AI 函数

函数支持

AI 配置相关参数

云数据库 SelectDB 版 通过资源机制集中管理 AI API 访问，保障密钥安全与权限可控。 现阶段可选择的参数如下：

厂商支持

目前直接支持的厂商有：OpenAI、Anthropic、Gemini、DeepSeek、Local、MoonShot、MiniMax、Zhipu、Qwen、Baichuan。

若有不在上列的厂商，但其 API 格式与 OpenAI/Anthropic/Gemini相同的， 在填入参数ai.provider_type时可直接选择三者中格式相同的厂商。 厂商选择只会影响 SelectDB 内部所构建的 API 的格式。

快速上手

1. 配置 AI 资源。

   例 1：

   CREATE RESOURCE 'openai_example'
   PROPERTIES (
       'type' = 'ai',
       'ai.provider_type' = 'openai',
       'ai.endpoint' = 'https://api.openai.com/v1/responses',
       'ai.model_name' = 'gpt-4.1',
       'ai.api_key' = 'xxxxx'
   );

   例 2：

   CREATE RESOURCE 'deepseek_example'
   PROPERTIES (
       'type'='ai',
       'ai.provider_type'='deepseek',
       'ai.endpoint'='https://api.deepseek.com/chat/completions',
       'ai.model_name' = 'deepseek-chat',
       'ai.api_key' = 'xxxxx'
   );

2. （可选）设置默认资源。

   SET default_ai_resource='ai_resource_name';

3. 执行 SQL 查询。

   case1:

   假设存在如下数据表，表中存储了与数据库相关的文档内容：

   CREATE TABLE doc_pool (
       id  BIGINT,
       c   TEXT
   ) DUPLICATE KEY(id)
   DISTRIBUTED BY HASH(id) BUCKETS 10
   PROPERTIES (
       "replication_num" = "1"
   );

   若需筛选与 SelectDB 相关性最高的 10 条记录，可采用如下查询：

   SELECT
       c,
       CAST(AI_GENERATE(CONCAT('Please score the relevance of the following document content to SelectDB, with a floating-point number from 0 to 10, output only the score. Document:', c)) AS DOUBLE) AS score
   FROM doc_pool ORDER BY score DESC LIMIT 10;

   该查询将利用 AI 生成每条文档内容与 SelectDB 的相关性评分，并按得分降序筛选前 10 条结果。

   +---------------------------------------------------------------------------------------------------------------+-------+
   | c                                                                                                             | score |
   +---------------------------------------------------------------------------------------------------------------+-------+
   | SelectDB is a lightning-fast MPP analytical database that supports sub-second multidimensional analytics. |   9.5 |
   | In SelectDB, materialized views can automatically route queries, saving significant compute resources.           |   9.2 |
   | SelectDB's vectorized execution engine boosts aggregation query performance by 5–10×.                            |   9.2 |
   | SelectDB Stream Load supports second-level real-time data ingestion.                                      |   9.2 |
   | SelectDB cost-based optimizer (CBO) generates better distributed execution plans.                                |   8.5 |
   | Enabling the SelectDB Pipeline execution engine noticeably improves CPU utilization.                             |   8.5 |
   | SelectDB supports Hive external tables for federated queries without moving data.                                |   8.5 |
   | SelectDB Light Schema Change lets you add or drop columns instantly.                                             |   8.5 |
   | SelectDB AUTO BUCKET automatically scales bucket count with data volume.                                         |   8.5 |
   | Using SelectDB inverted indexes enables second-level log searching.                                              |   8.5 |
   +---------------------------------------------------------------------------------------------------------------+-------+

   case2:

   以下表模拟在招聘时的候选人简历和职业要求

   CREATE TABLE candidate_profiles (
       candidate_id INT,
       name         VARCHAR(50),
       self_intro   VARCHAR(500)
   )
   DUPLICATE KEY(candidate_id)
   DISTRIBUTED BY HASH(candidate_id) BUCKETS 1
   PROPERTIES (
       "replication_num" = "1"
   );
   
   CREATE TABLE job_requirements (
       job_id   INT,
       title    VARCHAR(100),
       jd_text  VARCHAR(500)
   )
   DUPLICATE KEY(job_id)
   DISTRIBUTED BY HASH(job_id) BUCKETS 1
   PROPERTIES (
       "replication_num" = "1"
   );
   
   INSERT INTO candidate_profiles VALUES
   (1, 'Alice', 'I am a senior backend engineer with 7 years of experience in Java, Spring Cloud and high-concurrency systems.'),
   (2, 'Bob',   'Frontend developer focusing on React, TypeScript and performance optimization for e-commerce sites.'),
   (3, 'Cathy', 'Data scientist specializing in NLP, large language models and recommendation systems.');
   
   INSERT INTO job_requirements VALUES
   (101, 'Backend Engineer', 'Looking for a senior backend engineer with deep Java expertise and experience designing distributed systems.'),
   (102, 'ML Engineer',      'Seeking a data scientist or ML engineer familiar with NLP and large language models.');
   

   可以通过AI_FILTER把职业要求和候选人简介做语义匹配，筛选出合适的候选人

   SELECT
       c.candidate_id, c.name,
       j.job_id, j.title
   FROM candidate_profiles AS c
   JOIN job_requirements AS j
   WHERE AI_FILTER(CONCAT('Does the following candidate self-introduction match the job description?', 
                   'Job: ', j.jd_text, ' Candidate: ', c.self_intro));

   +--------------+-------+--------+------------------+
   | candidate_id | name  | job_id | title            |
   +--------------+-------+--------+------------------+
   |            3 | Cathy |    102 | ML Engineer      |
   |            1 | Alice |    101 | Backend Engineer |
   +--------------+-------+--------+------------------+

设计原理