# SQL und Vektorverbundabfragen

MyScale kombiniert die Leistung von SQL und Vektoren für Ihre KI-Daten-Workloads. Sie können komplexe traditionelle SQL- und Vektorabfragen auf ihre strukturierten (vektorisierten) und unstrukturierten Daten ausführen und dabei die umfassende Bibliothek intuitiver Syntax von SQL nutzen, um schnelle und effiziente Datenanalyseoperationen durchzuführen.

# SQL + Vektor: Warum ist es wichtig?

Die Bedeutung von traditionellen SQL- und Vektorverbundabfragen liegt in ihrer Fähigkeit, strukturierte Daten und Vektoreinbettungen (Daten) zu kombinieren, um komplexe Abfragen zu bearbeiten und hochdimensionale Daten auf eine einheitliche und effiziente Weise zu analysieren. Dieser Ansatz bietet mehrere Vorteile und Möglichkeiten:

  • Arbeiten mit mehr Datentypen und Funktionen: MyScale kann hochdimensionale Vektordaten und Metadaten speichern und abfragen. Diese nahtlose Integration ermöglicht es Benutzern, komplexere Abfragen und Datenanalysen zu entwerfen und auszuführen. Die Verwendung von KI-Modellen und Geschäftsdaten unterstützt Entscheidungsprozesse, indem sie eine umfassendere Perspektive auf die Daten bietet.

  • Hybride Suche: Die Kombination von semantischer Suche mit traditioneller Schlüsselwortübereinstimmung in der hybriden Suche überwindet die Herausforderung unzureichender semantischer Abdeckung in vektorisierten Dokumenten. Dies ist besonders vorteilhaft, um seltene Wörter zu finden oder wenn ein Benutzer gezielt nach bestimmten Begriffen sucht. Darüber hinaus wird die Grundlage für die hybride Suche durch SQL- und Vektorverbundabfragen geschaffen.

  • Einheitliche Abfragesprache: SQL ist eine weit verbreitete Datenbankabfragesprache, die von vielen Entwicklern und Datenanalysten verwendet wird. Benutzer können die Syntax, die sie bereits kennen, nahtlos integrieren, indem sie SQL und Vektorsuche in einer einzigen Abfrage kombinieren, ohne neue Abfragesprachen lernen zu müssen.

  • Effiziente Datenabfrage: Vektorisierte (unstrukturierte) und strukturierte Daten erfordern oft spezialisierte Vektorindizes und Suchalgorithmen für eine effiziente und effektive Abfrage. Durch die Kombination von SQL und Vektorsuche in einer einzigen Abfrage kann die MyScale-Engine vektorbezogene Suchen durchführen und Abfrageausführungspläne optimieren, um Daten schneller und effizienter abzurufen.

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    Für ein Beispiel, wie Daten effektiv abgerufen werden können, gehen Sie zu Erweiterte Anwendungen: Abfrageentwurf.

  • Optimierung von Datenworkflows: MyScale bietet eine einheitliche Datenlösung, von nativem SQL bis hin zu Vektorsuchabfragen. SQL-Benutzer können ihre Abfragen mit Vektorsuchen erweitern, während Vektorsuchbenutzer ihre Metadaten in gut strukturierten Tabellen speichern können.

Zusammenfassend lässt sich sagen, dass traditionelles SQL in Verbindung mit Vektorabfragen hochdimensionale Vektordaten verarbeiten und analysieren kann, wodurch sich der Anwendungsbereich von Datenbanken erweitert und erweiterte Datenanalysefunktionen bereitgestellt werden. Dies ist für zahlreiche Disziplinen von Bedeutung, darunter maschinelles Lernen, Datenwissenschaft, Bildverarbeitung und mehr.

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Siehe Beispielanwendungen und Erweiterte Anwendungen, um ein umfassendes Verständnis der robusten Natur von traditionellen SQL-Abfragen und Vektorsuche zu erhalten.

# Verwendung von SQL + Vektor: Eine Demo basierend auf ChatData

ChatData, eine LLM-Chat-App, bietet eine unübertroffene Effizienz und Genauigkeit bei der Interaktion mit Ihren Dokumenten. Die leistungsstarken Metadatenfilter und erweiterten Vektorsuchfunktionen vereinfachen das Beantworten von Fragen zu Domänendaten.

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Mit ChatData können Sie nahtlos mit Millionen von Dokumenten interagieren, einschließlich wissenschaftlicher Arbeiten, und schnell und einfach finden, was Sie brauchen. Egal, ob Sie Forscher, Student oder Wissensenthusiast sind, die Erkundung wissenschaftlicher Arbeiten und Forschungsdokumente war noch nie so einfach wie mit ChatData. Entdecken Sie das wahre Potenzial der Informationssuche mit ChatData und entdecken Sie eine Welt des Wissens direkt vor Ihren Augen.

Der folgende Abschnitt beschreibt, wie Sie eine Demo in MyScale erstellen können, die auf der Abfrageabrufimplementierung von ChatData basiert, und zeigt, wie Sie SQL- und Vektorverbundabfragen ausführen können.

# Erstellen der Datenbanktabellen

Diese Beispielanwendung verwendet eine Dokumentenbibliothek mit wissenschaftlichen Papierdaten (in Vektoren), die gespeichert und abgerufen werden, wenn sie abgefragt werden. Führen Sie die folgenden SQL-Anweisungen aus, um relationale Tabellen für die Dokumentenbibliothek in MyScale's SQL Workspace zu erstellen.

# Die Tabelle chatPDF

Diese Tabelle speichert Textdaten von Absätzen, die aus PDFs in der Dokumentenbibliothek extrahiert wurden, in den folgenden Feldern:

  1. id: Die Absatz-ID
  2. pdf (SHA256): Die entsprechende PDF-ID
  3. content: Der Absatzinhalt
  4. section: Die Abschnitt, zu dem der Absatz gehört
  5. page: Die Seitenzahl
  6. vector: Der Merkmalsvektor des Absatzes
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;

# Die Tabelle chatPDF_meta

Diese Tabelle speichert Metadateninformationen, die mit den PDF-Dateien der Dokumentenbibliothek verknüpft sind, in den folgenden Feldern:

  1. pdf (SHA256): Die PDF-ID
  2. title: Der Titel des PDFs
  3. authors: Der Autor bzw. die Autoren des PDFs
  4. abstract: Die Zusammenfassung des PDFs
  5. pub_date: Veröffentlichungsdatum
  6. doi: Die DOI (Document Object Identifier) des Dokuments - https://doi.org (opens new window)
  7. publisher: Der Herausgeber des Dokuments
  8. article_type: Der Artikeltyp/Kategorie - {'arXiv', 'Finance'}
  9. vector: Der Merkmalsvektor der Zusammenfassung des Absatzes
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;

# Die Tabelle chatPDF_ref

Diese Tabelle speichert Informationen über die in den PDF-Dateien der Dokumentenbibliothek zitierten Referenzen in den folgenden Feldern:

  1. pdf (SHA256): Die zitierte PDF-ID
  2. title: Der Titel des referenzierten Artikels
  3. authors: Der Autor bzw. die Autoren des referenzierten Artikels
  4. journal: Die Zusammenfassung des referenzierten Artikels
  5. year: Das Veröffentlichungsjahr des referenzierten Artikels
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;

# Die Tabelle chatPDF_user

Diese Tabelle speichert die Zugriffsberechtigungen der Benutzer auf alle PDF-Dokumente der Dokumentenbibliothek in den folgenden Feldern:

  1. id: ACL (Access Control List) ID
  2. user: ACL-Benutzer
  3. pdf: ACL-PDF-Dokumentcodes, auf die der Benutzer zugreifen kann
CREATE TABLE default.chatPDF_user (
    `id` String, 
    `user` String, 
    `pdf` String) 
ENGINE = ReplacingMergeTree ORDER BY id SETTINGS index_granularity = 8192;

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Wenn ein Benutzer Zugriffsrechte für mehrere PDF-Dokumente besitzt, gibt es mehrere Zeilen in der chatPDF_user-Tabelle, die mit dem entsprechenden Benutzer verknüpft sind.

# Ausführen von SQL- und Vektorverbundabfragen

Mit diesen Datenbanktabellen zeigen wir, wie effizient SQL- und Vektorverbundabfragen ausgeführt werden können, die die Abrufanforderungen erfüllen.

# Anwendungsfall 1: Abrufen relevanter Artikel aus einer Sammlung von PDF-Dateien

Dieses Szenario zielt darauf ab, die PDFs und ihre Titel aus der Tabelle chatPDF abzurufen, die folgende Bedingungen erfüllen:

  • Der Benutzer ist entweder "default" oder "test"
  • Die Autoren enthalten Namen wie "Cedar"
  • Die Reihenfolge wird basierend auf der Entfernung zum Abfragevektor bestimmt, der semantische Ähnlichkeiten repräsentiert

Um die Daten abzurufen, die diese Bedingungen erfüllen, führen Sie die folgende SQL-Anweisung aus:

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;

# Anwendungsfall 2: Suche nach relevanten Abschnitten zum bereitgestellten Einbettungsvektor

Wie unten zu sehen ist, verwendet die SQL-Anweisung in diesem Szenario den Einbettungsvektor, um nach Abschnitten zu suchen, die folgende Bedingungen erfüllen:

  • Diese Abschnitte müssen sich auf den Abfrageeinbettungsvektor beziehen
  • Der Benutzer ist entweder "default" oder "test"
  • Die Abfrageergebnisse werden entsprechend den Schlüsselwörtern der zerlegten Dokumente neu angeordnet, wodurch die Leistung von Vektor und Schlüsselwörtern für eine genauere Abfrage kombiniert wird.
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;

# Hinweise

Folgende Punkte gelten bei der Verwendung von MyScale für SQL- und Vektorverbundabfragen:

  1. Sie können das Abfrage-Muster distance()...ORDER BY...LIMIT in Unterabfragen verwenden, die Vektorindizes nutzen. Wenn MyScale dieses Muster erkennt und eine Tabelle, die in der Unterabfrage verwendet wird, Vektorspalten mit Vektorindizes enthält, werden die Vektorindizes zur Beschleunigung der Abfrage verwendet. In ClickHouse kann die ORDER BY-Klausel nicht nur in der äußersten Abfrage erscheinen.

  2. MyScale ist eine spaltenorientierte Datenbank, daher sollten die Abfrageergebnisse idealerweise nur wesentliche Spalten enthalten. Nicht wesentliche Spalten sollten nicht in der SELECT-Klausel erscheinen, und SELECT * sollte vermieden werden, um die Abfragegeschwindigkeit zu verbessern.