Vector Database Certification Table of Contents


Table of Contents
 


 


Vector Databases Fundamentals

  • Introduction to Vector Databases - Full Overview
  • Why Vector Databases
  • Vector Databases - Benefits and Advantages

Traditional vs Vector Databases - Differences

  • Traditional vs Vector Databases - Limitations and Challenges
  • Vector Databases & Embeddings - Full Work Flow
  • Embeddings vs Vectors – Differences
  • Vector Databases - How They Work and Advantages
  • Vector Databases Use Cases
  • Vector and Traditional Databases - Summary

Vector Databases Solutions - Top 5 Vector Databases

  • The Top 5 Vector Databases - Overview
  • Understanding LLM (Large Language Models)

Building Vector Databases - Hands-on - Chroma Vector Database

  • Development Environment Setup
  • Setup VS-Code, Python and OpenAI API Key
  • Chroma Database workflow
  • Creating a Chroma Vector Database & Adding Documents & Querying them
  • Looping Through the Results & Showing Similarity Search Results
  • Chroma Default Embedding Function
  • Chroma Vector Database - Persisting Data and Saving
  • Creating an OpenAI Embeddings - Raw without Chroma
  • Using OpenAI's Embedding API to Create Embedding in Chroma
  • Vector Databases Metrics and Data Structures

Common Measures of Vector Similarity

  • Vector Similarity Deep Dive - Cosine Similarity
  • Euclidean Distance - L2 Norm
  • Dot Product

Vector Databases and LLM - the Full Workflow

  • Vector Databases and LLM - Deep Dive
  • Loading all Documents
  • Generating Embeddings from Documents & Insert them into Chroma Database
  • Getting the Relevant Chunks when Given a Query
  • Using OpenAI LLM to Generate Response - Full Flow

Vector Databases & the Langchain Framework

  • The LangChain Framework - Quick Overview
  • Getting started with LangChain and the OpenAIChat Wrapper
  • Loading Documents with LangChain Document Loader
  • Splitting the Documents with LangChain
  • Creating a Chroma Vector Database with LangChain
  • Getting the Response from the Model - the Complete Workflow

Pinecone Vector Database

  • Pinecone - Deep Dive
  • Create Pinecone Account & Dashboard Overview
  • Creating our Pinecone Index in Code
  • Upserting and Querying our Pinecone Index
  • Querying Pinecone Manually in the Dashboard
  • Using LangChain Pinecone Wrapper - Create Index and Upsert & Similarity Search
  • Creating a Retriever and Chain Objects & a LLM to get a Response
  • Clean up - Delete Pinecone Index
  • Challenge - Explore other Vector Database

Choosing the Right Vector Database

  • Choosing the Right Vector Database - Comparison Tables
  • Which Database Should I Choose?
  • Choosing the Right Database - Criteria

Apply for Certification

https://www.vskills.in/certification/vector-database-certification-course

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