LangChain, OpenAI, and Streamlit : Build Real-World Next-Gen LLM Apps
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, vectorstores, 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 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 Vectorstores
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