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LangChain Certification Course cover image

LangChain Certification Course
Amit Diwan

Learn LangChain, its components, and how it can be used with RAG to set up a QA chain.

Summary

Price
£49 inc VAT
Study method
Online, On Demand 
Duration
0.6 hours · Self-paced
Qualification
No formal qualification
Certificates
  • Certificate of Completion - Free
  • Reed Courses Certificate of Completion - Free
Additional info
  • Tutor is available to students

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Overview

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.

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

Certificates

Curriculum

3
sections
14
lectures
0h 38m
total
    • 1: About Course 00:40
    • 2: LangChain - Introduction, Features, and Use Cases 04:20
    • 3: What is Chaining in LangChain 01:42
    • 4: Components/ Modules of LangChain 02:59
    • 5: Preprocessing Component of LangChain 01:42
    • 6: Models Component of LangChain 01:57
    • 7: Prompts Component of LangChain 01:59
    • 8: Memory Component of LangChain 01:38
    • 9: Chains Component of LangChain 01:31
    • 10: Indexes Component of LangChain 01:57
    • 11: Agents Component of LangChain 01:49
    • 12: LangChain with RAG - Workflow 01:25
    • 13: LangChain with RAG - Process 02:56
    • 14: LangChain with RAG – Final Coding Example 10:47

Description

Learn LangChain and its components. Implement LangChain with RAG to understand the workflow and set up a Question-Answering chain.

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

  • LangChain - Introduction, Features, and Use Cases
  • What is Chaining in LangChain

LangChain – Components

  • Components/ Modules of LangChain
  • Preprocessing Component of LangChain
  • Models Component of LangChain
  • Prompts Component of LangChain
  • Memory Component of LangChain
  • Chains Component of LangChain
  • Indexes Component of LangChain
  • Agents Component of LangChain

LangChain with RAG

  • LangChain with RAG - Workflow
  • LangChain with RAG - Process
  • LangChain with RAG – Final Coding Example

Note: Downloadable code attached in the last lesson

Who is this course 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

Requirements

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

Career path

  1. The average salary for an AI Prompt Engineer is £61,801 per year in the United Kingdom (Glassdoor stats).
  2. The average salary for an AI Prompt Engineer is $1,50,181 per year in the US (Glassdoor stats).

Questions and answers

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FAQs

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