Crafting ChatGPT-Powered AI Applications: A Comprehensive Dive into LangChain, OpenAI, and Streamlit
About the Course:
The course focuses on teaching participants about LangChain and OpenAI. It covers various topics related to LangChain, such as prompts, parsers, chains, agents, tools and memories, vector stores, document handling, building front-ends with Streamlit, and hands-on application development.
Course Objective:
This comprehensive course is designed to teach you how to QUICKLY harness the power of the LangChain library for LLM applications.
This course will equip you with the skills and knowledge necessary to develop cutting-edge LLM solutions for a diverse range of topics.
By the end of the course, participants should be able to develop real-world applications using LangChain, OpenAI, and Streamlit.
Who is the Target Audience?
- Software Engineers
- Backend Developers
- Fullstack engineers
- Data Scientist
- ML Engineers
- AI enthusiasts
Basic Knowledge:
- Python programming language
Understanding LangChain and OpenAI
- Introduction to LangChain: History and Role in AI Development
- OpenAI and the Power of Large Language Models (LLMs)
Prompts and Parsers in LangChain
- Introduction to Prompts and PromptTemplates
- Understanding Output Parsers
Deep Dive into Chains
- The Concept of Chains: SequentialChain, LLMChain, RetrievalQA Chain
- Creating Sequences of Operations
- Exploring Sequential Chains
LangChain Agents
- Introduction to Agents and Custom Agents
- Exploring the Powerful Emerging Development of LLM as Reasoning Agents
- LangChain Agents in Action
LangChain Tools and Memories
- Understanding LangChain Tools and Toolkits
- Memories for LLMs: Storing Conversations and Managing Limited Context Space
LangChain and Vectorstores
- Deep Dive into Vector stores
- Introduction to Vector Databases
- Splitting and Embedding Text Using LangChain
- Asking Questions (Similarity Search) and Getting Answers (GPT-4)
Document Handling in LangChain
- Understanding DocumentLoaders and TextSplitters
- Expanding LangChain Applications: Question Answering Over Documents
- Developing an LLM-Powered Question-Answering Application
- Building a Summarization System with LLMs
Building Front-ends with Streamlit
- Introduction to Streamlit for Powerful Web-based Front-ends
- Creating Front-ends for LLM and Generative AI Apps
- Exploring Streamlit: Main Concepts, Widgets, Session State, Callbacks
Hands-on Experience: Building Applications with LangChain, OpenAI, and Streamlit
- A Learning-by-Doing Experience
- Building Real-World LLM Applications Step-by-Step