小试牛刀｜试用 DB-GPT x OceanBase 构建自给自足的 Chat Data 应用 - 大模型知识库|大模型训练|开箱即用的企业大模型应用平台|智能体开发|53AI

DB-GPT 是一款 “AI 原生数据应用开发框架”，目的是构建大模型领域的基础设施。能够通过开发多模型管理(SMMF)、Text2SQL 效果优化、RAG 框架以及优化、Multi-Agents 框架协作、AWEL（智能体工作流编排）等多种技术能力，让围绕数据库构建大模型应用更简单，更方便。

OceanBase 从 4.3.3 版本开始支持了向量数据类型的存储和检索，并且经过适配可以作为 DB-GPT 的可选向量数据库，支持 DB-GPT 对结构化数据和向量数据的存取需求。

之前做 AI Workshop的实验，都是 OceanBase for AI。这次实验在 OceanBase for AI的基础上，会进一步尝试 AI for OceanBase的效果，看看如何通过 AI来提升数据库的易用性。

Tips：如果大家想要线下直接体验，并和OceanBase 、DB-GPT技术专家面对面交流，欢迎大家报名 ? 12月21日，在成都举办的主题为《DB for AI：探索数据库智能运维与 AI 深度应用》的城市交流会。划到文末可扫码报名～

实验介绍

这一部分的内容，只是个人对这个实验的理解，欢迎大家来纠错。

这一小节标题中的 Chat Data 应用，含义是可以使用自然语言告知大模型生成对应的查询 SQL，不仅可以直接在数据库中执行该 SQL 获取结果，还能够将得到的查询结果数据进行可视化展示。相关的实验介绍及步骤详见：《OceanBase X DB-GPT 实验教程》（附录1）。

为了简单阐述我对这个实验的理解，以及为方便大家理解 OceanBase 数据库在这次实验中的作用，随手画了下面这张图（灵魂画手，大家轻喷）。

图中展示的这个 OceanBase 租户里有三类 database，分别为：

用于存储用户数据的库（图中的 User Data 库）；
用于存向量数据的库（图中的 Vector 库）；
以及其他库（图中的 Others 库）。

Chat Data 应用的服务对象是数据库，那这个被服务数据库自然就是 OceanBase，对应图中的 User Data 库。

Chat Data 应用需要对用户输入的自然语言，将数据库对象的元数据拿出来，进行相似性检查。所以也需要一个服务于 Chat Data 应用的向量数据库，那这个提供服务的数据库，自然也落到了 OceanBase 头上，对应图中的 Vector 库。

也就是说，这次 OceanBase 联合 DB-GPT 的实验，不需要专门去另外搭建一个向量数据库，能够做到自产自销、自给自足。通过 DB-GPT，利用 OceanBase 的向量能力，服务 OceanBase 的用户数据。

上面这张图画的实在太宽了，大家可以切成两半，分开来看。图中 OceanBase 库的左侧，是生产向量的过程。

如上图所示，DB-GPT 在搭建 Chat Data 应用的过程中：

1. 首先需要创建一个 User Data 库的连接，在创建这个连接的时候，就会把连接中对应库（例如这个库的真名叫 dbgpt_test_db）中用户数据的元信息（表名、列名等）拿出来;

2. 然后把这些元信息转成向量的形式；

3. 最后存入 Vector 库中的一张叫做 dbgpt_test_db_profile 的表内。

DB-GPT每创建一个新的 User Data 库的连接，就会在 OceanBase 的 Vector 库内创建一张叫做 database_name_profile 的表，表中有一个 document 列，用于存储元数据的文本信息；还有一个 embedding 列，用于存储将 document 列转换成的 1024 维向量。

obclient [dbgpt_vec_db]> desc dbgpt_test_db_profile;+-----------+---------------+------+-----+---------+-------+| Field     | Type          | Null | Key | Default | Extra |+-----------+---------------+------+-----+---------+-------+| id        | varchar(4096) | NO   | PRI | NULL    |       || embedding | VECTOR(1024)  | YES  |     | NULL    |       || document  | longtext      | YES  |     | NULL    |       || metadata  | json          | YES  |     | NULL    |       |+-----------+---------------+------+-----+---------+-------+4 rows in set (0.003 sec)
obclient [dbgpt_vec_db]> select document from dbgpt_test_db_profile;+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+| document                                                                                                                                                                                                             |+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+                                                                                                                              || plant_and_animal_table(id, name, name_embedding)                                                                                                                                                                     || lineitem(l_orderkey, l_partkey, l_suppkey, l_linenumber, l_quantity, l_extendedprice, l_discount, l_tax, l_returnflag, l_linestatus, l_shipdate, l_commitdate, l_receiptdate, l_shipinstruct, l_shipmode, l_comment) || t1(c1, c2, c3, c4)                                                                                                                                                                                                   |+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+3 rows in set (0.003 sec)
obclient [dbgpt_vec_db]> select embedding from dbgpt_test_db_profile limit 1\G*************************** 1. row ***************************embedding: 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row in set (0.001 sec)

最上面那张图中 OceanBase 库的右侧，是和用户交互，然后通过大模型消费向量数据，产生答案的过程。

如上图所示，用户在和 Chat Data 应用交互的过程中：

步骤 1 - 3：首先会把用户的自然语言请求，通过模型转换为向量，并在 dbgpt_test_db_profile 表内查询相似度最高的向量。

步骤 4 - 5：大语言模型会基于 Vector 库返回的向量结果及对应的元数据文本，把自然语言转换为对应的 SQL，然后在 User Data 库中执行 SQL 和收集结果数据。还可以根据用户需求将结果数据生成适合的图表。

Chat Data 应用在网上看到的效果是这样，可以直接对根据自然语言对数据库进行查询，并生成适合展示结果数据的图表：

趟坑记录

这次实在是没遇到啥坑，为了保持和前两篇内容结构相仿，鸡蛋里面挑骨头，强行给 DB-GPT 和 OceanBase 提上几个小问题。

第一个问题是 OceanBase 为了支持单集群多实例，引入了一个租户（tenant）的概念，很多数据库并没有这个概念。所以大家如果看到需要填写类似于连接串的地方，只要求填写用户名，但没有要求填写租户名，那就把 user_name 填写成 user_name@tenant_name 的形式吧（后面在 DB-GPT 中创建连接串时也会遇到类似的问题，大家可以自适应克服一下）。

第二个问题是拉 DB-GPT 的镜像比较慢，不过大概率是我网络的原因。晚上九点拉的，十分钟才下载了百分之一二的样子，被逼无奈，只能先回家打游戏?，等到第二天上班的时候，就已经拉完了。公司周末网络神速，所以这个下载速度可能并不值得大家参考。大家如果用公司网络下载比较大的镜像，推荐选择周末或者夜深人静的时候。

如果大家是在 MacOS 上搞的，可能会有个操作系统兼容性问题的 WARNING，不过既然不是 ERROR，那就先忽略吧，反正用起来没啥问题。

第三个问题是 DB-GPT 中的数据源。如果有元数据更新，Chat Data 应用不会去自动感知并更新元数据，需要大家手动删掉数据源再重新连接一次。听说之前 DB-GPT 会把删除和重建的动作合并成了一个刷新的按钮，不知为何新版本被去掉了，好在有绕过的方法，影响不大。（DB-GPT 新版本即将优化该功能）

准备数据

这次实现如果只想做 Chat Data 应用的话，并不需要准备什么数据，随便在数据库里搞点儿测试数据就好了。我是搞了个 TPCH 1G 的测试集，然后导入了一张大表 lineitem 的数据。大家如果已经有了 OceanBase 的测试环境，直接拿测试环境的数据来用就行。

效果展示

我的测试集里没有那么多花里胡哨的东西，估计不会画出花花绿绿的图，大家自行以小见大、以陋见繁吧。

示例一

先让 Chat Data 写一条简单的 SQL，对 TPCH 测试集的数据进行查询，并生成可视化的图表，效果如下：

Chat Data 会自动拿着生成的 SQL 去数据库里执行，并返回查询结果。感觉都能直接当成执行 SQL 的客户端来用了……

生成图表这里并没有详细说明横纵坐标分别是什么，可能因为 SQL 比较简单，所以Chat Data 凭自己感觉生成的柱状图也是最优解。而且还可以直接在柱状图中选择为哪些结果数据展示出对应的柱子。

示例二

接下来稍微犯点儿坏，测试一下窗口函数。因为我自己完全想象不出来 Chat Data 能拿着窗口函数的计算结果，画出什么样的 chart 来。

只要自然语言的描述足够清晰，SQL 就能写的分毫不差，这里不再细表。

画图的结果比较出乎意料，Chat Data 可能是感知到了这条 SQL 是在故意为难它，直接就撂挑子不画了。和答疑机器人对不懂的问题一本正经地胡说八道不太一样，Chat Data 还是很懂得量力而行的（对于 AI 来说，有自知之明也许是件好事）。

如果对于其他适合可视化展示结果的 SQL，它就会一次性生成多种 chart，供用户选择。

示例三

最后发现数据库里自带了一张叫做 plant_and_animal_table 的表，应该是设计实验的同学专门为大家准备的，里面还有个向量类型列。

desc plant_and_animal_table;+----------------+--------------+------+-----+---------+----------------+| Field          | Type         | Null | Key | Default | Extra          |+----------------+--------------+------+-----+---------+----------------+| id             | int(11)      | NO   | PRI | NULL    | auto_increment || name           | text         | YES  |     | NULL    |                || name_embedding | VECTOR(1536) | YES  |     | NULL    |                |+----------------+--------------+------+-----+---------+----------------+3 rows in set (0.013 sec)

猜测图中把原本的 1536 维向量简化成了 2 维向量，不同维度的向量值，能够反映生物的某些属性。例如图中的 x 轴的值可能就和这个生物是动物还是植物比较相关。