONS { indexConfig: {
 `vector.dimensions`: 1536,
 `vector.similarity_function`: 'cosine'
}} (2)
1 CREATE VECTOR INDEX 命令可选支持幂等性。这意味着它的默认行为是：如果尝试两次创建相同的索引，则会抛出错误。使用 IF NOT EXISTS 时，如果已存在具有相同名称或模式的索引，则不会抛出错误且不会执行任何操作。如果存在同名约束，仍可能抛出错误。当未执行操作时，会返回一条信息性通知，显示阻塞创建的现有索引。
2 要了解有关可用配置设置的更多信息，请参阅 配置设置。在此示例中，向量维度被明确设置为 1536。建议在创建向量索引时提供维度，因为这可以确保仅索引该大小的向量，并使维度不匹配在查询时清晰地报错。向量相似度函数设置为 'cosine'，这是文本嵌入通常首选的相似度函数。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。

您还可以使用以下语法为具有特定类型关系的给定属性创建向量索引：

为关系类型在单个属性上创建向量索引
CREATE VECTOR INDEX name IF NOT EXISTS
FOR ()-[r:REL_TYPE]-() ON (r.embedding)
OPTIONS { indexConfig: {
 `vector.dimensions`: $dimension,
 `vector.similarity_function`: $similarityFunction
}}

与全文索引类似，向量索引可以为多个标签或关系类型创建。在这种情况下，只要节点具有向量属性，所有至少有一个标签与所列标签匹配的节点，或者所有类型与所列关系类型匹配的关系，都会被包含在内。

与多属性全文索引不同，向量属性是决定节点或关系是否包含在索引中的唯一属性。附加属性仅在类型为 INTEGER、FLOAT、STRING、BOOLEAN、DATE、ZONED DATETIME、LOCAL DATETIME、ZONED TIME、LOCAL TIME 或 DURATION 时才会包含在索引中。如果节点或关系没有附加属性，或者附加属性是其他类型，则索引将其视为 null。

您可以使用以下查询在多个标签上创建带有额外过滤属性的向量索引：

为多个标签并带有额外属性创建向量索引
CREATE VECTOR INDEX multiLabelAdditionalProperties IF NOT EXISTS
FOR (n:Movie|Actor)
ON n.embedding
WITH [n.title, n.plot, n.name, n.born]
OPTIONS { indexConfig: {
 `vector.dimensions`: 1536,
 `vector.similarity_function`: 'cosine'
}}

同样，对于具有多个关系类型和额外属性的关系，可以使用以下语法：

为多个关系类型和额外属性创建向量索引
CREATE VECTOR INDEX name IF NOT EXISTS
FOR ()-[r:REL_TYPE1|REL_TYPE2|REL_TYPE3]-()
ON (r.embedding)
WITH [r.property]
OPTIONS { indexConfig: {
 `vector.dimensions`: $dimension,
 `vector.similarity_function`: $similarityFunction
}}

配置设置

有关不同索引提供程序所接受值的更多信息，请参阅 向量索引提供程序兼容性。

vector.dimensions

要索引的向量维度。有关更多信息，请参阅 Neo4j 中的向量与嵌入。此设置可以省略，任何 LIST<INTEGER | FLOAT> 或（从 Neo4j 2025.10 开始）VECTOR 值都可以根据其维度被索引和查询，尽管只有相同维度的向量才能进行比较。 设置此值会增加额外检查，确保仅索引具有配置维度的向量，并使使用不同维度向量查询索引时返回错误。

建议在创建向量索引时提供维度。这样做可以避免索引中同时存在不同维度向量的混乱情况，同时仍只允许相同维度向量之间的比较。
接受的值

1 到 4096 之间的 INTEGER（包含边界值）。

默认值

无。

vector.similarity_function

用于评估两个向量相似度的相似度函数名称。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。

接受的值

STRING: 'cosine', 'euclidean'。

默认值

'cosine'.

vector.quantization.enabled

量化是一种减小向量表示大小的技术。启用量化可以加快搜索性能，但可能会略微降低准确性。建议在内存有限的机器上启用量化。

接受的值

BOOLEAN: true, false。

默认值

true

高级配置设置

vector.hnsw.m

M 参数控制 HNSW（分层可导航小世界）图中每个节点的最大连接数。增加此值可能会提高准确性，但会增加索引填充和更新时间，特别是对于高维向量。

接受的值

1 到 512 之间的 INTEGER（包含边界值）。

默认值

16

vector.hnsw.ef_construction

将向量插入 HNSW 图时跟踪的最近邻居数量。增加此值可提高索引质量，并可能带来更高的准确性（但收益递减），同时增加索引填充和更新时间。

接受的值

1 到 3200 之间的 INTEGER（包含边界值）。

默认值

100

查询向量索引

从 Neo4j 2026.01 开始，查询向量索引的首选方式是使用 Cypher SEARCH 子句。一种功能较弱的替代方案（也适用于早期 Neo4j 版本）是使用过程 db.index.vector.queryNodes 和 db.index.vector.queryRelationships。

索引在 state 为 POPULATING（在创建后立即出现）时无法使用。要检查向量索引的 state（是 ONLINE 可用，还是 POPULATING 正在构建中；populationPercent 列显示索引创建进度），请运行以下命令：SHOW VECTOR INDEXES。

SEARCH 子句返回节点的邻域，可选地包括它们各自的相似度得分，并按这些得分排序。得分在 0.0 和 1.0 之间，得分越接近 1.0，表示索引向量与查询向量越相似。

查找情节与《教父》最相似的五部电影
MATCH (m:Movie {title: 'Godfather, The'})
MATCH (movie: Movie)
  SEARCH movie IN (
    VECTOR INDEX moviePlots
    FOR m.embedding
    LIMIT 5
  ) SCORE AS score
RETURN movie.title AS title, movie.plot AS plot, score
结果
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 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 |
| "Goodfellas"               | "Henry Hill and his friends work their way up through the mob hierarchy."                                                                                                                                                | 0.9300689697265625 |
| "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 |
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

返回的所有电影的情节都围绕犯罪家族组织展开。score 结果按降序排列，最佳匹配结果排在第一位。由于索引是用该特定属性进行查询的，因此《教父》的相似度得分为 1.0。若要排除查询向量本身，请添加谓词 WHERE score < 1。

使用 db.index.vector.queryNodes() 过程查询节点向量索引

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

  • indexName 指的是要查询的向量索引的唯一名称。

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

  • query 指的是 LIST<INTEGER | FLOAT>，或从 Neo4j 2025.10 开始使用的 VECTOR，即要搜索其邻域的向量。

该过程返回节点邻域及其各自的相似度得分，并按这些得分排序。得分在 0 和 1 之间，得分越接近 1，表示索引向量与查询向量越相似。

查找情节与《教父》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
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 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 |
| "Goodfellas"               | "Henry Hill and his friends work their way up through the mob hierarchy."                                                                                                                                                | 0.9300689697265625 |
| "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 |
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

请注意，返回的所有电影的情节都围绕犯罪家族组织展开。score 结果按降序排列，最佳匹配结果排在第一位（在这种情况下，《教父》的相似度得分为 1.0，这是预料之中的，因为索引是用该特定属性查询的）。如果不想要查询向量本身，添加谓词 WHERE score < 1 可以移除相同的向量。

查询关系向量索引

要查询关系向量索引，请使用 Cypher SEARCH 子句或 db.index.vector.queryRelationships 过程，方法与上面展示的节点向量索引相同。

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

db.index.vector.queryRelationships() 的参数描述与 db.index.vector.queryNodes() 相同。

使用 向量函数 在不使用向量索引的情况下计算两个特定向量对之间的相似度得分。

性能建议

向量索引可以利用孵化中的 Java 向量 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您的 配置设置 中：

配置设置
server.jvm.additional=--add-modules=jdk.incubator.vector
虽然 Neo4j 5 系列至少需要 Java 17，但在 Neo4j 5.14-5.26 中，需要至少 Java 20 才能利用此性能提升。如果您使用的是 Neo4j 2025.01 或更高版本，则需要 Java 21。有关不同 Neo4j 版本支持的 Java 版本的更多信息，请参阅 操作手册 → 系统要求 → Java。

显示向量索引

要列出数据库中的所有向量索引，请使用 SHOW VECTOR INDEXES 命令。这与 其他索引的 SHOW 命令 相同，索引类型过滤为 VECTOR。

列出索引需要 SHOW INDEX 权限。
示例 1. 显示所有向量索引
显示具有默认返回列的向量索引
SHOW VECTOR INDEXES
结果
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 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        |
| 3  | "multiLabelAdditionalProperties" | "ONLINE" | 100.0             | "VECTOR" | "NODE"     | ["Movie", "Actor"] | ["embedding", "title", "plot", "name", "born"] | "vector-3.0"  | NULL             | 2024-05-07T09:19:09.225Z | 47        |
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

有关所有返回列的完整说明，请参阅 搜索性能索引 → 列出索引 → 列出索引的返回列。

示例 2. 显示具有完整或过滤后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 *。

显示所有返回列的所有向量索引
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.hnsw.m`: 16, `vector.quantization.enabled`: TRUE, `vector.similarity_function`: "COSINE", `vector.hnsw.ef_construction`: 100}} | ""             | "CREATE VECTOR INDEX `moviePlots` FOR (n:`Movie`) ON (n.`embedding`) OPTIONS {indexConfig: {`vector.dimensions`: 1536,`vector.hnsw.ef_construction`: 100,`vector.hnsw.m`: 16,`vector.quantization.enabled`: true,`vector.similarity_function`: 'COSINE'}}"                                                                            |
| 3  | "multiLabelAdditionalProperties" | "ONLINE" | 100.0             | "VECTOR" | "NODE"     | ["Movie", "Actor"] | ["embedding", "title", "plot", "name", "born"] | "vector-3.0"  | NULL             | 2024-05-07T09:19:09.225Z | 47        | 2024-05-07T08:26:19.072Z  | {indexConfig: {`vector.dimensions`: 1536, `vector.hnsw.m`: 16, `vector.quantization.enabled`: TRUE, `vector.similarity_function`: "COSINE", `vector.hnsw.ef_construction`: 100}} | ""             | "CREATE VECTOR INDEX `multiLabelAdditionalProperties` FOR (n:`Movie`|`Actor`) ON (n.`embedding`) WITH [n.`title`, n.`plot`, n.`name`, n.`born`] OPTIONS {indexConfig: {`vector.dimensions`: 1536,`vector.hnsw.ef_construction`: 100,`vector.hnsw.m`: 16,`vector.quantization.enabled`: true,`vector.similarity_function`: 'COSINE'}}" |
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

要仅返回特定详细信息，请在 YIELD 子句后指定所需的列名。

显示具有过滤返回列的所有向量索引
SHOW VECTOR INDEXES YIELD name, type, entityType, labelsOrTypes, properties
结果
+--------------------------------------------------------------------------------------------------------------------------------+
| name                             | type     | entityType | labelsOrTypes      | properties                                     |
+--------------------------------------------------------------------------------------------------------------------------------+
| "moviePlots"                     | "VECTOR" | "NODE"     | ["Movie"]          | ["embedding"]                                  |
| "multiLabelAdditionalProperties" | "VECTOR" | "NODE"     | ["Movie", "Actor"] | ["embedding", "title", "plot", "name", "born"] |
+--------------------------------------------------------------------------------------------------------------------------------+

删除向量索引

删除向量索引的方法与 删除其他索引的命令 相同，即 DROP INDEX。可以删除在与当前版本不同的 Cypher 版本中创建的向量索引。例如，尽管多个标签或附加属性是在 Cypher 25 中添加的，但此类索引仍可以使用 Cypher 5 进行删除。

索引名称在删除索引时也可以作为参数给出：DROP INDEX $name。

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

向量索引提供程序兼容性

无法在 Cypher 25 中创建索引时指定索引提供程序。相反，Neo4j 会选择性能最优的提供程序（目前向量索引为 vector-2.0）。之前创建的 vector-1.0 索引将继续运行。

了解更多关于向量索引提供程序的差异
已支持 vector-1.0 vector-2.0

索引模式

节点单标签、单属性索引。

 

不支持关系。

节点单标签、单属性索引。

 

关系单类型、单属性索引。

索引属性值类型

LIST<FLOAT>

LIST<INTEGER | FLOAT>

索引向量维度

1 到 2048 之间的 INTEGER （包含边界值）。

1 到 4096 之间的 INTEGER （包含边界值）。

索引向量量化和 HNSW 设置

不支持。

已支持。

余弦相似度向量有效性

所有向量分量均可在 IEEE 754 单精度中有限表示。

 

其 l2 范数非零，且可在 IEEE 754 单精度中有限表示。

所有向量分量均可在 IEEE 754 双精度中有限表示。

 

其 l2 范数非零，且可在 IEEE 754 双精度中有限表示。

 

每个向量分量与其 l2 范数的比值可在 IEEE 754 单精度中有限表示。

余弦和欧几里得相似度函数

相似度函数的选择会影响哪些索引向量被视为相似，以及哪些向量是有效的。向量本身的语义含义可能决定了选择哪种相似度函数。请参考您正在使用的特定向量嵌入模型的文档，因为它可能建议优先选择某些相似度函数。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。

相似度函数
名称 大小写不敏感参数 核心相似度特征

余弦 (Cosine)

"cosine"

角度

欧几里得 (Euclidean)

"euclidean"

距离

对于 l2 归一化向量（单位向量），余弦和欧几里得相似度函数产生相同的相似度顺序。

了解更多关于余弦相似度函数

当两个向量之间的角度决定了它们的相似程度时，使用余弦相似度。

余弦向量索引的有效向量是指：

  • 所有向量分量均可在 IEEE 754 双精度中有限表示。[2]

  • 其 l2 范数非零，且可在 IEEE 754 双精度中有限表示。

  • 每个向量分量与其 l2 范数的比值可在 IEEE 754 单精度中有限表示。

余弦相似度在笛卡尔坐标中解释向量。该度量与两个向量之间的角度有关。然而，角度可以用许多单位、符号约定和周期来描述。这个角度的三角余弦既不受上述角度约定的影响，也是有界的。余弦相似度将三角余弦重新绑定。

The cosine of vector v and vector u is defined as half of the quanity 1 plus the scalar product of v hat u hat, which equals half of the quantity 1 plus the scalar product of vector v vector u over the product of the l2-norm of vector v and the l2 norm ov vector u, which exists in the bounded set of real numbers between 0 inclusive and 1 inclusive.

在上述方程中，三角余弦由两个单位向量的标量积给出。

了解更多关于欧几里得相似度函数

当两个向量之间的距离决定了它们的相似程度时，欧几里得相似度很有用。

欧几里得向量索引的有效向量是指所有向量分量均可在 IEEE 754 单精度中有限表示。

欧几里得在笛卡尔坐标中解释向量。该度量与欧几里得距离有关，即两个点相距多远。然而，该距离是无界的，作为相似度得分不太有用。欧几里得相似度对欧几里得距离的平方进行了界定。

The Euclidean of vector v and vector u is defined as 1 over the quantity 1 plus the square of the l2-norm of vector v subtract vector u, which exists in the bounded set of real numbers between 0 exclusive and 1 inclusive.

向量索引过程

用法 过程 描述

使用节点向量索引。

db.index.vector.queryNodes()

查询指定的节点向量索引。返回请求数量的近似最近邻节点及其相似度得分，并按得分排序。

从 Neo4j 2026.01 开始，查询节点向量索引的首选方式是使用 Cypher SEARCH 子句。

使用关系向量索引。

db.index.vector.queryRelationships()

查询指定的关系向量索引。返回请求数量的近似最近邻关系及其相似度得分，并按得分排序。

从 Neo4j 2026.01 开始，查询关系向量索引的首选方式是使用 Cypher SEARCH 子句。

设置节点向量属性。

db.create.setNodeVectorProperty()

以比直接使用 SET 更节省空间的方式更新给定节点的属性向量。

设置关系向量属性。

db.create.setRelationshipVectorProperty()

以比直接使用 SET 更节省空间的方式更新给定关系的属性向量。

局限性和已知问题

向量索引不再是测试版功能。但是，它仍然存在一些局限性：

  • 该查询是近似最近邻搜索。请求的 k 个最近邻居可能不是精确的 k 个最近邻，但会在同一个更广泛的邻域内。

  • 对于接近索引向量总数的请求最近邻数量 k，搜索返回的结果数可能少于 k。

  • 每个模式只能有一个向量索引。例如，您不能在同一个标签-属性键对上同时拥有一个 欧几里得 和一个 余弦 向量索引。

  • 同一事务中所做的更改对索引不可见。

向量索引也包含一些已知问题。下表列出了这些问题，如果已修复，还注明了修复的版本：

已知问题
已知问题 修复于

如果在 Neo4j 5.18 及更高版本中，数据库最后写入的版本早于 5.11，且旧版过程是新版本上执行的第一个写操作，则使用遗留过程 db.index.vector.createNodeIndex() 创建向量索引可能会失败并报错。在 Neo4j 5.20 中，该错误描述已更明确。

改用 CREATE VECTOR INDEX 命令可以避免此问题。如果必须使用该过程，在执行该过程之前，先在较新版本的二进制文件上对数据库执行任何其他写操作即可避免此问题。

来自 SHOW PROCEDURES 的过程签名将向量参数呈现为 ANY 类型，而不是语义正确的 VECTOR | LIST<INTEGER | FLOAT> 类型。

类型仍然会被强制执行为 VECTOR | LIST<INTEGER | FLOAT>。
1. Lucene 实现了一个分层可导航小世界 (HNSW) 图，以在向量字段上执行 k 近似最近邻 (k-ANN) 查询。有关更多信息，请参阅 使用分层可导航小世界图的高效且稳健的近似最近邻搜索 — Yury A. Malkov 和 Dmitry A. Yashunin
2. IEEE 浮点算术标准