# SQL和向量联合查询

MyScale将SQL和向量的强大功能结合起来，用于处理AI数据工作负载。您可以使用SQL的全面语法库，在结构化数据和向量化的非结构化数据上执行复杂的传统SQL和向量查询，实现快速高效的数据分析操作。

# SQL + 向量：为什么重要？

传统SQL和向量联合查询的重要性在于它们能够将结构化数据和向量嵌入（数据）结合起来，以统一高维数据的复杂查询和分析。这种方法具有以下几个优点和功能：

处理更多数据类型和函数：MyScale可以存储和查询高维向量数据以及您能想到的任何元数据。这种无缝集成使用户能够设计和执行更复杂的查询和数据分析。使用AI模型和业务数据有助于决策，提供更全面的数据视角。
混合搜索：将语义搜索与传统的关键词匹配结合在一起，通过混合搜索克服了向量化文档中语义覆盖不足的挑战。这对于定位罕见的词语或用户有意搜索特定术语时特别有益。此外，混合搜索的基础是通过SQL和向量联合查询建立的。
统一的查询语言：SQL是一种广泛使用的数据库查询语言，许多开发人员和数据分析师都在使用。用户可以通过在单个查询中联接SQL和向量搜索，无需学习新的查询语言，无缝集成他们已经熟悉的语法。
高效的数据检索：向量化（非结构化）和结构化数据通常需要专门的向量索引和搜索算法才能实现高效和有效的检索。通过在单个查询中联接SQL和向量搜索，MyScale引擎可以执行与向量相关的搜索并优化查询执行计划，实现更快速、更高效的数据检索。

提示

要了解如何有效地检索数据的示例，请转到高级应用程序：查询设计。
简化数据工作流程：MyScale提供了一个统一的数据解决方案，从原生SQL到向量搜索查询。SQL用户可以通过向量搜索扩展他们的查询，而向量搜索用户可以将他们的元数据存储在结构良好的表中。

总之，传统SQL与向量查询结合可以处理和分析高维向量数据，扩展数据库应用领域，提供增强的数据分析能力。这对于许多学科非常重要，包括机器学习、数据科学、图像处理等。

提示

请参阅示例应用程序和高级应用程序，以全面了解传统SQL查询和向量搜索的强大性质。

# 使用SQL + 向量：基于ChatData的演示

ChatData是一个LLM聊天应用程序，与您的文档交互时提供了无与伦比的效率和准确性。强大的元数据过滤器和高级向量搜索功能简化了对领域数据的问题回答过程。

提示

使用ChatData，您可以无缝地与数百万份文档进行交互，包括学术论文，快速轻松地找到所需的内容。无论您是研究人员、学生还是知识爱好者，探索学术论文和研究文档从未像使用ChatData那样简单。使用ChatData发掘信息检索的真正潜力，发现知识的世界尽在指尖。

下面的部分描述了如何在基于ChatData的查询检索实现中创建一个MyScale演示，并演示如何执行SQL和向量联合查询。

# 创建数据库表

这个示例应用程序使用一个包含学术论文数据（以向量形式存储和检索）的文档库。执行以下SQL语句，在MyScale的SQL Workspace中创建用于文档库的关系表。

# `chatPDF`表

该表存储从文档库中提取的段落文本数据，包含以下字段：

id：段落ID
pdf（SHA256）：对应的PDF ID
content：段落内容
section：段落所属的部分
page：页码
vector：段落的特征向量

CREATE TABLE default.chatPDF (
    `id` String,
    `pdf` String,
    `content` String,
    `section` String,
    `page` Int32,
    `vector` Array(Float32),
    VECTOR INDEX vec_idx vector TYPE MSTG('metric_type=Cosine'),
    CONSTRAINT vec_len CHECK length(vector) = 512)
ENGINE = ReplacingMergeTree ORDER BY id SETTINGS index_granularity = 8192;

# `chatPDF_meta`表

该表存储与文档库的PDF文件相关联的元数据信息，包含以下字段：

pdf（SHA256）：PDF ID
title：PDF标题
authors：PDF作者
abstract：PDF摘要
pub_date：出版日期
doi：文档的DOI（文档对象标识符）- https://doi.org (opens new window)
publisher：文档的出版商
article_type：文章类型/类别 - {'arXiv', 'Finance'}
vector：段落摘要的特征向量

CREATE TABLE default.chatPDF_meta (
    `pdf` String,
    `title` String,
    `authors` Array(String),
    `abstract` String,
    `pub_date` Nullable(Date32),
    `doi` String,
    `publisher` String,
    `article_type` String,
    `vector` Array(Float32),
    VECTOR INDEX vec_idx vector TYPE MSTG('metric_type=Cosine'),
    CONSTRAINT vec_len CHECK length(vector) = 512)
ENGINE = ReplacingMergeTree ORDER BY pdf SETTINGS index_granularity = 8192;

# `chatPDF_ref`表

该表存储文档库的PDF文件中引用的参考文献信息，包含以下字段：

pdf（SHA256）：引用的PDF ID
title：引用文章的标题
authors：引用文章的作者
journal：引用文章的摘要
year：引用文章的出版年份

CREATE TABLE default.chatPDF_ref (
    `title` String,
    `pdf` String,
    `journal` String,
    `authors` Array(String),
    `year` Nullable(Date32))
ENGINE = ReplacingMergeTree ORDER BY title SETTINGS index_granularity = 8192;

# `chatPDF_user`表

该表存储文档库中所有PDF文档的用户访问权限，包含以下字段：

id：ACL（访问控制列表）ID
user：ACL用户
pdf：用户可以访问的ACL PDF文档代码

CREATE TABLE default.chatPDF_user (
    `id` String, 
    `user` String, 
    `pdf` String) 
ENGINE = ReplacingMergeTree ORDER BY id SETTINGS index_granularity = 8192;

提示

如果用户对多个PDF文档拥有访问权限，则在chatPDF_user表中将有多行记录与该用户关联。

# 执行SQL和向量联合查询

使用这些数据库表，我们将演示如何高效执行满足检索要求的SQL和向量联合查询。

# 案例1：从一组PDF文件中检索相关论文

此场景旨在从chatPDF表中检索满足以下条件的PDF和它们的标题：

user为"default"或"test"
authors包含类似"Cedar"的姓名
根据查询向量到语义相似性的距离确定顺序

要检索满足这些条件的数据，请执行以下SQL语句：

SELECT content, section, page, title, pdf
FROM chatPDF
JOIN chatPDF_meta ON chatPDF.pdf = chatPDF_meta.pdf
WHERE pdf IN (
    SELECT pdf FROM chatPDF_user
    JOIN chatPDF_meta ON chatPDF_user.pdf = chatPDF_meta.pdf
    WHERE user IN ('default', 'test') AND match(arrayStringConcat(authors, ';'), '(?i)(;Ceder\s|\sCeder;)')
)
GROUP BY content, section, page, d, title, pdf
ORDER BY distance(chatPDF.vector, 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AS d
LIMIT 5;

# 案例2：搜索与提供的嵌入向量相关的部分

如下所示，此场景中的SQL语句使用嵌入向量搜索与以下条件相符的部分：

这些部分必须与查询嵌入向量相关
user为"default"或"test"
查询结果根据拆分文档的关键词重新排序，结合向量和关键词的力量进行更准确的检索。

SELECT content, section, page, temp_t.title AS title, temp_t.pdf AS pdf 
FROM (
    SELECT content, section, page, title, pdf
    FROM chatPDF 
    JOIN chatPDF_meta ON chatPDF_meta.pdf = chatPDF.pdf 
    WHERE pdf IN (
        SELECT pdf FROM chatPDF_user
        JOIN chatPDF_meta ON chatPDF_user.pdf = chatPDF_meta.pdf
        WHERE user IN ('test', 'default')
        )
    GROUP BY content, section, page, d, title, pdf
    ORDER BY distance(chatPDF.vector, 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AS d
    LIMIT 20
) temp_t
JOIN chatPDF_meta ON temp_t.pdf = chatPDF_meta.pdf
ORDER BY log(1 + countMatches(arrayStringConcat([abstract, content, title], ' '), '(?i)(solid-state|electrolyte)')+1) AS d DESC
LIMIT 5;

# 注意事项

在使用MyScale进行SQL和向量联合查询时，请注意以下几点：

您可以在使用向量索引的子查询中使用distance()...ORDER BY...LIMIT查询模式。当MyScale检测到这种模式并且子查询涉及的表具有带有向量列的向量索引时，它将使用向量索引加速查询。在ClickHouse中，ORDER BY子句不仅可以出现在最外层查询中。
MyScale是一种列式数据库，因此查询结果应该只包含必要的列。非必要的列不应出现在SELECT子句中，应避免使用SELECT *以提高查询速度。