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Zero To Mastery

Developing LLM Apps with LangChain

via Zero To Mastery

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Overview

This byte-sized course will teach you how to build state-of-the-art LLM-powered applications with LangChain, Pinecone, and Python! This is the perfect way to start learning about the world of AI application development.
  • How to integrate external data sources, including books and documents, into LLMs like GPT for enhanced question-answering capabilities beyond their initial training data
  • The fundamentals of building LLM-powered applications using the LangChain framework, enabling you to harness the full potential of AI in custom projects
  • Techniques for leveraging Pinecone and OpenAI with Python to create sophisticated Q&A applications, enhancing your technical skill set and understanding of modern AI tools
  • Step-by-step guidance on deploying scalable and performant AI applications, preparing you to create a portfolio-worthy project in just 3 hours

Syllabus

  •   Introduction
    • Introduction
    • Byte FAQ
    • Course Resources
    • Exercise: Meet Your Classmates and Instructor
    • Set Your Learning Streak Goal
  •   Deep Dive into LangChain
    • Introduction to LangChain
    • Setting Up the Environment: LangChain, Python-dotenv
    • ChatModels: GPT-3.5-Turbo and GPT-4
    • Caching LLM Responses
    • LLM Streaming
    • Prompt Templates
    • ChatPromptTemplate
    • Simple Chains
    • Sequential Chains
    • Introduction to LangChain Agents
    • LangChain Agents in Action: Python REPL
    • LangChain Tools: DuckDuckGo and Wikipedia
    • Creating a React Agent
    • Testing the React Agent
  •   LangChain and Vector Stores (Pinecone)
    • Short Recap of Embeddings
    • Introduction to Vector Databases
    • Authenticating to Pinecone
    • Working with Pinecone Indexes
    • Working with Vectors
    • Namespaces
    • Splitting and Embedding Text Using LangChain
    • Inserting the Embeddings into a Pinecone Index
    • Asking Questions (Similarity Search)
  •   Project: RAG - Q&A Application on Your Private Documents (Pinecone & Chroma)
    • Project Introduction
    • Loading Your Custom (Private) PDF Documents
    • Loading Different Document Formats
    • Public and Private Service Loaders
    • Chunking Strategies and Splitting the Documents
    • Note: LangChain and Pinecone Library Versions
    • Embedding and Uploading to a Vector Database (Pinecone)
    • Asking and Getting Answers
    • Using Chroma as a Vector DB
    • Adding Memory to the RAG System (Chat History)
    • Using a Custom Prompt
  •   Where To Go From Here?
    • What's Next?
    • Review This Project!

Taught by

Andrei Dumitrescu

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