Building a Vector Search Engine with Qdrant

This course focuses entirely on **Qdrant**, an open-source and cloud-native vector database. It covers setting up a Qdrant collection, storing embeddings, querying efficiently, configuring indexing, and managing large-scale vector data.

This course focuses on **advanced semantic search techniques** in Qdrant, including multi-query expansion, hybrid retrieval using payload filtering, reranking strategies, and improving semantic relevance.

This course focuses on **scaling Qdrant deployments**, improving retrieval efficiency, reducing search latency, and handling real-time updates for production workloads.