mz0in / intro-llm-rag

LLM Models and RAG Hands-on guide

TOC

This guide is primarily for technical teams engaged in developing a basic conversational AI with RAG solutions. It offers a basic introduction to the technical aspects. This guide helps anyone with basic technical background to get involved in the AI domain. This guide combines between the theoretical, basic knowledge and code implementation. It's important to note that most of the content is compiled from various online resources, reflecting the extensive effort in curating and organizing this information from numerous sources.

• intro
  • What is Conversational AI?
  • The Technology Behind Conversational AI
  • LLM Basics
  • What is a large language model (LLM)?
  • How do LLMs work?
  • What are the Relations and Differences between LLMs and Transformers?
  • What are Pipelines in Transformers?
  • What are Hugging Face Transformers?
  • Chains
  • What are chains?
  • Foundational chain types in LangChain
  • LLMChain
  • Creating an LLMChain
  • Sequential Chains
  • SimpleSequentialChain
  • SequentialChain
  • Transformation
  • Prompt Engineering
  • What is Prompt Engineering?
    • Prompt
    • Types of Prompts
    • Instruction Prompting
    • Role Prompting
    • “Standard” Prompting
    • Chain of Thought (CoT) Prompting
    • Recommendations and Tips for Prompt Engineering with OpenAI API
  • Embeddings
    • A problem with semantic search.
    • What are embeddings?
    • What is a vector in machine learning?
    • How do embeddings work?
    • How are embeddings used in large language models (LLMs)?
  • Vector Stores
    • What Are Vector Databases?
    • The Benefits of Using Open Source Vector Databases
    • Open Source Vector Databases Comparison: Chroma Vs. Milvus Vs. Weaviate
      • 1. Chroma
      • 2. Milvus
      • 3. Weaviate
      • 4.Faiss
  • Chunking
    • Document Splitting
    • Chunking Methods
    • Character Splitting
    • Recursive Character Text Splitting
    • Split by Tokens
      • Tiktoken Tokenizer
      • Hugging Face Tokenizer
      • Other Tokenizer
    • Things to Keep in Mind
  • Quantization
    • What is Quantization?
    • How does quantization work?
    • Hugging Face and Bitsandbytes Uses
    • Loading a Model in 4-bit Quantization
    • Loading a Model in 8-bit Quantization
    • Changing the Compute Data Type
    • Using NF4 Data Type
    • Nested Quantization for Memory Efficiency
    • Loading a Quantized Model from the Hub
    • Exploring Advanced techniques and configuration
  • Temperature
    • Top P and Temperature
    • Temperature
    • Top p
    • Token length
    • Max tokens
    • Stop tokens
  • Langchain Memory
    • What is Conversational memory?
    • ConversationChain
    • Forms of Conversational Memory
      • ConversationBufferMemory
      • ConversationSummaryMemory
      • ConversationBufferWindowMemory
      • ConversationSummaryBufferMemory
      • Other Memory Types
  • Agents & Tools
    • Tools
    • Agents
    • Chains
    • Memory
    • Callback Handlers
  • Walkthrough — Project Utilizing Langchain
  • RAG
    • The Curse Of The LLMs
    • The Challenge
    • What is RAG?
    • How does RAG help?
    • New RAG techniques
• groq
  • What is groq?
  • What is LPU?
  • How Groq's LPU Works
  • How LPU is different from GPU
  • Groq Tools
  • Groq and RAG Architecture Example
• What is LlamaParse ?
• Use Case – 1
  • implementation-1-A4000
  • implementation-2-A100
  • implementation-3-groq
  • implementation-4-llama3-A4000
  • benchmark
• Use Case – 2
  • Action integration with chatbot (google calendar booking)
• Source Code