Building a Vector Search Engine with ChromaDB

This course focuses entirely on ChromaDB, a lightweight open-source vector database. It covers setting up, storing embeddings, searching efficiently, handling indexing, and managing large-scale vector data.

This course focuses on advanced semantic search techniques in ChromaDB, including multi-query expansion, hybrid retrieval (combining different search strategies), reranking techniques, and improving search relevance beyond basic vector similarity.

This course focuses on scaling ChromaDB for large-scale deployments, improving search efficiency, reducing retrieval latency, and parallelizing queries for real-time performance.