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Das Handbuch zur Vektordatenbank: Effiziente Skalierung für KI-gesteuerte Suche
Paperback
Publisher: Independently Published
Published: Mar 16 2026
Pages: 240
Weight: 0.72
Height: 0.51 Width: 6.00 Depth: 9.00
Language: German
Klassische Datenbanken sto en an ihre Grenzen, wenn es um semantische Suche, Embeddings und KI-gestützte Anwendungen geht.
Moderne KI-Systeme benötigen eine Infrastruktur, die Bedeutung statt nur Schlüsselwörter versteht. Genau hier kommen Vektordatenbanken ins Spiel.
Das Handbuch zur Vektordatenbank zeigt Ihnen, wie Sie leistungsfähige, skalierbare Systeme für semantische Suche, Retrieval-Augmented Generation (RAG) und intelligente Empfehlungssysteme entwickeln.
Von den Grundlagen bis zur produktionsreifen Architektur - praxisnah, technisch fundiert und strategisch gedacht.
Was Sie in diesem Buch lernen
Wie Vektorembeddings funktionieren
Unterschied zwischen klassischer Suche und semantischer Suche
Approximate Nearest Neighbor (ANN) verständlich erklärt
Indexierungsstrategien für gro e Datenmengen
Performance-Optimierung und Skalierung
Hybrid-Suche (Keyword + Vektor) implementieren
RAG-Systeme effizient aufbauen
Speicher- und Latenzoptimierung
Monitoring und Produktionsbetrieb
Technologien und Konzepte im Fokus
Dieses Buch behandelt moderne Vektordatenbank-Systeme und Frameworks, darunter:
Pinecone
Weaviate
Milvus
Chroma
FAISS
Sie erfahren, wann welche Lösung sinnvoll ist - und wie Sie Architekturentscheidungen strategisch treffen.
Praxisanwendungen
Semantische Dokumentensuche
KI-gestützte Chatbots mit Wissensbasis
Empfehlungssysteme
Ähnlichkeitssuche für Bilder und Texte
Skalierbare Suchsysteme für SaaS-Produkte
Enterprise-Suche mit hybriden Retrieval-Strategien
Alle Konzepte werden anhand realer Architekturbeispiele und Performance-Szenarien erklärt.
Für wen ist dieses Buch geeignet?
KI- und ML-Engineers
Backend-Entwickler
Softwarearchitekten
Data Engineers
CTOs und Tech-Leads
Wenn Sie Suchsysteme bauen wollen, die Bedeutung verstehen statt nur Begriffe vergleichen, ist dieses Handbuch Ihr technischer Leitfaden.
Skalieren Sie KI-Anwendungen richtig.
Optimieren Sie Retrieval-Performance.
Bauen Sie zukunftssichere Sucharchitekturen.
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