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

This course will equip you with the skills and knowledge necessary to develop cutting-edge LLM solutions for a diverse range of topics.

Programming

6 Hours

Description

Step into the future of AI development with this immersive course, tailored for both budding AI enthusiasts and seasoned professionals. Navigate the intricate world of LangChain, empower your AI concepts with the intelligence of ChatGPT, and bring them to life using the dynamic capabilities of OpenAI and Streamlit.

Course Objectives

Our aim is to arm you with a holistic skill set, ensuring you are not just versed in theory, but also adept at practical application. By journey's end, you'll be equipped with the prowess to: Architect and develop AI applications with a deep understanding of ChatGPT's potential. Seamlessly integrate the LangChain library for robust LLM solutions, catering to a multitude of needs. Build interactive and user-centric front-ends using Streamlit, enhancing user experience and engagement. Transform ideas into impactful real-world applications, pushing the boundaries of AI innovation. Join us, and take your AI aspirations from concept to reality, shaping the future of intelligent applications.

Target Audience

 Software Engineers

Backend Developers

Fullstack engineers

Data Scientist

ML Engineers

AI enthusiasts

Basic Understanding

Python programming language

Course Content

No sessions available.

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

Session 1: Understanding LangChain and OpenAI

  1. Introduction to LangChain: History and Role in AI Development
  2. OpenAI and the Power of Large Language Models (LLMs)

Session 2: Prompts and Parsers in LangChain

  1. Introduction to Prompts and PromptTemplates
  2. Understanding Output Parsers

Session 3: Deep Dive into Chains

  1. The Concept of Chains: SequentialChain, LLMChain, RetrievalQA Chain
  2. Creating Sequences of Operations
  3. Exploring Sequential Chains

Session 4: LangChain Agents

  1. Introduction to Agents and Custom Agents
  2. Exploring the Powerful Emerging Development of LLM as Reasoning Agents
  3. LangChain Agents in Action

Session 5: LangChain Tools and Memories

  1. Understanding LangChain Tools and Toolkits
  2. Memories for LLMs: Storing Conversations and Managing Limited Context Space

Session 6: LangChain and Vectorstores

  1. Deep Dive into Vectorstores
  2. Introduction to Vector Databases
  3. Splitting and Embedding Text Using LangChain
  4. Asking Questions (Similarity Search) and Getting Answers (GPT-4)

Session 7: Document Handling in LangChain

  1. Understanding DocumentLoaders and TextSplitters
  2. Expanding LangChain Applications: Question Answering Over Documents
  3. Developing an LLM-Powered Question-Answering Application
  4. Building a Summarization System with LLMs

Session 8: Building Front-ends with Streamlit

  1. Introduction to Streamlit for Powerful Web-based Front-ends
  2. Creating Front-ends for LLM and Generative AI Apps
  3. Exploring Streamlit: Main Concepts, Widgets, Session State, Callbacks

Session 9: Hands-on Experience: Building Applications with LangChain, OpenAI, and Streamlit

  1. A Learning-by-Doing Experience
  2. Building Real-World LLM Applications Step-by-Step

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