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Produktionsreife Vektordatenbank im Milliardenmaßstab – Elasticsearch Die Open-Source -Vektordatenbank von Elasticsearch bietet eine effiziente Möglichkeit zum Erstellen, Speichern und Suchen von Vektoreinbettungen.
Kombinieren Sie Textsuche und Vektorsuche für hybrides Retrieval, um das Beste aus beiden Welten zu erhalten und für mehr Relevanz und eine größere Genauigkeit zu sorgen.
Entdecken Sie die neuesten Innovationen, die Elasticsearch und Lucene zur ersten Wahl für Vektordatenbanken machen.
Blog lesen Erfahren Sie, wie Sie Elasticsearch als Vektordatenbank für Einbettungen verwenden können, um Suchvorgänge zu unterstützen und Anwendungsfälle wie Retrieval Augmented Generation (RAG), Zusammenfassungen und Fragen und Antworten zu erstellen.
Erfahren Sie mehr über Search Labs Elastic ist das erste Unternehmen, das eine bessere binäre Quantisierung (BBQ) anbietet, eine Optimierung für Vektordatenbanken mit schnellerer, genauerer Vektorsuche und 95 % Speicherreduzierung.
Erfahren Sie mehr über BBQ
Elasticsearch – die am weitesten verbreitete Vektordatenbank Kopieren Sie, um es in zwei Minuten lokal auszuprobieren Copy to clipboard curl -fsSL https://elastic.co/start-local | sh
Dokumentation lesen
Eine Vektordatenbank ist Ihr Ausgangspunkt … Für ein tolles Sucherlebnis braucht es mehr als eine Vektordatenbank. Elasticsearch bietet mehrere Abruftypen, flexible Machine Learning Modelle und erweiterte Suchfunktionen wie Aggregationen, Filtern und automatische Vervollständigung.
In der Cloud , serverlos , On-Prem oder mit Air-Gap ausführen.
Inference-Endpoint erstellen
Wenden Sie ein Machine Learning-Modell zum Zeitpunkt der Ingestion auf Ihre Daten an.
Erfahren Sie mehr über die Inference-API und das E5-Modell .
PUT _inference / text_embedding / my - e5 - endpoint
{
"service" : "elasticsearch" ,
"service_settings" : {
"num_allocations" : 1 ,
"num_threads" : 1 ,
"model_id" : ".multilingual-e5-small"
}
}
POST _inference / my - e5 - endpoint
{
"input" : "How many adult mallard ducks fit in an american football field?"
}
PUT my - index
{
"mappings" : {
"properties" : {
"my_vector" : {
"type" : "dense_vector"
},
"my_text" : {
"type" : "text"
},
"category" : {
"type" : "text"
}
}
}
}
# Index a document
PUT my - index / _doc / 2
{
"my_vector" : [
0.018743202 ,
- 0.011059246 ,
…
0.05155631 ,
0.04044561
],
"my_text" : "57,000 average sized mallard ducks fit on an American football field" ,
"category" : "weird_stats"
}
Führen Sie eine kNN-Suche aus, die zu Ihrem Anwendungsfall passt – wenden Sie dabei den ANN-Algorithmus für Geschwindigkeit und Skalierung mithilfe des HNSW-Index oder der exakten Übereinstimmung von Lucene an.
Entdecken Sie ANN und KNN .
GET / my - index / _search
{
"retriever" : {
"knn" : {
"field" : "my_vector" ,
"query_vector_builder" :{
"text_embedding" : {
"model_id" : "my-e5-endpoint" ,
"model_text" : "How many adult mallard ducks fit in an american football field??"
}
},
"k" : 10 ,
"num_candidates" : 10
}
}
}
Verfeinerung der Suchergebnisse mit Filtern und Facettensuche
GET / my - index / _search
{
"retriever" : {
"knn" : {
"field" : "my_vector" ,
"query_vector_builder" :{
"text_embedding" : {
"model_id" : "my-e5-endpoint" ,
"model_text" : "How many adult mallard ducks fit in an american football field??"
}
},
"k" : 10 ,
"num_candidates" : 10 ,
"filter" : [
{
"term" : {
"category" : "weird_stats"
}
}
]
}
}
}
Kombinieren von Retrieval-Methoden mit Hybridsuche
Mit Hybrid-Retrieval können Sie aus den Abrufmethoden wählen, die die besten Ergebnisse für Ihren Anwendungsfall liefern: BM25, das trainierte Sparse-Encoder-Modell (ELSER ) von Elastic oder dichte Vektoren.
Video zur Hybridsuche ansehen
GET / my - index / _search
{
"retriever" : {
"rrf" : {
"retrievers" : [
{
"standard" : {
"query" : {
"bool" : {
"filter" : [
{
"term" : {
"category" : "weird_stats"
}
}
]
}
}
}
},
{
"knn" : {
"field" : "vector" ,
"query_vector_builder" : {
"text_embedding" : {
"model_id" : "my-e5-endpoint" ,
"model_text" : "How many adult mallard ducks fit in an american football field??"
}
},
"k" : 10 ,
"num_candidates" : 10
}
}
],
"rank_constant" : 1 ,
"rank_window_size" : 50
}
}
}
Anwendung von Sicherheit auf Dokumenteneben und Compliance-Berechtigungen
POST / _security / role / employee_role {
"indices" : [
{
"names" : [ "example-index" ],
"privileges" : [ "read" ],
"field_security" : {
"grant" : [ "state" , "name.first" , "name.last" ]
}
}
]
}
GET example - index / _search {
"query" : {...},
"knn" = {...},
"aggs" : {...}
}
PUT _inference / text_embedding / my - e5 - endpoint
{
"service" : "elasticsearch" ,
"service_settings" : {
"num_allocations" : 1 ,
"num_threads" : 1 ,
"model_id" : ".multilingual-e5-small"
}
}
Vektordatenbank-Integrationen
Was bringt mir eine Vektordatenbank?
Konzentrieren Sie sich beim Suchen auf Intent und Kontextbedeutung, die über reine Textübereinstimmung hinausgehen.
Durchsuchen Sie alle Ihre Daten – Text-, Vektor-, Bild-, Audio-, Video-, Geo- oder unstrukturierte Daten.
Generative-KI-Suche (GKI-Suche) Erstellen Sie Workloads für die Retrieval-augmentierte Generierung für GKI-Sucherlebnisse mit Vektor- und Hybridrelevanz.
Auswahl einer Vektordatenbank Wählen Sie die Vektordatenbank aus, die für das von Ihnen angestrebte Vektorsucherlebnis am besten geeignet ist.
Speichern von Einbettungen
vollständig unterstützt (kostenlos)
vollständig unterstützt (kostenpflichtig)
vollständig unterstützt (kostenlos)
vollständig unterstützt (kostenlos)
Hybride Suche (BM25 + Vektoren)
vollständig unterstützt (kostenlos)
Filtern, Facettieren, Aggregieren
vollständig unterstützt (kostenlos)
Autovervollständigung von Suchbegriffen
vollständig unterstützt (kostenlos)
Optimierung für mehrere Datentypen (Text, Vektordaten, Geodaten)
vollständig unterstützt (kostenlos)
Unterstützung für verschiedene Einbettungsmodelle
vollständig unterstützt (kostenpflichtig)
Integriertes Modell für die semantische Suche
vollständig unterstützt (kostenpflichtig)
vollständig unterstützt (kostenpflichtig)
Ingestions-Tools (Web-Crawler*, Connectors*, API-Framework, Beats, Fleet, Agent)
vollständig unterstützt (*kostenpflichtig)
Security auf Dokument- und Feldebene
vollständig unterstützt (kostenpflichtig)
Observability-Tools (Kibana)
vollständig unterstützt (kostenlos)
vollständig unterstützt (kostenlos)
Abfragen mit Pipe-Zeichen – ES|QL (demnächst verfügbar)
vollständig unterstützt (kostenlos)
Elasticsearch — in Aktion Erfahren Sie, wie Unternehmen KI-Suchanwendungen entwickeln, um das Kundenerlebnis zu verbessern und den Nutzern zu helfen, genau das zu finden, was sie suchen.
Reed, der größte Personalvermittler Großbritanniens, bringt Jobsuchende und Arbeitgeber mithilfe von Vektoreinbettungen in Elasticsearch zusammen.
Stack Overflow kombiniert die Fähigkeiten menschlicher Experten mit generativer KI, um das Abrufen vertrauenswürdiger Informationen aus den Wissensdatenbanken der Entwickler zu beschleunigen.
Adobe skaliert, verwaltet mehrere Anwendungsfälle und setzt Machine-Learning-Funktionen mit Elastic ein.
Erste Schritte mit der Implementierung der Vektorsuche
Eine Vektordatenbank speichert Informationen als Vektoren, also als numerische Darstellungen von Datenobjekten, die auch als Vektoreinbettungen bezeichnet werden. Sie verwendet Vektoreinbettungen für die multimodale Suche in einem riesigen Datensatz strukturierter, unstrukturierter und halbstrukturierter Daten, zum Beispiel Bilder, Text, Videos und Audio. Vektordatenbanken werden zur Verwaltung von Vektoreinbettungen erstellt und bieten daher eine vollständige Lösung zur Datenverwaltung.
Vektoreinbettungen nutzen ein Machine Learning-Modell, um Text in Zahlen zu übersetzen, damit Sie Vektorsuchen durchführen können. Durch die Umwandlung von Daten in Vektoren erleichtern Einbettungen den Vergleich, die Suche und die Analyse von Ähnlichkeiten zwischen Elementen in diesem Bereich.
Eine Vektordatenbank bietet Effizienz bei der Skalierung, indem sie eine nahtlose Datenmigration zwischen lokalen und Cloud-Umgebungen ermöglicht und Speicherplatz für Vektoreinbettungen bietet. Vektordatenbanken eignen sich hervorragend für die Ähnlichkeitssuche. Sie ermöglichen es Ihnen, verwandte Objekte leicht zu finden, was für Empfehlungssysteme, die Bildersuche und die Entdeckung von Inhalten unerlässlich ist. Mit semantischen Suchfunktionen gehen sie über den einfachen Abgleich von Schlüsselwörtern hinaus und liefern Ergebnisse auf Grundlage von Bedeutung und Kontext. Durch die Speicherung von Vektoreinbettungen unterstützen sie Anwendungen für KI und Machine Learning und erleichtern so den Einsatz von NLP- und Empfehlungsmodellen.
Ja, Elasticsearch ist die weltweit am weitesten verbreitete Open-Source-Vektordatenbank , die Ihnen effiziente Möglichkeiten bietet, Vektoreinbettungen in großem Maßstab zu erstellen, zu speichern und zu suchen. Mit der unternehmenstauglichen Vektordatenbank von Elastic erreichen Sie schnelle Abfragezeiten und optimale Leistung, selbst bei sich schnell ändernden Daten. Sie ist skalierbar, liefert relevante, personalisierte Suchergebnisse und vereinfacht gleichzeitig die Entwicklungsprozesse.
Elastic bietet alle Vorteile einer leistungsstarken Vektordatenbank sowie integrierte Sicherheit , Einhaltung gesetzlicher Vorschriften und hohe Verfügbarkeit. Mit mehr als einem Jahrzehnt Erfahrung im Suchbereich gewährleistet Elastic erstklassige Suchrelevanz und flexible Deployment-Optionen . Als einheitliche Platform minimiert Elastic den Tool-Wildwuchs und den technischen Aufwand und liefert gleichzeitig präzise Antworten mit klaren Quellenangaben.