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LangChain Crash Course
Rating: 4.8 out of 5(5 ratings)
159 students

LangChain Crash Course

Learn LangChain, its components, and how it can be used with RAG to set up a QA chain for summarizing documents.
Created byStudyopedia Trainings
Last updated 4/2025
English
English [Auto],

What you'll learn

  • Learn LangChain from scratch
  • Understand the LangChain workflow
  • Summarize multiple PDF documents with LangChain and RAG
  • Understand chaining in LangChain
  • Get to know the LangChain components with examples
  • Load and parse the PDF documents
  • Split documents into chunks
  • Setup the embedding models
  • Learn to create a vector store from the document chunks
  • Setup a local LLM
  • Learn to create a QA chain

Course content

3 sections • 14 lectures • 37m total length

  • About Course0:40

    Explore link chain, a framework for building applications powered by large language models, with tools to integrate llms for question answering, text generation, rag, and chatbots.

  • LangChain - Introduction, Features, and Use Cases4:20

    Explore LangChain's features, including chaining of llms and processes, memory, rag (retrieval augmented generation), vector stores, agents, and prompts, to build context-aware, reasoning-based AI applications.

  • What is Chaining in LangChain1:42

    Understand chaining in LangChain as linking multiple components into a sequence to complete a task. Leverage context from previous steps and combine retrieval with generation to form retrieval augmented workflows.

Requirements

  • A computer with an Internet
  • You should be able to use a web browser at a beginner level

Description

Welcome to the LangChain course. LangChain is a framework designed to build applications powered by large language models (LLMs). It provides tools and abstractions to make it easier to integrate LLMs into applications, enabling tasks like question answering, text generation, retrieval-augmented generation (RAG), chatbots, and more.

LangChain – Use Cases

Here are some of the use cases of LangChain:

  1. Question Answering: Build systems that answer questions by retrieving relevant information and generating answers using LLMs.

  2. Chatbots: Create conversational agents that can maintain context across interactions.

  3. Retrieval-Augmented Generation (RAG): Combine retrieval of relevant documents with text generation for more accurate and context-aware responses.

  4. Text Summarization: Generate summaries of long documents or articles.

  5. Code Generation: Build tools that generate code based on natural language descriptions.

  6. Personal Assistants: Create virtual assistants that can perform tasks like scheduling, email drafting, or information retrieval.

Course Lessons

LangChain – Introduction

1. LangChain - Introduction, Features, and Use Cases

2. What is Chaining in LangChain

LangChain – Components

3. Components/ Modules of LangChain

4. Preprocessing Component of LangChain

5. Models Component of LangChain

6. Prompts Component of LangChain

7. Memory Component of LangChain

8. Chains Component of LangChain

9. Indexes Component of LangChain

10. Agents Component of LangChain

LangChain with RAG

11. LangChain with RAG - Workflow

12. LangChain with RAG - Process

13. LangChain with RAG - Final Coding Example

Who this course is for:

  • Those who want to begin their AI journey
  • Beginner AI Enthusiasts
  • Learn LangChain with RAG
  • Those who want to understand chaining in LangChain
  • Those who want to summarize multiple PDF documents

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