Duration: 3 Days

Description

This course provides a comprehensive introduction to vector databases and semantic search using OpenAI technologies. Participants will learn how to implement and optimize vector-based search systems for efficient information retrieval. The workshop covers the theory behind vector embeddings, setting up and managing a vector database, and integrating semantic search capabilities with practical use cases.

Audience

This course is designed for developers, data engineers, and machine learning practitioners interested in building intelligent search systems using vector databases and semantic search techniques. It is well-suited for those who want to move beyond traditional keyword-based search and leverage the power of embeddings and similarity scoring. Whether you’re working on recommendation systems, document search, or domain-specific AI tools, this course provides a practical foundation for integrating vector-based retrieval into your applications.

Objectives

  • Understand vector embeddings and their role in semantic search
  • Set up and manage an OpenAI-powered vector database
  • Implement and optimize semantic search using vector embeddings
  • Integrate vector search into real-world applications
  • Fine-tune and customize vector-based search for specific domains

Prerequisites

Participants should have at least six months of hands-on experience with Python programming. A basic understanding of machine learning concepts and large language models is expected, along with familiarity working with REST APIs. Prior exposure to cloud computing environments and foundational knowledge of databases will help ensure a smooth experience with the course material.

Course Outline

Module 1: What is a Vector Database?

  • Definition of Vector Databases and their Role in Modern Search
  • Key Differences Between Traditional and Vector Databases
  • Use Cases for Vector Databases in Various Industries

Module 2: Introduction to Embeddings

  • Concept of Embeddings in Machine Learning and Natural Language Processing
  • How Embeddings Represent Text as vectors in High-Dimensional Space
  • Overview of Popular Pre-Trained Models for Generating Embeddings

Module 3: Setting Up OpenAI for Embeddings

  • Obtaining an API Key and Understanding Usage Limits
  • Introduction to the OpenAI Embeddings API
  • Generating Basic Text Embeddings Using OpenAI Models

Module 4: Building a Simple Vector Database

  • Overview of Popular Vector Databases
  • Initial Setup and Environment Configuration
  • Storing and Retrieving Embeddings from a Vector Database

Module 5: Understanding Semantic Search

  • Definition and Benefits of Semantic Search Over Keyword-Based Search
  • How Embeddings are Used to Perform Semantic Search
  • Introduction to Cosine Similarity and Other Distance Metrics

Module 6: Implementing Semantic Search with OpenAI

  • Querying the Vector Database Using Semantic Search
  • Integrating Cosine Similarity with a Vector Search Function
  • Build a Simple Semantic Search Engine Using OpenAI Models

Module 7: Indexing and Query Optimization

  • Strategies for Efficient Indexing of Large Datasets
  • Implementing Approximate Nearest Neighbor (ANN) for Faster Search
  • Tuning Vector Database Performance for Scale

Module 8: Evaluating Search Results

  • Metrics for Evaluating Search Quality
  • Analyzing and Interpreting Semantic Search Results
  • Evaluate and Optimize Search Engine Results

Module 9: Fine-Tuning Embeddings for Domain-Specific Applications

  • How to Fine-Tune OpenAI Embeddings for Specific Domains
  • Fine-Tuning Embeddings with Domain-Specific Data
  • Using Few-Shot Learning to Improve Search Relevance

Module 10: Integrating Vector Search into Applications

  • Connecting Vector Search to Web Applications via REST APIs
  • Building a Full-Stack Application that Uses Semantic Search for Real-Time Results
  • Build a Simple Web-Based Search Engine

Module 11: Scaling and Monitoring Vector Databases

  • Best Practices for Scaling Vector Databases in Production Environments
  • Implementing Monitoring Tools to Track Performance and Ensure Reliability
  • Techniques for Handling Large-Scale Data in Vector Databases