Build Chroma Search

Overview

Build Chroma Search is an intermediate, project-based course for developers and aspiring machine learning engineers who want to build and deploy a complete, real-world semantic search application. In today's AI-driven landscape, keyword search is no longer enough; this course teaches you how to leverage the power of vector embeddings and the specialized vector database, Chroma, to create a search engine that understands meaning, not just words. You will progress through a full development lifecycle, from indexing a document collection to exposing your search functionality through a deployable Flask API. The course places a strong emphasis on professional standards, guiding you to quantitatively measure your API's performance using critical relevance metrics like Mean Reciprocal Rank (MRR) and precision@5. Through hands-on labs and a final summative project, you will not only build a functional search API but also produce an evaluation report to validate its quality, equipping you with a portfolio-ready project and the skills to tackle advanced information retrieval tasks.

Syllabus

Build the Semantic Search Engine

This module focuses on building the core of your search application. You will learn the fundamental concepts behind semantic search, explore the Chroma vector database, and get hands-on experience indexing a document collection. By the end of this module, you will have a functional search engine capable of retrieving documents based on semantic meaning.

Build the Evaluation Pipeline

A search engine is only as good as its results. In this module, you'll learn how to quantitatively measure search quality. You will explore industry-standard relevance metrics, build a Python script to implement them, and prepare to benchmark your search engine's performance objectively.

Deploy & Analyze the Search API

In this final module, you will turn your local search script into a shareable, production-style service. You'll learn to wrap your Chroma engine in a simple web API using Flask, analyze its performance, and consider the trade-offs of different models. This module culminates in the final project where you will build, deploy, and evaluate your complete semantic search API.