LangChain 101 (Jan 2024 update) - Virtual Edition: Kickoff 2024

## LangChain 101 - Virtual Edition: Kickoff 2024

## Introduction: Empowering AI Enthusiasts with LangChain

Over the past quarter, the Austin LangChain Users Group has been diligently crafting a series of lectures and hands-on labs with a clear goal: swiftly onboarding newcomers to the LangChain project and actively empowering them to participate in the AI revolution. Our focus has been to make these resources not only informative but also engaging and practical, ensuring that participants can immediately apply what they learn.
We have committed ourselves to this endeavor as a public good. All content, including our comprehensive labs, is freely accessible for personal or organizational use. We firmly believe in the philosophy that by improving our immediate world, we contribute to creating a better global community. This initiative is our contribution to that vision, offering a platform for learning and collaboration in the rapidly evolving field of AI and LangChain.

### Opening Remarks

• Topic: Introduction to the Austin LangChain 2024 Agenda
• Details: Overview of Austin LangChain User Group, and our 2024 plans

### LangChain Theory Session

• Topic: Basics of LangChain and Getting Started with AI Microservices
• Details: Foundational understanding of LangChain and its application in AI microservices.

### Hands-on Lab 1: Understanding and Interacting with a LangChain Agent

• Objective: Basic familiarity with a LangChain agent.
• Activity: Interaction and exploration of a pre-configured LangChain agent.

### Hands-on Lab 2: Creating a Simple AI Microservice with LangChain and Streamlit

• Objective: Develop a basic AI microservice.
• Activity: Building a simple AI microservice using LangChain, integrated with Streamlit for interface development.

### Hands-on Lab 3: Streamlit Deep Dive

• Objective: Discover the capabilities and ease of using Streamlit.
• Activity: This lab focuses on exploring the features of Streamlit, demonstrating its power and simplicity in creating user interfaces for AI applications. Participants will learn how to effectively utilize Streamlit's components to enhance their LangChain projects, gaining insights into building more interactive and visually appealing applications.

### Hands-on Lab 4: Developing a Chatbot Using Local LLMs

• Objective: Implement a chatbot with local Large Language Models.
• Activity: Creation of a chatbot utilizing local LLMs, focusing on deployment and customization.

### Browser-Based Accessibility

• Note: All labs and sessions are designed to be completed entirely within a web browser for easy access and participation.
• Note: An OpenAI API is required for three of these labs. You can get one here - https://platform.openai.com/api-keys