💻
RAG and LLM Bootcamp
  • Welcome to the Bootcamp
    • Course Structure
    • Course Syllabus and Timelines
    • Know your Educators
    • Action Items and Prerequisites
    • Kick-Off Session for the Bootcamp
  • Basics of LLMs
    • What is Generative AI?
    • What is a Large Language Model?
    • Advantages and Applications of LLMs
    • Bonus Resource: Multimodal LLMs and Google Gemini
  • Word Vectors, Simplified
    • What is a Word Vector?
    • Word Vector Relationships
    • Role of Context in LLMs
    • Transforming Vectors into LLM Responses
    • Bonus: Overview of the Transformer Architecture
      • Attention Mechanism
      • Multi-Head Attention and Transformer Architecture
      • Vision Transformers (ViTs)
    • Bonus: Future of LLMs? | By Transformer Co-inventor
    • Graded Quiz 1
  • Prompt Engineering and Token Limits
    • What is Prompt Engineering
    • Prompt Engineering and In-context Learning
    • For Starters: Best Practices
    • Navigating Token Limits
    • Hallucinations in LLMs
    • Prompt Engineering Excercise (Ungraded)
      • Story for the Excercise: The eSports Enigma
      • Your Task fror the Module
  • RAG and LLM Architecture
    • What is Retrieval Augmented Generation (RAG)?
    • Primer to RAG: Pre-trained and Fine-Tuned LLMs
    • In-context Learning
    • High-level LLM Architecture Components for In-context Learning
    • Diving Deeper: LLM Architecture Components
    • Basic RAG Architecture with Key Components
    • RAG versus Fine-Tuning and Prompt Engineering
    • Versatility and Efficiency in RAG
    • Key Benefits of using RAG in an Enterprise/Production Setup
    • Hands-on Demo: Performing Similarity Search in Vectors (Bonus Module)
    • Using kNN and LSH to Enhance Similarity Search (Bonus Module)
    • Bonus Video: Implementing End-to-End RAG | 1-Hour Session
    • Graded Quiz 2
  • Hands-on Development
    • Prerequisites (Must)
    • Docker Basics
    • Your Hands-on RAG Journey
    • 1 – First RAG Pipeline
      • Building with Open AI
      • How it Works
      • Using Open AI Alternatives
      • RAG with Open Source and Running "Examples"
    • 2 – Amazon Discounts App
      • How the Project Works
      • Building the App
    • 3 – Private RAG with Mistral, Ollama and Pathway
      • Building a Private RAG project
      • (Bonus) Adaptive RAG Overview
    • 4 – Realtime RAG with LlamaIndex/Langchain and Pathway
      • Understand the Basics
      • Implementation with LlamaIndex and Langchain
  • Final Project + Giveaways
    • Prizes and Giveaways
    • Suggested Tracks for Ideation
    • Sample Projects and Additional Resources
    • Submit Project for Review
Powered by GitBook
On this page
  • 💡 A practical insight
  • How to Choose the Right Vector Embeddings Model
  1. Word Vectors, Simplified

Word Vector Relationships

PreviousWhat is a Word Vector?NextRole of Context in LLMs

Last updated 11 months ago

Navigating the landscape of text representation, it's essential to grasp how words relate to each other in vector form. In the upcoming video, Anup Surendran dives into the history of word vectors and takes a closer look at Google's groundbreaking Word2Vec project. Why are vector relationships so critical, and what biases do they bring?

Let's find out!

In this segment, Anup delves into the development of word vectors, highlighting the significant advancement made by Google's Word2Vec project. A remarkable aspect of Word2Vec is its ability to perform vector arithmetic, enabling mathematical operations with words. A classic example illustrating this feature is the equation "King - Man + Woman = Queen," demonstrating the intuitive understanding of relationships between words.

The video further examines how word vector relationships contribute to similarity searches, a crucial function in large language models. Additionally, Anup addresses an essential topic: the biases present in these technological advancements. Recognizing and understanding these biases is vital, not only for a deeper comprehension of Large Language Models but also for their ethical application. 🌐

💡 A practical insight

The terms "vector embeddings" and "word vectors" are often used interchangeably when discussing LLMs. These embeddings are stored in vector indexes, which are sophisticated data structures designed for fast and accurate retrieval of information based on these embeddings.

The rise of LLMs has spurred the development of specialized 'databases' focused on managing vector indexes. Platforms like Pinecone, Weaviate, ChromaDB, Mills etc., are prime examples of such databases.

However, these databases are not mandatory in every production-grade application of LLMs. We'll delve into this later in the course. For the moment, being familiar with these terms and their relevance to LLMs is a great starting point.

How to Choose the Right Vector Embeddings Model

Selecting the appropriate model for generating embeddings is an intriguing topic on its own. It's essential to recognize that there isn't a one-size-fits-all solution in this domain. A glance at this reveals a variety of embedding models, each tailored for specific applications. Currently, OpenAI's text-embedding-ada-002 is a commonly used ago-to model for producing efficient vector embeddings from diverse data, whether structured or unstructured. But like we've moved from GPT-2 to GPT-3, and now GPT-4, these embedding models are also bound to evolve and become more efficient. We'll delve deeper into its utilization in our tutorials by the end of this course.

MTEB Leaderboard on Hugging Face