总结内容

AlloyDB for PostgreSQL 中的 ai.summarize 函数使用 Gemini 等大语言模型 (LLM) 将输入文本浓缩并重组为最重要的要点。通过直接在 SQL 中运行摘要，您可以从大型数据集中提取关键发现，而无需手动审核；通过在提示中指定字词或句子限制来控制输出长度；以及批量异步处理行。

您可以使用 ai.summarize 将会议转写内容浓缩为决策和待办项，将技术文档总结为简要概览，或将客户支持日志和用户评价汇总为统一的问题摘要。

准备工作

在使用 ai.summarize 函数之前，请确保您满足以下要求。

启用扩展程序

确保您已安装最新版本的 google_ml_integration.enable_preview_ai_functions 扩展程序（1.6.1 版或更高版本），并启用了预览功能。

如需在 AlloyDB 中启用 google_ml_integration.enable_preview_ai_functions 标志，请使用 SET 命令。此标志用于控制对预览版 AI 函数（例如 ai.summarize）的访问权限。

  1. 确保您的 google_ml_integration extension 为 1.6.1 或更高版本。 您可以通过运行以下命令来检查版本：

    SELECT extversion FROM pg_extension WHERE extname = 'google_ml_integration';

    如果您需要升级到包含这些预览版函数的版本，请调用以下命令：

    CALL google_ml.upgrade_to_preview_version();

  2. 为当前会话或整个数据库启用该标志。如需为当前会话启用该标志，请执行以下命令：

    SET google_ml_integration.enable_preview_ai_functions = 'on';

    此更改不需要重启数据库。此标志的默认值为 off。

创建示例表

如需按照本文档中的摘要函数示例进行操作，请创建一个表，并使用以下电影评价填充该表。

CREATE TABLE movie_reviews (
    id INT PRIMARY KEY,
    movie_id INT,
    review TEXT
);

INSERT INTO movie_reviews (id, movie_id, review) VALUES
(1, 1, 'The Midnight Echo represents a masterclass in atmospheric tension. The cinematography captures the isolation of the frozen tundra perfectly while the lead actor delivers a superb performance. Although the pacing slows down significantly in the second act, the final twist remains unexpected. This is a truly remarkable experience.'),
(2, 1, 'Neon Velocity delivers high octane action but lacks the narrative depth required to make the stakes feel real. The visual effects are undeniably stunning, creating a vibrant cyberpunk world that pops off the screen. However, the dialogue feels cliched and the secondary characters are thin. This film is a shallow blockbuster.');

-- more rows are present in this table

总结单个字符串

如需处理单个文本块，请使用标量版本的 ai.summarize。

SELECT ai.summarize(
  input => 'TEXT_CONTENT',
  additional_instructions => 'INSTRUCTIONS', -- Optional
  model_id => 'MODEL_ID' -- Optional
);

运行以下示例查询，提供电影评价的简明摘要：

SELECT ai.summarize(review) FROM movie_reviews;

以下是示例输出：

 id |                         summary
----+-----------------------------------------------------------
  1 | "Atmospheric thriller with a haunting performance and shocking twist."
  2 | "Visually stunning cyberpunk action film lacks narrative depth."
...

批量总结

如需高效处理多条记录，请使用基于数组的版本。

SELECT ai.summarize(
  inputs => TEXT[],
  additional_instructions => 'INSTRUCTIONS', -- Optional
  batch_size => INT, -- Optional. The default value is 10.
  model_id => VARCHAR(100) -- Optional. The default value is gemini-3.5-flash-lite.
);

以下示例展示了如何使用基于数组的 ai.summarize 函数版本，在单次调用中高效处理多条文本记录。

WITH summarized_results AS (
SELECT
  ARRAY_AGG(id ORDER BY id) as ids,
  ai.summarize(
    inputs => array_agg(review ORDER BY id),
    additional_instructions =>
      'Please summarize this in max 10 words, review : ',
    batch_size => 15
  ) as summaries
FROM movie_reviews
),
correlated_results AS (
    SELECT ids[i] as ID, summaries[i] as summary
    FROM summarized_results,
    generate_series(1, array_length(ids, 1)) AS i
)
SELECT movie_reviews.id, correlated_results.summary as summary
FROM movie_reviews
JOIN correlated_results ON movie_reviews.id = correlated_results.id
ORDER BY movie_reviews.id DESC ;

以下是示例输出：

 id | summary
---+----------------------------------------------------------------------------
  1 | "Masterclass in tension, haunting performance with a twist."
  2 | "High octane action, stunning visuals, shallow blockbuster."
  3 | "Gentle, moving exploration of  emotional honesty."

使用游标进行汇总

您可以使用游标高效处理可能很大的评价集，将其馈送到 AI 函数中，然后逐个处理结果，并将其存储回数据库中。

以下示例展示了如何将游标与 ai.summarize 函数结合使用，以高效处理 movie_reviews 表中的行，为每条评价生成摘要，并将这些摘要存储在名为 review_summaries 的新表中。 这种基于游标的方法对于处理大型数据集非常有用，因为这些数据集可能太大，无法在单个批次中处理或一次性加载到内存中。

input_cursor 参数接受 REFCURSOR。这意味着您需要提供一个游标名称，该名称就像一个指向 SQL 查询结果的指针。然后，ai.summarize 函数会从此游标中提取数据，用作摘要的输入。

CREATE OR REPLACE FUNCTION ai.summarize(
  input_cursor REFCURSOR,
  additional_instructions TEXT DEFAULT NULL,
  batch_size INT DEFAULT NULL,
  model_id VARCHAR(100) DEFAULT NULL)
RETURNS REFCURSOR

以下示例会创建一个表 review_summaries（如果需要），然后遍历电影评价，使用 AI 函数为每条评价生成简明摘要，并将摘要与原始评价 ID 关联后存储在表中。

-- Create a table to store the results
CREATE TABLE IF NOT EXISTS review_summaries (
    review_id INT, 
    summary_text TEXT
);

DO $$
DECLARE
    -- Use descriptive cursor and variable names
    review_cursor   REFCURSOR;
    result_record   RECORD;
    cursor_response  REFCURSOR;
    id_array        INT[];
    idx             INT := 1;
BEGIN
    -- 1. Open cursor for the input text
    OPEN review_cursor FOR
        SELECT review AS input FROM movie_reviews ORDER BY id;

    -- 2. Call the AI summarize function
    cursor_response := ai.summarize(
        input_cursor            => review_cursor,
        additional_instructions => 'Please summarize the following review in max 10 words: '
    );

    -- 3. Map IDs into an array to maintain relational integrity during the loop
    SELECT ARRAY_AGG(id ORDER BY id) INTO id_array FROM movie_reviews;

    -- 4. Iterate through AI results and insert into the results table
    LOOP
        FETCH cursor_response INTO result_record;
        EXIT WHEN NOT FOUND;

        INSERT INTO review_summaries (review_id, summary_text)
        VALUES (id_array[idx], result_record.output);

        idx := idx + 1;
    END LOOP;

    -- 5. Clean up cursors
    CLOSE review_cursor;
    CLOSE cursor_response;
END;
$$;

-- Verify results
SELECT * FROM review_summaries;

以下是示例输出：

 review_id | summary_text
-----------+---------------------------------------------------------------------------
         1 | "Masterclass in atmospheric tension with haunting performance, devastating twist."
         2 | "High octane action, stunning visuals, but shallow blockbuster."
         3 | "Gentle, moving exploration of grief with poetic screenplay."

根据其他指令进行总结

以下 SQL 查询会传递额外的指令来指导摘要生成：

SELECT ai.agg_summarize(review, 'Please summarize this within 10 words') FROM movie_reviews GROUP BY movie_id;

使用特定模型进行总结

如需指定模型而不提供其他指令，您必须传递模型 ID 并将其明确转换为 VARCHAR，以免被误认为是指令：

SELECT ai.agg_summarize(review, 'gemini-3.5-flash-lite'::VARCHAR) FROM movie_reviews GROUP BY movie_id;

使用额外指令和特定模型进行总结

以下 SQL 查询会同时传递额外指令和特定模型 ID：

SELECT ai.agg_summarize(review, 'Please summarize this within 10 words', 'gemini-3.5-flash-lite') FROM movie_reviews GROUP BY movie_id;

跨行汇总

ai.agg_summarize 函数可对一列中的多行执行操作。 它会将值汇总到一个统一的提示中，以便为整个组生成一个摘要。

以下 SQL 查询使用 ai.agg_summarize 聚合函数，根据 movie_reviews 表中的所有评价，为每个 movie_id 生成一个统一的摘要。

SELECT ai.agg_summarize(review) FROM movie_reviews GROUP BY movie_id;

与单独汇总每一行的 ai.summarize 不同，ai.agg_summarize 会将多行中的文本合并为单个输入，从而为整组行生成一个合并摘要。

以下是示例输出：

agg_summarize
"The Midnight Echo is a masterclass in atmospheric tension, featuring haunting performances and stunning cinematography, though its pacing falters in the second act before a final twist. Neon Velocity offers stunning visual effects and high-octane action within a vibrant cyberpunk world, but suffers from shallow narrative depth, cliched dialogue, and underdeveloped characters. Garden of Whispers is a gentle, moving, and emotionally honest exploration of and life, characterized by poetic screenwriting and natural performances. Shadow Protocol ambitiously attempts to reinvent the spy thriller with a non-linear narrative and precise action, but ultimately confuses the audience and presents a derivative central mystery. The Last Alchemist uniquely blends historical drama with subtle fantasy, boasting meticulous production design and electric chemistry between its protagonists."

后续步骤