PostgreSQL pgvector向量数据库

原创 已于 2025-12-17 15:21:29 修改 · 645 阅读 · 4 ·
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#数据库 #postgresql

于 2025-12-17 15:00:00 首次发布

一、向量数据库介绍

  •   核心功能
  • ‌向量存储‌：直接在 PostgreSQL 表中存储高维向量（如文本嵌入、图像特征），无需单独部署向量数据库。
  • ‌距离计算‌：支持 L2（欧氏距离）、余弦相似度（Cosine）和内积（Inner Product）三种度量方法。
  • ‌索引优化‌：内置 HNSW（Hierarchical Navigable Small World）和 IVFFlat（Inverted File with Flat Clustering）索引算法，支持近似最近邻搜索（ANN）。
  • ‌SQL兼容性‌：向量列可与普通列（如文本、数字）结合进行 JOIN、过滤和排序操作。
  • 应用特点如下：
  • ‌推荐系统‌：基于用户行为向量推荐相关内容。
  • ‌自然语言处理‌：语义相似性搜索（如文本匹配、问答系统）。
  • ‌计算机视觉‌：图像特征匹配（如人脸识别、相似图片检索）。
  • ‌多模态搜索‌：融合文本和图像向量进行跨模态匹配。

二、向量数据库使用

       1、创建一个数据库

create database spring_ai_vector
    with owner "High" tablespace pg_default;

comment on database spring_ai_vector is 'pg测试数据库';

     2、启用vector

CREATE EXTENSION vector;
SELECT extname, extversion FROM pg_extension;

     3、创建向量表

CREATE TABLE documents (
                           id SERIAL PRIMARY KEY,
                           content TEXT NOT NULL,
                           embedding vector(384) NOT NULL,  -- 384维向量
                           metadata JSONB
);

   4、添加索引

CREATE INDEX ON documents USING ivfflat (embedding);

  5、写入数据

  通过Python脚本生成384维度浮点数据

import numpy as np

def generate_vector(dim=384):
    return np.random.rand(dim).tolist()

if __name__ == "__main__":
    vector = generate_vector()
    print(vector)

INSERT INTO documents (content, embedding)
VALUES
    ('Document 1', '[0.3989308168826504, 0.5882632238251593, 0.474965990432864, 0.3970752257518608, 0.1167664011515791, 0.5772383216675584, 0.5645317882009054, 0.9188317817623228, 0.7138018954214925, 0.20597514874228962, 0.018491876552327646, 0.6790373805101362, 0.3101398672737342, 0.11354703468252592, 0.3473799360080342, 0.7729658634307738, 0.5841288734032305, 0.16905625021503456, 0.03597542453472791, 0.40728590562985856, 0.8001182200527573, 0.7901849901008005, 0.8766096770673742, 0.07332438651927498, 0.8030594391366687, 0.2864598972883755, 0.4265815634862693, 0.6466859534592285, 0.36696764693736894, 0.26142917371933583, 0.05928538661302041, 0.7811831475625653, 0.02199247142841343, 0.14122590704840599, 0.33681168059134325, 0.3548316930339972, 0.5771745484754328, 0.995531235087264, 0.3200024792321535, 0.8574993715504815, 0.25924477704508464, 0.9850055594309013, 0.09075178889015856, 0.10836244807603446, 0.5627960665925931, 0.32353005064148455, 0.928093992398043, 0.9369495869495343, 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'),
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');

6、查询使用

pgvector 支持三种主要距离度量：

  • ‌欧氏距离（L2）‌：使用 <-> 运算符（适用于未归一化向量）
  • ‌余弦相似度‌：使用 <=> 运算符（归一化向量的内积，范围[-1, 1]）
  • ‌内积（IP）‌：使用 <#> 运算符（已归一化向量）

欧氏距离（Euclidean Distance）是n维空间中两点之间的直线距离，其通用计算公式为：
对于点 A(a_1, a_2, \dots, a_n)A(a1​,a2​,…,an​) 和点 B(b_1, b_2, \dots, b_n)B(b1​,b2​,…,bn​)，距离 dd 定义为：
d = \sqrt{\sum_{i=1}^{n} (a_i - b_i)^2}d=∑i=1n​(ai​−bi​)2​
其中 nn 为空间维度（如384维向量中 n=384n=384），a_iai​ 和 b_ibi​ 分别为两点的坐标分量。

在PostgreSQL的pgvector扩展中，欧氏距离可通过 <-> 运算符直接计算

-- 余弦相似度查询（值越接近0越相似）
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0.04097587939504088, 0.07709259638046528, 0.8878916611916171, 0.6345130122236856, 0.7813392621564572, 0.6391556357594126, 0.02970454641370701, 0.8994071388452608, 0.2508105753340226, 0.14023840733108706, 0.9110515025746289, 0.892779911271669, 0.7798998775650049, 0.18604182715111472, 0.6150202768113533, 0.7029039132058537, 0.7106688212542625, 0.7296303709117612, 0.3047370953091991, 0.005764016126088123, 0.9334362752238586, 0.4131224207259482, 0.2536726194180303, 0.6219217915961684, 0.2101932808989765, 0.8187659220778744, 0.6665349091185734, 0.8548109275525102, 0.06991210279867555, 0.5722664263207784, 0.29672389301447444, 0.7723852066303597, 0.9927238913351595, 0.5915746852554157, 0.9524673104034163, 0.07187765839243032, 0.5750138623268215, 0.7769213211226643, 0.2434759162924589, 0.19830442970003703, 0.2233452572963468, 0.07512783574370818, 0.9973955437433416, 0.8320674052152764, 0.7839705626248945, 0.975996308845479, 0.5743339827551721, 0.7316928762201251, 0.760383630733506, 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' AS similarity
FROM documents
ORDER BY similarity
LIMIT 5;

欧氏距离计算

SELECT id, content, embedding <-> '[0.3989308168826504, 0.5882632238251593, 0.474965990432864, 0.3970752257518608, 0.1167664011515791, 0.5772383216675584, 0.5645317882009054, 0.9188317817623228, 0.7138018954214925, 0.20597514874228962, 0.018491876552327646, 0.6790373805101362, 0.3101398672737342, 0.11354703468252592, 0.3473799360080342, 0.7729658634307738, 0.5841288734032305, 0.16905625021503456, 0.03597542453472791, 0.40728590562985856, 0.8001182200527573, 0.7901849901008005, 0.8766096770673742, 0.07332438651927498, 0.8030594391366687, 0.2864598972883755, 0.4265815634862693, 0.6466859534592285, 0.36696764693736894, 0.26142917371933583, 0.05928538661302041, 0.7811831475625653, 0.02199247142841343, 0.14122590704840599, 0.33681168059134325, 0.3548316930339972, 0.5771745484754328, 0.995531235087264, 0.3200024792321535, 0.8574993715504815, 0.25924477704508464, 0.9850055594309013, 0.09075178889015856, 0.10836244807603446, 0.5627960665925931, 0.32353005064148455, 0.928093992398043, 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' AS similarity
FROM documents
ORDER BY similarity
LIMIT 5;

三、向量相似度计算

  • 欧氏距离：欧氏距离计算会遍历两个向量的每个维度，计算平方和，再取平方根。

float euclidean_distance(const float *a, const float *b, int length) {
    float sum = 0.0;
    for (int i = 0; i < length; i++) {
        float diff = a[i] - b[i];
        sum += diff * diff;
    }
    return sqrt(sum);
}
  • 余弦相似度：余弦相似度计算两个向量的点积，并归一化向量以计算余弦角度。

float cosine_similarity(const float *a, const float *b, int length) {
    float dot_product = 0.0;
    float norm_a = 0.0;
    float norm_b = 0.0;
    for (int i = 0; i < length; i++) {
        dot_product += a[i] * b[i];
        norm_a += a[i] * a[i];
        norm_b += b[i] * b[i];
    }
    return dot_product / (sqrt(norm_a) * sqrt(norm_b));
}
  • 内积：内积计算两个向量对应维度的乘积之和。

float dot_product(const float *a, const float *b, int length) {
    float result = 0.0;
    for (int i = 0; i < length; i++) {
        result += a[i] * b[i];
    }
    return result;
}
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在现代的机器学习和人工智能应用中，向量相似度检索是一个非常重要的技术，尤其是在文本、图像或其他类型的嵌入向量的操作中。本文将介绍如何在 PostgreSQL 中安装pgvector插件，用于存储和检索向量数据，并展示如何通过 Python 脚本向数据库插入向量并执行相似度查询。
【PGCCC】PostgreSQL 作为向量数据库：入门和扩展
PGCCC的博客
12-26 1923
这篇文章介绍了如何使用 PostgreSQL 作为向量数据库构建生成式 AI 应用程序。从使用 pgvector 扩展启动 PostgreSQL，加载 Airbnb 数据集，到查找相关房源，还提到了扩展性能和可扩展性的方法，如使用专用索引和分布式版本如 YugabyteDB。PostgreSQL 拥有丰富的扩展和解决方案生态系统，使我们能够将该数据库用于通用人工智能应用程序。本指南将引导您完成使用 PostgreSQL 作为向量数据库构建生成式 AI 应用程序所需的步骤。
PostgreSQL向量检索：pgvector入门指南
智的博客
09-29 1216
摘要：pgvector是PostgreSQL的开源扩展，用于高效存储和检索高维向量数据。支持欧氏距离、内积和余弦相似度计算，提供精确和近似最近邻搜索（IVFFlat/HNSW）。其优势在于与PostgreSQL原生集成，无需额外维护向量数据库，支持事务、权限控制和SQL混合查询。安装简单，通过CREATE EXTENSION vector启用，可创建固定维度的向量列。基本操作包括向量插入、最近邻查询（使用<->、<#>等运算符）和Python集成示例。适用于中小规模向量存储，适用于文
PostgreSQL作为向量数据库
qq_22201881的博客
06-13 1376
当设置hnsw.ef_search = 1000时，SQL 1 检索到28条数据，bus类型数量为17，查询时间16.8ms相较于 SQL 1 使用 hnsw.ef_search = 1000 与默认 hnsw.ef_search = 40 查询参数的检索对比，扩大 hnsw.ef_search 参数后检索结果召回率显著提高，但是检索时间有所增加，执行计划没有改变。随着时间的推移，这可能会导致在创建索引时建立的初始聚簇不再准确地代表数据的情况 ，解决此问题的唯一方法就是定期重建索引。
17-7 向量数据库之野望7 - PostgreSQL 和pgvector
ms44的专栏
07-16 1204
Pgvector 是 PostgreSQL 的一个扩展，允许它高效地存储和查询矢量数据。它引入了一种新的数据类型，vector旨在保存高维数据。此扩展使 PostgreSQL 能够执行矢量相似性搜索，使其适用于涉及机器学习模型、NLP 等的应用程序。
PostgreSQL pgvector：如何利用向量数据库提升搜索效率和精度
人工智能全栈工程师 | 智能算法研发与实战落地​专注于人工智能算法研究、代码实现与工程落地，提供从模型设计到部署应用的全流程解决方案。
12-10 2697
我们介绍了 pgvector，这是一个功能强大的 PostgreSQL 扩展，支持矢量数据的存储和搜索，并提供了一个易于访问的矢量数据库解决方案。像Chroma-DB这样的向量数据库能够满足这一需求，因为它们提供了经过优化的嵌入式存储和查询功能，并且具备典型数据库所不具备的独立向量索引特性。通过存储数据向量并使用向量之间的相似度度量，向量数据库可以实现高效、准确的数据搜索和分析。请注意，根据您的数据和用例，运算符的选择可能会对搜索结果的质量以及最终应用程序的有效性产生重大影响。
从一次增删改操作开始：彻底理解 MySQL Buffer Pool 的地位与作用
soft2001525的博客
12-11 2629
它是 InnoDB 的核心内存引擎，是所有数据读写的唯一入口，是数据库性能与安全性的关键支撑。理解 Buffer Pool 的逻辑，你就理解了 MySQL 的一半。redo logundo log（事务回滚）MVCC脏页、刷盘策略自适应哈希索引B+树索引加载机制查询优化都与 Buffer Pool 有直接关系。如果你想系统搞懂 MySQL，从 Buffer Pool 开始是最佳路径。如果本篇文章对你有帮助，欢迎：✏️ 评论交流。
兰顿蚂蚁——CAD二次开发
最新发布
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【代码】兰顿蚂蚁——CAD二次开发。
Qt6.5.3 mingw64 Ninja编译oracle oci驱动
小蝈蝈的博客
12-12 867
2.将sdk和basic的内容复制到一起，basic中的instantclient_xx_x目录下的内容，sdk的instantclient_xx_xx目录下的内容。├── include/ ← 头文件（来自 sdk/include）到 自己的项目的运行路径下的**/plugins/sqldrivers/**└── sdk/ ← 原始目录（可保留，但非必需）3.将oci.dll生成mingw64支持的.a库。4.切换到Qt源码中的路径下，我的路径。
KingbaseES 面向应用程序的SQL开发：从原理到实战的深度探索
优快云博客专家，领域包括但不限于：AI、大数据、Python、架构师，有合作、课程、问题、疑惑请私信博主
12-14 1124
KingbaseES数据库SQL开发实践解析 摘要：本文深入探讨国产数据库KingbaseES在SQL开发中的核心实践，重点分析四大关键技术：1) SQL处理机制，包括游标管理和绑定变量优化；2) 正则表达式的高级应用与性能优化；3) 多类型索引的选择与优化策略；4) 事务控制与锁机制。通过具体代码示例，展示了KingbaseES在金融、政务等关键领域的高性能SQL开发能力，包括事务隔离级别设置、死锁检测等高级特性。文章为开发者提供了从基础语法到高级优化的完整SQL开发指南，助力企业实现高效可靠的数据库应用
基于Java+SpringBoot+Vue的美甲店管理系统【附源码+文档+部署视频+讲解)
小熊的博客
12-11 1707
基于SpringBoot的美甲店管理系统开发案例，涵盖用户、美甲师和管理员三大角色功能模块，采用Vue+SpringBoot+MySQL技术栈实现。系统包含服务预约、订单管理、耗材采购等全流程数字化功能，采用MVC设计模式和B/S架构。包含用户、美甲师、管理员三类角色的功能模块，该系统聚焦美甲店的日常运营场景，通过用户端浏览服务与套餐、美甲师端处理预约与评价、管理员端统筹人员、业务与资源的模式，实现美甲店从服务展示、预约调度到耗材管理的全流程数字化，提升门店运营效率与服务体验。
小白安装Redis
2201_75999403的博客
12-11 944
简单亲测无痛安装redis
《Python实战小课：爬虫工具场景——开启数据抓取之旅》导读
2501_93253814的博客
12-15 1181
本文介绍了Python爬虫技术在三大场景中的应用：行业资讯爬取、学术文献摘要获取和电商评价收集。针对行业资讯，详细解析了从网页请求到数据存储的全流程；在学术文献方面，重点阐述了如何构建搜索请求和提取关键信息；对于电商评价，则说明了数据定位和清洗方法。文章还探讨了爬虫优化策略及反爬机制应对方案，为数据获取工作提供了实用指南。通过系统学习这些技术，读者可以提升数据采集能力，为商业决策、学术研究和市场分析提供有力支持。
postgresql创建向量数据库
03-26
### 如何在 PostgreSQL 中创建和配置向量数据库 要在 PostgreSQL 中创建和配置向量数据库，可以利用 `pgvector` 插件来实现高效的向量存储和相似度查询。以下是具体的操作说明： #### 启动 PostgreSQL 并安装插件 首先，在本地环境中启动 PostgreSQL 终端，并连接到目标数据库实例。通过命令行执行以下操作以初始化环境： ```bash psql -U myuser -d mydb ``` 这一步用于登录到指定的 PostgreSQL 用户账户以及对应的数据库名称[^1]。 #### 创建数据库并启用 pgvector 扩展 一旦成功进入 PostgreSQL 命令行界面，可以通过 SQL 查询语句完成后续设置。运行如下指令以创建新的数据库（如果尚未存在），并激活 `pgvector` 插件： ```sql CREATE EXTENSION IF NOT EXISTS vector; ``` 上述代码片段会检查当前数据库是否已加载该扩展；如果没有，则自动添加它以便支持向量化运算功能。 #### 加载数据集与定义表结构 为了演示目的，假设我们正在处理类似于 Airbnb 房源的数据集合。可以根据实际需求设计适合存储向量信息的表格模型。例如下面的例子展示了如何建立一张包含房屋描述及其对应嵌入表示形式的新表： ```sql CREATE TABLE listings ( id BIGINT PRIMARY KEY, name TEXT, description TEXT, price NUMERIC, embedding VECTOR(1536) -- Assuming embeddings are 1536-dimensional vectors. ); ``` 这里特别注意字段 `embedding` 被声明为固定长度为 1536 的浮点数数组类型，代表每条记录所关联的文字内容经过编码转换后的数值表现形式[^2]。 #### 构建索引来优化查询效率 当面对大规模数据集时，单纯依靠线性扫描方式计算最近邻可能变得不可接受地缓慢。因此建议针对向量列构建专门化的索引机制提高检索速度。目前推荐采用 IVFFlat 或 HNSW 方法之一来进行近似最邻近距离估算： ```sql -- Using an index to speed up similarity searches CREATE INDEX ON listings USING ivfflat (embedding vector_l2_ops) WITH (lists = 100); -- Alternatively, you can use hnsw instead of ivfflat for potentially better performance on large datasets: -- CREATE INDEX ON listings USING hnsw (embedding vector_ip_ops); ``` 这些高级别的参数调整选项允许开发者灵活控制内存消耗同精确程度之间的权衡关系。 #### 实施基于余弦距离/欧几里得范式的匹配逻辑 最后阶段涉及编写自定义函数或者直接调用内置 API 来衡量任意两个输入样本间的相对接近程度。比如寻找给定文本字符串的最佳候选对象列表的过程大致遵循这样的模式： ```sql SELECT id, name, 1 - (description <-> 'a nice place by the water') AS score FROM listings ORDER BY score LIMIT 5; ``` 此表达式内部运用了 `<->` 运算符快速评估两组特征空间位置差异大小，并按照升序排列返回前五个结果项。 --- ### 总结 综上所述，借助于 PostgreSQL 提供的强大生态系统——特别是其第三方模块开发社区贡献出来的成果如 `pgvector` ——能够轻松搭建起一套完整的解决方案框架服务于现代机器学习应用场景下的海量多模态数据分析任务之中[^3]。
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