Building a Vector Search Engine with ChromaDB

Overview

This path introduces ChromaDB as the backbone for building vector-based search—covering embedding generation, semantic retrieval, and scalable optimization. Learn to create fast, intelligent search systems from the ground up.

Syllabus

Course 1: Understanding Embeddings and Vector Representations
Course 2: Storing, Indexing, and Managing Vector Data with ChromaDB
Course 3: Implementing Semantic Search with ChromaDB
Course 4: Optimizing and Scaling ChromaDB for Vector Search

Courses

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2 hours
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This course introduces vector embeddings, why they are useful for search, and how to generate them using different models like OpenAI and Hugging Face.
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3 hours
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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.
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2 hours
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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.
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2 hours
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This course focuses on scaling ChromaDB for large-scale deployments, improving search efficiency, reducing retrieval latency, and parallelizing queries for real-time performance.