Postgresql17数据库中通过代码安装向量插件:PGVector0.8.1
pgvector 是一个为 PostgreSQL 数据库开发的开源扩展，它使得 PostgreSQL 能够原生支持向量（Vector）数据类型和向量相似性搜索，是构建 AI 应用（尤其是基于大语言模型 LLM 的应用）的关键基础设施。将向量与其余数据一起存储。支持：1.精确和近似最近邻搜索2.单精度、半精度、二进制和稀疏向量3.L2距离、内积、余弦距离、L1距离、汉明距离和雅克卡德距离4.具有Po
一、概述
pgvector 是一个为 PostgreSQL 数据库开发的开源扩展，它使得 PostgreSQL 能够原生支持向量（Vector）数据类型和向量相似性搜索，是构建 AI 应用（尤其是基于大语言模型 LLM 的应用）的关键基础设施。
将向量与其余数据一起存储。支持：
1.精确和近似最近邻搜索
2.单精度、半精度、二进制和稀疏向量
3.L2距离、内积、余弦距离、L1距离、汉明距离和雅克卡德距离
4.具有Postgres客户端的任何语言
二、安装pgvector
2.1下载工程
https://github.com/pgvector/pgvector
2.2进入docker容器里面
docker exec -it postgres /bin/bash
在容器里面安装下面三个组件。
apt-get update
apt-get install make
apt-get install gcc
apt install postgresql-server-dev-17
2.3docker容器中安装pgvector
# cd /pgvector
make
make install
2.4启用 PGVector
启用扩展（在每个要使用它的数据库中执行一次）
CREATE EXTENSION IF NOT EXISTS vector;
所有可用的扩展
SELECT * FROM pg_available_extensions order by name ;
查看当前数据库实例中已安装和启用的扩展
SELECT * FROM pg_extension;
三、PGVector 日常使用
3.1创建带向量字段的表
CREATE TABLE test_emb1(id bigserial PRIMARY KEY, info text,embedding vector(1024));
注：由于使用bge-m3嵌入模型，因为向量维度为1024，官网的测试例子都是3.
3.2 将“苹果、水果、梨、香蕉”等文本信息进行向量化并插入到表中。
INSERT INTO test_emb1(info,embedding)
VALUES ('梨','[-0.025978144258260727,
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3.3查询“红苹果”与哪个文本相似
select * from (
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0.04781698063015938]' AS distance,info FROM test_emb1 ) order by distance ;
可以看到“红苹果”与“苹果”最相似，“苹果”就是我们要找的文本内容。
四、距离函数说明
- <-> l2_distance 两个向量相减得到的新向量的长度
含义：两点之间的直线距离。
用途：最常用的相似性度量，适用于连续型向量（如 embeddings）。
注意：值越小，越相似。
- <#> vector_negative_inner_product 两个向量内积的负值
含义：向量内积的负值。
用途：
内积本身常用于衡量相似性（如推荐系统）。
取负值后，值越小表示越相似，符合“距离越小越相似”的惯例。
未归一化的余弦相似性（Cosine Similarity）本质上就是内积。
注意：与余弦距离不同，它受向量长度影响。
- <=> cosine_distance 两个向量夹角的cos值
含义：基于向量夹角的距离。衡量方向差异，忽略长度。
范围：0（完全相同方向）到 2（完全相反方向）。
用途：文本 embeddings、图像特征等方向比长度更重要的场景。
注意：值越小，方向越接近，越相似。
- <+> L1 distance 曼哈顿距离
含义：各维度绝对差之和。
用途：对异常值比 L2 更鲁棒，常用于稀疏向量或 L1 正则化场景。
注意：值越小，越相似。
- <~> - 汉明距离（Hamming Distance）
含义：两个等长二进制向量中，对应位不同的个数。
用途：二值化特征、指纹识别、错误检测。
注意：用于二进制向量（bit vectors）。
- <%> - Jaccard 距离（Jaccard Distance）
含义：基于集合交集与并集的比例。衡量两个集合的不相似性。
与二进制向量的关系：
二进制向量可视为集合（1 表示元素存在）。
Jaccard 距离适用于稀疏二进制向量（如文档的词袋模型）。
用途：文本相似性、推荐系统、集合比较。
|
符号 |
名称 |
适用向量类型 |
距离越小表示 |
|
<-> |
L2 距离 |
连续型向量 |
越相似 |
|
<#> |
负内积 |
连续型向量 |
越相似（方向+长度） |
|
<=> |
余弦距离 |
连续型向量 |
方向越接近 |
|
<+> |
L1 距离 |
连续型向量 |
越相似 |
|
<~> |
汉明距离 |
二进制向量 |
越相似 |
|
<%> |
Jaccard 距离 |
二进制/集合 |
越相似 |
4.1使用场景建议
文本 embeddings：<=>（余弦距离）最常用。
图像特征：<->（L2）或 <=>。
二值化特征（如 SimHash）：<~>（汉明距离）。
集合/稀疏特征（如用户行为）：<%>（Jaccard 距离）。
推荐系统内积：<#>（负内积）。
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