Building a Vector Search Engine with ChromaDB

This intermediate path explains how embeddings represent meaning and support semantic search. You will generate vector embeddings with models from OpenAI and Hugging Face and use them to retrieve relevant content. You will set up ChromaDB, store and index embeddings, manage vector data, and implement efficient similarity searches. You will also combine multi-query expansion, hybrid retrieval, and reranking to improve result relevance beyond basic vector similarity. The path concludes with techniques for scaling vector search, reducing retrieval latency, and parallelizing queries for real-time workloads. It is designed for developers who understand programming fundamentals and want to build practical semantic search applications.