Mastering LangChain JS Course Overview

Aug 10, 2024

Course on LangChain JS by Tom Chant

Introduction

  • Course Objective: To master LangChain JS, a revolutionary AI framework for building context-aware reasoning applications.
  • Developer: Tom Chant, course creator at Scrimba.
  • Language: Vanilla JavaScript and API integrations.
  • Prerequisites: Knowledge of working with APIs and vanilla JavaScript.
  • Platform: Accessible via interactive course on Scrimba, with downloadable code options.

What You Will Learn

  1. Text Processing & Vectorization: Basics of text processing and vectorization to advanced techniques.
  2. Embeddings & Vector Stores: Working with embeddings models and vector stores, such as Supabase.
  3. Templates & Prompts: Building templates & prompts using Lang Chain's expression language.
  4. Chains & Pipelines: Setting up chains and pipelines using Lang Chain's methods.
  5. Retrieving Data: Using the runnable sequence class for creating complex chains.
  6. Building Real-World Applications: Hands-on experience in building a context-aware chatbot.

Course Breakdown

Section 1: Introduction to LangChain

  • LangChain Overview: Framework for building context-aware reasoning applications.
  • LangChain JS: Version of LangChain focused on JavaScript community.
  • Objective: Build a chatbot that can answer questions based on a provided document.

Section 2: Setting Up and Splitting Data

  • Superbase Setup: Creating a project in Supabase, enabling PG Vector extension, and setting up a table for document storage.
  • Text Splitting: Using Recursive Character Text Splitter to split documents into chunks for embedding.
  • Chunk Size & Overlap: Adjusting chunk size and overlap for optimal performance.

Section 3: Creating Embeddings and Storing Vectors

  • OpenAI Embeddings: Using OpenAI embeddings model to create vectors from text chunks.
  • Storing Vectors: Uploading vectors to Supabase vector store.
  • Handling Embeddings: Understanding the embeddings model and its applications.

Section 4: Building the Chains

  • Standalone Question Chain: Creating a chain to convert user questions into standalone questions for better accuracy.
  • Pipe Method: Using the pipe method to chain prompts and models together.
  • String Output Passer: Ensuring the output is in the correct format for each subsequent step.

Section 5: Advanced Chains with Runnable Sequence

  • Runnable Sequence: Creating more complex chains using the runnable sequence method.
  • Passing Input Variables: Using runnable pass-through to retain original input across the chain.
  • Combining Chains: Breaking down into smaller chains and combining them into a main chain.

Section 6: Adding Conversation Memory

  • Conversation Memory: Storing conversation history to provide context in ongoing interactions.
  • Formatting Memory: Labeling and formatting conversation history for better AI understanding.
  • Wiring Memory to Chains: Integrating conversation history into chains for improved performance.

Section 7: Testing and Tuning the Chatbot

  • Prompt Engineering: Modifying prompts to fine-tune AI responses.
  • Performance Tweaks: Adjusting chunk size, overlap, and number of retrieved chunks for better results.
  • Model Settings: Experimenting with OpenAI settings like temperature and model versions.

Conclusion

  • Skills Acquired: Building scalable, context-aware AI applications using LangChain JS.
  • Community: Encouragement to join the Discord community and share progress.
  • Future Learning: Open invitation to reach out for feedback and further learning opportunities.

Final Note: Congratulations on completing the course and mastering LangChain JS! Reach out to Tom Chant on Twitter for feedback and further interaction.