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Vector Databases for AI: Semantic Search with ChromaDB
Rating: 3.0 out of 5(10 ratings)
39 students

Vector Databases for AI: Semantic Search with ChromaDB

Learn vector database concepts, LSM trees, querying, scaling, and build real-world semantic search applications
Created byTech Jedi
Last updated 1/2026
English

What you'll learn

  • Understand how vector databases work internally and how they differ from traditional SQL and NoSQL databases
  • Explain the role of LSM trees and indexing strategies in high-performance vector search
  • Design data models and schemas optimized for vector-based workloads
  • Create, insert, update, and query data in vector databases using real tools
  • Implement similarity search and semantic search techniques for AI applications
  • Use ChromaDB and Pinecone for building practical vector database solutions
  • Apply advanced querying and real-time retrieval strategies
  • Optimize performance using caching, partitioning, and load balancing
  • Scale vector databases horizontally and vertically for production environments
  • Secure vector databases with authentication, encryption, and compliance best practices
  • Integrate vector databases with applications, APIs, and data pipelines
  • Build an end-to-end semantic search system, including a full-stack demo application

Course content

8 sections • 40 lectures • 2h 52m total length

  • Overview of Vector Databases3:55
  • Benefits of Using Vector Databases3:48
  • Comparison with Traditional Databases5:29
  • Use Cases of Vector Databases5:23

    Explore how vector databases store high-dimensional vectors and enable fast similarity searches integrated with large language models (LLMs) to power semantic search, recommendations, and text generation.

  • Introduction To Vector Database-Demo2:17

Requirements

  • Basic understanding of databases or data storage concepts is helpful but not mandatory
  • Familiarity with programming basics (any language such as Python, Java, or JavaScript)
  • General knowledge of APIs and backend concepts is a plus
  • Interest in AI, machine learning, or data engineering concepts
  • A computer with an internet connection to follow demos and hands-on examples

Description

This course contains the use of artificial intelligence (AI) tools for content preparation and educational support.

Vector Databases for AI : Semantic Search with ChromaDB

Learn the fundamentals of vector databases, semantic search, ChromaDB, Pinecone, and modern AI retrieval systems

Vector databases are becoming an essential part of modern AI applications, powering semantic search, recommendation systems, intelligent retrieval, and AI-driven search experiences.

This course is designed to help students and developers understand how vector databases work and how they are used in real-world AI applications. The course combines foundational concepts with practical demonstrations using tools such as ChromaDB and Pinecone.

You will learn how vector databases differ from traditional databases, how similarity search works, and how modern AI systems use vector retrieval for intelligent search applications.

The course also covers important engineering topics including:

  • Vector database architecture

  • Indexing strategies

  • Querying and retrieval

  • Scaling and performance optimization

  • Security and production considerations

  • Integration with applications and APIs

Throughout the course, you will work with practical examples and demos to understand how vector search systems are built and managed in modern AI environments.

What You’ll Learn

  • Fundamentals of vector databases

  • Semantic search concepts

  • Vector similarity concepts

  • ChromaDB basics and operations

  • Pinecone fundamentals

  • Querying and retrieval techniques

  • Indexing and performance optimization

  • Scaling vector databases

  • Security and production best practices

  • Building a semantic search application

Who This Course Is For

  • Developers

  • Cloud engineers

  • Backend engineers

  • AI enthusiasts

  • Students exploring AI infrastructure

  • Anyone interested in semantic search systems

Requirements

  • Basic programming knowledge

  • Familiarity with databases is helpful but not required

  • Interest in AI and modern search systems

Who this course is for:

  • Data Engineers and Data Architects who want to work with modern AI-driven data systems
  • AI / Machine Learning Engineers looking to store, search, and retrieve vector embeddings efficiently
  • Backend and Software Developers building semantic search, recommendation systems, or AI-powered applications
  • Developers exploring LLM-based systems, RAG pipelines, and semantic retrieval
  • Data professionals transitioning into AI infrastructure and vector databases
  • Students and professionals who want practical, real-world exposure to vector databases like ChromaDB and Pinecone

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