使用 Kernel Memory 和 MSSQL 快速建立 RAG 服務 - DB 研究

前言

在前一篇使用 Kernel Memory 和 MSSQL 快速建立 RAG 服務可以很快建立出 RAG 服務，並讓使用者進行問答。
接下來我們來看一下它在 SQLServer 中是如何處理

研究

ImportDocumentAsync

從 await kmClient.ImportDocumentAsync(docPath, index: tenantId, documentId: documentId);開始，
它會呼叫 Kernel Memory 的/upload API，
呼叫MemoryServerless.ImportDocumentAsync(因為OrchestrationType 設定成 InProcess)，
呼叫BaseOrchestrator.ImportDocumentAsync執行Pipeline的處理，
因為沒設定 Pipeline 自定的 Steps，所以會使用預設的 Steps，
預設的 Steps 為extract(讀取文件內容), partition(將文件內容切成多個 Chunks), gen_embeddings(將各 Chunks 轉成 Embedding) 及 save_records(將 Chunks 及 Embeddings 存到 MSSQL)

在save_records時，會做以下幾個步

1.建立基本 Tables
建立KMCollections及KMMemories(config 中 SqlServer 區段MemoryCollectionTableName及MemoryTableName的設定值)這 2 個 Table(SqlServerMemory.CreateTablesIfNotExistsAsync)。
KMCollections Table 只有一個id nvarchar(256)這個欄位，是記錄index的值。
KMMemories Table 會記錄文件 Chunks 的相關內容。

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IF OBJECT_ID(N'[dbo].[KMCollections]', N'U') IS NULL
    CREATE TABLE [dbo].[KMCollections]
    (   [id] NVARCHAR(256) NOT NULL,
        PRIMARY KEY ([id])
    );

IF OBJECT_ID(N'[dbo].[KMMemories]', N'U') IS NULL
    CREATE TABLE [dbo].[KMMemories]
    (   [id] UNIQUEIDENTIFIER NOT NULL,
        [key] NVARCHAR(256)  NOT NULL,
        [collection] NVARCHAR(256) NOT NULL,
        [payload] NVARCHAR(MAX),
        [tags] NVARCHAR(MAX),
        [embedding] NVARCHAR(MAX),
        PRIMARY KEY ([id]),
        FOREIGN KEY ([collection]) REFERENCES [dbo].[KMCollections]([id]) ON DELETE CASCADE,
        CONSTRAINT UK_KMMemories UNIQUE([collection], [key])
    );

2.新增index資料及建立index所需的 Tables(DefaultQueryProvider.PrepareCreateIndexQuery)
2.1.將index(gss)新增到KMCollectionsTable 中
2.2.建立KMEmbeddings_[index]及KMMemoriesTags_[index](config 中 SqlServer 區段EmbeddingsTableName及TagsTableName的設定值)這 2 個 Table。(如果不存在才建立)

index 相關的 Table 是將Tags及Embeddings每項存成一筆資料。

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BEGIN TRANSACTION;

    INSERT INTO [dbo].[KMCollections]([id])
        VALUES ('gss');

    IF OBJECT_ID(N''[dbo].[KMMemoriesTags_gss]'', N''U'') IS NULL
        CREATE TABLE [dbo].[KMMemoriesTags_gss]
        (
            [memory_id] UNIQUEIDENTIFIER NOT NULL,
            [name] NVARCHAR(256)  NOT NULL,
            [value] NVARCHAR(256) NOT NULL,
            FOREIGN KEY ([memory_id]) REFERENCES [dbo].[KMMemories]([id])
        );

    IF OBJECT_ID(N''[dbo].[KMEmbeddings_gss]'', N''U'') IS NULL
        CREATE TABLE [dbo].[KMEmbeddings_gss]
        (
            [memory_id] UNIQUEIDENTIFIER NOT NULL,
            [vector_value_id] [int] NOT NULL,
            [vector_value] [float] NOT NULL,
            FOREIGN KEY ([memory_id]) REFERENCES [dbo].[KMMemories]([id])
        );

    IF OBJECT_ID(N''[dbo.IXC_KMEmbeddings_gss]'', N''U'') IS NULL
        CREATE CLUSTERED COLUMNSTORE INDEX [IXC_KMEmbeddings_gss]
        ON [dbo].[KMEmbeddings_gss];

COMMIT;

3.刪除documentId為 hr001 的資料(如果有的話)
讀取_files/gss/hr001/__pipeline_status.json將舊的 Embeddings 資料刪除(artifact_type為TextEmbeddingVector的資料)。
Loop 去執行刪除，

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BEGIN TRANSACTION;

    DELETE [tags]
        FROM [dbo].[KMMemoriesTags_gss] [tags]
        INNER JOIN [dbo].[KMMemories] ON [tags].[memory_id] = [dbo].[KMMemories].[id]
        WHERE
            [dbo].[KMMemories].[collection] = N'gss'
        AND [dbo].[KMMemories].[key]=N'd=hr001//p=每段的Id';

    DELETE [embeddings]
        FROM [dbo].[KMEmbeddings_gss] [embeddings]
        INNER JOIN [dbo].[KMMemories] ON [embeddings].[memory_id] = [dbo].[KMMemories].[id]
        WHERE
            [dbo].[KMMemories].[collection] = N'gss'
            AND [dbo].[KMMemories].[key]=N'd=hr001//p=每段的Id';

    DELETE FROM [dbo].[KMMemories]
           WHERE [collection] = N'gss' AND [key]=N'd=hr001//p=每段的Id';

COMMIT;

4.將 Chunks 資料新增/修改到KMMemories,KMEmbeddings_gss及KMMemoriesTags_gss 資料表
Loop 執行

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BEGIN TRANSACTION;
    -- 每段 Chunks 資料
    MERGE INTO [dbo].[KMMemories]
        USING (SELECT N'd=hr001//p=每段的Id') as [src]([key])
        ON [dbo].[KMMemories].[key] = [src].[key]
        WHEN MATCHED THEN
            UPDATE SET payload=N'{"url":"","schema":"20231218A","file":"hr001.docx","text":"Chunk 的文字內容，會以u524D","vector_provider":"AI.OpenAI.OpenAITextEmbeddingGenerator","vector_generator":"__","last_update":"2025-03-07T06:20:04"}', embedding=N'[Embeddings 值, 如0.01507735,0.013598721]', tags=N'{"__document_id":["hr001"],"__file_type":["application/..."],"__file_id":["6ecf..."],"__file_part":["11c5..."],"__part_n":["0"],"__sect_n":["0"]}'
        WHEN NOT MATCHED THEN
            INSERT ([id], [key], [collection], [payload], [tags], [embedding])
            VALUES (NEWID(), N'd=hr001//p=每段的Id', N'gss', N'{"url":"","schema":"20231218A","file":"hr001.docx","text":"Chunk 的文字內容，會以u524D","vector_provider":"AI.OpenAI.OpenAITextEmbeddingGenerator","vector_generator":"__","last_update":"2025-03-07T06:20:04"}', N'{"__document_id":["hr001"],"__file_type":["application/..."],"__file_id":["6ecf..."],"__file_part":["11c5..."],"__part_n":["0"],"__sect_n":["0"]}', N'[Embeddings 值, 如0.01507735,0.013598721]');

    --將每個 Embedding 存到 KMEmbeddings_gss 中
    MERGE [dbo].[KMEmbeddings_gss] AS [tgt]
        USING (
            SELECT
                [dbo].[KMMemories].[id],
                cast([vector].[key] AS INT) AS [vector_value_id],
                cast([vector].[value] AS FLOAT) AS [vector_value]
            FROM [dbo].[KMMemories]
            CROSS APPLY
                openjson(N'[Embeddings 值, 如0.01507735,0.013598721]') [vector]
            WHERE [dbo].[KMMemories].[key] = N'd=hr001//p=每段的Id'
                AND [dbo].[KMMemories].[collection] = N'gss'
        ) AS [src]
        ON [tgt].[memory_id] = [src].[id] AND [tgt].[vector_value_id] = [src].[vector_value_id]
        WHEN MATCHED THEN
            UPDATE SET [tgt].[vector_value] = [src].[vector_value]
        WHEN NOT MATCHED THEN
            INSERT ([memory_id], [vector_value_id], [vector_value])
            VALUES ([src].[id],
                    [src].[vector_value_id],
                    [src].[vector_value] );

    DELETE FROM [tgt]
        FROM  [dbo].[KMMemoriesTags_gss] AS [tgt]
        INNER JOIN [dbo].[KMMemories] ON [tgt].[memory_id] = [dbo].[KMMemories].[id]
        WHERE [dbo].[KMMemories].[key] = N'd=hr001//p=每段的Id'
                AND [dbo].[KMMemories].[collection] = N'gss';

    --把Tags存到 KMMemoriesTags_gss
    MERGE [dbo].[KMMemoriesTags_gss] AS [tgt]
        USING (
            SELECT
                [dbo].[KMMemories].[id],
                cast([tags].[key] AS NVARCHAR(MAX)) COLLATE SQL_Latin1_General_CP1_CI_AS AS [tag_name],
                [tag_value].[value] AS [value]
            FROM [dbo].[KMMemories]
            CROSS APPLY openjson(N'{"__document_id":["hr001"],"__file_type":["application/..."],"__file_id":["6ecf..."],"__file_part":["11c5..."],"__part_n":["0"],"__sect_n":["0"]}') [tags]
            CROSS APPLY openjson(cast([tags].[value] AS NVARCHAR(MAX)) COLLATE SQL_Latin1_General_CP1_CI_AS) [tag_value]
            WHERE [dbo].[KMMemories].[key] = N'd=hr001//p=每段的Id'
                AND [dbo].[KMMemories].[collection] = N'gss'
        ) AS [src]
        ON [tgt].[memory_id] = [src].[id] AND [tgt].[name] = [src].[tag_name]
        WHEN MATCHED THEN
            UPDATE SET [tgt].[value] = [src].[value]
        WHEN NOT MATCHED THEN
            INSERT ([memory_id], [name], [value])
            VALUES ([src].[id],
                    [src].[tag_name],
                    [src].[value]);
COMMIT;
AskStreamingAsync

從 kmClient.AskStreamingAsync(question, minRelevance: 0.7, options: new SearchOptions { Stream = true }, index: tenantId)開始，先將問題轉成 Embeddings ，再到資料庫找出相似度大於0.7的 Chunks 資料，

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WITH
[embedding] as
(
    SELECT
        cast([key] AS INT) AS [vector_value_id],
        cast([value] AS FLOAT) AS [vector_value]
    FROM
        openjson(N'[問題的 Embeddings, 例如 0.008542997,0.012275496]')
),
[similarity] AS
(
    SELECT TOP (100)
        [dbo].[KMEmbeddings_gss].[memory_id],
        SUM([embedding].[vector_value] * [dbo].[KMEmbeddings_gss].[vector_value]) /
        (
            SQRT(SUM([embedding].[vector_value] * [embedding].[vector_value]))
            *
            SQRT(SUM([dbo].[KMEmbeddings_gss].[vector_value] * [dbo].[KMEmbeddings_gss].[vector_value]))
        ) AS cosine_similarity
        -- sum([embedding].[vector_value] * [dbo].[KMEmbeddings_gss].[vector_value]) as cosine_distance -- Optimized as per https://platform.openai.com/docs/guides/embeddings/which-distance-function-should-i-use
    FROM
        [embedding]
    INNER JOIN
        [dbo].[KMEmbeddings_gss] ON [embedding].vector_value_id = [dbo].[KMEmbeddings_gss].vector_value_id
    INNER JOIN
        [dbo].[KMMemories] ON [dbo].[KMEmbeddings_gss].[memory_id] = [dbo].[KMMemories].[id]
    WHERE 1=1

    GROUP BY
        [dbo].[KMEmbeddings_gss].[memory_id]
    ORDER BY
        cosine_similarity DESC
)
SELECT DISTINCT
    JSON_VALUE(payload, '$.text') AS Txt,
    [dbo].[KMMemories].[id],[dbo].[KMMemories].[key],[dbo].[KMMemories].[payload],[dbo].[KMMemories].[tags],
    [similarity].[cosine_similarity]
FROM
    [similarity]
INNER JOIN
    [dbo].[KMMemories] ON [similarity].[memory_id] = [dbo].[KMMemories].[id]
WHERE
    [cosine_similarity] >= 0.7

ORDER BY [cosine_similarity] desc

將這些內容組成 Prompt 來生成答案，如下，

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Facts:
==== [File:請假規則.docx;Relevance:89.3%]:
Chunk 1 的內容....

==== [File:請假規則.docx;Relevance:87.1%]:
Chunk 2 的內容....

==== [File:請假規則.docx;Relevance:87.0%]:
假日，不給給薪薪薪)。
Chunk 3 的內容...
==== [File:請假規則.docx;Relevance:86.6%]:
位為為為0.5小時。
Chunk 4 的內容...

======
Given only the facts above, provide a comprehensive/detailed answer.
You don't know where the knowledge comes from, just answer.
If you don't have sufficient information, reply with 'INFO NOT FOUND'.
Question: 請假6天，需要簽到那位主管核准? 要事前幾天請呢?
Answer:

所以最後就從 LLM 就依 Search 到的內容來得到結果。

結論

embeddings 就是一堆的數值，比較難的是將文字計算出這些 embeddings，但許多 Embeddings Models 已經可以幫我們做到這些事。
所以我們可以將這些數值存到 Table 中，並建立columnstore index來加快它的查詢，計算出文字的相似度。
Cosine similarity [-1,1]的 SQL 如下，

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SELECT
    SUM(a.value * b.value) / (
        SQRT(SUM(a.value * a.value)) * SQRT(SUM(b.value * b.value))
    ) AS cosine_similarity
FROM
    vectors_values

數值越接近 1，代表兩個向量越相似。

詳細請參考Vector Similarity Search with Azure SQL database and OpenAI

  • 註:如果使用 MSSQL 建議使用 Azure SQL，或是等待 SQL 2025，使用 Vector 資料型態效能比較好，因為我用 LocalDB 來處理，常常會有 SQL Timeout 的狀況

參考資源

SQL 資料庫 引擎中的向量概觀
Vector Similarity Search with Azure SQL database and OpenAI
RAG with SQL Vector Store: A Low-Code/No-Code Approach using Azure Logic Apps

tag:

    缺失模块。
    1、请确保node版本大于6.2
    2、在博客根目录（注意不是yilia根目录）执行以下命令：
    npm i hexo-generator-json-content --save

    3、在根目录_config.yml里添加配置： 

      jsonContent:
    meta: false
    pages: false
    posts:
      title: true
      date: true
      path: true
      text: false
      raw: false
      content: false
      slug: false
      updated: false
      comments: false
      link: false
      permalink: false
      excerpt: false
      categories: false
      tags: true

大家好，我是亂馬客 Rainmaker，目前任職於叡揚資訊，大家也都叫我 RM 。 平常負責開發公司各產品線的加值、共用的軟體，例如 Azure, OpenAI, Chatbot, Database ... 同時 Support 公司各種技術及資安修改問題。 Blog中的內容也是個人的看法， 如果有需要討論的地方， 可以留言，或是 mail 給我 :) rainmaker_ho@gss.com.tw