Qdrant - Vector Database & Search Engine Courses

Beyond Web Search - Real-Time Web Intelligence for AI-Native Agents

Discover how to build AI-native search infrastructure that delivers structured, real-time web intelligence for LLM applications beyond traditional human-focused search.

• YouTube
• 18 minutes
• On-Demand
• Free Video

Vision-Language Models - A New Architecture for Embedding Models

Explore Vision-Language Models as embedding architectures, covering VLM training insights, dense/multi-vector retrieval, image resolution, modality gaps, and quantization trade-offs.

• YouTube
• 19 minutes
• On-Demand
• Free Video

Vector Search with Gemini Embedding and Open EmbeddingGemma Models

Explore Google DeepMind's Gemini Embedding and EmbeddingGemma models for vector search, covering task controls, dimensionality options, and production deployment strategies.

• YouTube
• 16 minutes
• On-Demand
• Free Video

High-Throughput, Low-Latency Embedding Pipelines for Real-World Applications

Discover how to build production-ready embedding pipelines that handle scale while maintaining low latency for RAG, search, and recommendation systems.

• YouTube
• 17 minutes
• On-Demand
• Free Video

Vector Search Powers Workflow Engineering for Context-Rich AI Systems

Discover how vector databases enable workflow engineering for AI systems through state management, long-term memory, and production-ready patterns with practical code examples.

• YouTube
• 16 minutes
• On-Demand
• Free Video

Multimodal Vectors for Instant, Personalized Product Discovery

Discover how multimodal embeddings and vector databases create autonomous product search systems that combine imagery, metadata, and personal context for instant, personalized discovery.

• YouTube
• 11 minutes
• On-Demand
• Free Video

Hands-On GraphRAG - Using Knowledge Graphs to Improve Retrieval Grounding

Discover how GraphRAG combines knowledge graphs with RAG to enhance retrieval precision, explainability, and context grounding for more reliable generative AI applications.

• YouTube
• 15 minutes
• On-Demand
• Free Video

Stories from the AI Search Frontier - Building Production-Ready Natural Language Search

Discover real-world AI search deployment challenges and solutions using metadata-aware embeddings for TB-scale production systems across industries.

• YouTube
• 13 minutes
• On-Demand
• Free Video

Practical Multimodal Embeddings - Video Recommendations and Cross-Modal Search

Explore multimodal embeddings for video recommendations and cross-modal search using TwelveLabs API and Qdrant, covering chunking strategies and evaluation methods.

• YouTube
• 16 minutes
• On-Demand
• Free Video

Beyond Text-Only - LlamaIndex Retriever with Superlinked's Mixture of Encoders

Discover how to build a custom LlamaIndex retriever using Superlinked's mixture of encoders to improve search accuracy across text, images, numbers, and categories with Qdrant.

• YouTube
• 18 minutes
• On-Demand
• Free Video

From Massive Text Streams to Searchable Knowledge with Apache Kafka and Qdrant

Discover how to build 24/7 pipelines transforming unstructured text streams into searchable knowledge using Apache Kafka, Qdrant vector database, and scalable embedding generation techniques.

• YouTube
• 13 minutes
• On-Demand
• Free Video

Optimizing GDPR Compliance Retrieval with Hybrid Graph-Augmented RAG Systems

Explore advanced RAG architectures for legal compliance, comparing vector, hybrid, and graph-augmented systems to achieve precise GDPR query responses with proper citations.

• YouTube
• 12 minutes
• On-Demand
• Free Video

Qdrant Essentials - Build a Movie Recommendation Engine

Discover how to build a movie recommendation engine using Qdrant vector database in this hands-on tutorial covering essential search and recommendation techniques.

• YouTube
• 21 minutes
• On-Demand
• Free Video

Qdrant x Haystack - Build Smarter Recommenders with Agentic Search

Discover how to build intelligent recommendation systems using Qdrant vector database and Haystack framework through agentic search techniques in this 24-minute tutorial.

• YouTube
• 24 minutes
• On-Demand
• Free Video

Qdrant Essentials - Creating Vectors and Embeddings for Vector Search in Qdrant

Discover how to create vectors and embeddings for efficient vector search using Qdrant database technology in this essential 18-minute tutorial.

• YouTube
• 18 minutes
• On-Demand
• Free Video