Understanding Vector Databases Online Course

Understanding Vector Databases Online Course

This online course provides a comprehensive exploration of vector databases, starting with essential prerequisites and course structure. You’ll gain an in-depth understanding of vector database fundamentals, including their growing importance in modern data management. The course compares traditional databases with vector databases, covering their respective limitations and advantages. You will explore top vector database solutions like Chroma and Pinecone, with hands-on sessions for setting up development environments, creating and querying databases, and understanding key metrics. 

The course also covers vector similarity measures such as cosine similarity and Euclidean distance. Additionally, you will learn to integrate vector databases with Large Language Models (LLM) and the LangChain framework. Finally, you'll learn to choose the right vector database through comparison tables and criteria. By the end of the course, you'll have both theoretical knowledge and practical experience in using vector databases for advanced data management.

Key Benefits

• Examine the differences between traditional and vector databases to gain a comprehensive understanding of their respective advantages and limitations.
• Learn how to effectively integrate vector databases with Large Language Models (LLMs) and frameworks such as LangChain to enhance data processing and retrieval workflows.
• Acquire hands-on experience with top-tier vector database solutions, including Chroma and Pinecone, to build practical skills in their implementation and optimization.

Target Audience

This course is for data scientists, database administrators, and software developers who possess a foundational understanding of databases and programming. Prerequisites for enrollment include a working knowledge of Python and a basic grasp of core database concepts.

Learning Objectives

• Understand the core principles and real-world applications of vector databases.
• Analyze and compare the key differences between traditional and vector databases.
• Set up and effectively utilize leading vector database solutions such as Chroma and Pinecone.
• Apply vector similarity measures, including cosine similarity and Euclidean distance, for advanced data analysis.
• Integrate Large Language Models (LLMs) and frameworks like LangChain with vector databases to enhance data processing capabilities.
• Comprehend the foundational role of vector databases in modern data management and their growing significance.

Course Outline

The Understanding Vector Databases Exam covers the following topics - 

Module 1 - Course Introduction - Prerequisites and Structure

Module 2 - In-Depth Exploration of Vector Databases - Core Concepts

• Overview of Vector Databases
• Why Opt for Vector Databases?
• Advantages and Key Benefits of Vector Databases

Module 3 - Comparing Traditional Databases with Vector Databases

• Key Differences Between Traditional and Vector Databases
• Limitations and Challenges of Traditional Databases vs. Vector Databases
• Full Workflow of Vector Databases and Embeddings
• Differences Between Embeddings and Vectors
• How Vector Databases Operate and Their Advantages
• Practical Use Cases of Vector Databases

Module 4 - Top 5 Vector Database Solutions

• An Overview of the Top 5 Vector Databases
• Understanding Large Language Models (LLM)

Module 5 - Building Vector Databases - Practical Application with Chroma

• Setting Up the Development Environment
• Installation of VS-Code, Python, and OpenAI API Key
• Chroma Database Workflow
• Creating and Querying a Chroma Vector Database, Including Document Insertion
• Iterating Through Results and Demonstrating Similarity Search
• Using Chroma’s Default Embedding Function
• Persisting Data and Saving in Chroma Vector Database
• Creating OpenAI Embeddings Without Chroma
• Embedding with OpenAI API for Chroma Integration
• Understanding Vector Database Metrics and Data Structures

Module 6 - Common Vector Similarity Metrics

• Deep Dive into Vector Similarity: Cosine Similarity
• Euclidean Distance and L2 Norm
• Dot Product for Similarity Measurement

Module 7 - Integrating Vector Databases with Large Language Models (LLM) - Full Workflow

• Detailed Workflow for Vector Databases and LLM
• Document Loading Process
• Embedding Generation from Documents and Insertion into Chroma
• Retrieving Relevant Document Chunks for Queries
• Using OpenAI’s LLM to Generate Responses

Module 8 - Working with Langchain Framework and Vector Databases

• Introduction to Langchain Framework
• Getting Started with LangChain and OpenAIChat Wrapper
• Loading Documents with LangChain Document Loader
• Document Splitting Techniques in LangChain
• Creating Chroma Vector Database Using LangChain
• Complete Workflow for Generating Responses from the Model

Module 9 - Exploring Pinecone Vector Database

• In-Depth Overview of Pinecone
• Setting Up Pinecone Account and Dashboard Overview
• Index Creation and Management in Pinecone
• Upserting and Querying Pinecone Index via Code
• Manual Querying in Pinecone Dashboard
• Using LangChain’s Pinecone Wrapper for Index Creation and Similarity Search
• Creating Retriever and Chain Objects with LLM for Response Generation
• Clean Up: Deleting Pinecone Index
• Challenge: Exploring Alternative Vector Databases

Module 10 - Selecting the Appropriate Vector Database

• Comparative Analysis of Vector Databases: A Decision Guide
• Criteria for Choosing the Right Database
• Making the Right Choice: Factors to Consider