Duration: x Days

Description

This course introduces LangChain and LlamaIndex, two powerful tools for building advanced language model applications. Over three days, participants will learn how to leverage these frameworks to integrate large language models (LLMs) into dynamic applications, automate workflows, and enhance model performance by working with structured data sources. The course covers LangChain’s modular approach to building language-based apps and LlamaIndex’s ability to connect LLMs with external data sources, making them practical for real-world applications.

Audience

This course is designed for developers, data scientists, and technical professionals interested in building advanced language model applications using LangChain and LlamaIndex. It’s ideal for those seeking to create intelligent, context-aware tools such as chatbots, summarization pipelines, and information retrieval systems. The course offers a practical, hands-on exploration of chaining language model capabilities with external data sources and APIs. Participants will gain the skills to integrate these tools into scalable, production-ready workflows.

Objectives

  • Understand LangChain and LlamaIndex and their roles in LLM-based applications
  • Build pipelines using LangChain for text generation, summarization, and QA
  • Integrate LLMs with external data using LlamaIndex
  • Optimize LLM performance with chaining, memory, and context-aware techniques
  • Deploy and test LLM-powered applications in practical scenarios

Prerequisites

Participants should have at least six months of hands-on experience with Python programming. A basic understanding of machine learning concepts and large language models is expected. Familiarity with natural language processing (NLP) libraries such as Hugging Face is recommended to get the most out of the course. Most importantly, learners should have a strong interest in building applications powered by language models.

Course Outline

Module 1: Overview of LangChain

  • What is LangChain? How Does It Support IT-Based Applications?
  • Core Modules: Chains, Memory, Agents, and DataConnectors
  • Key Use Cases: Text Generation, QA, and Summarization

Module 2: Setting Up the LangChain Environment

  • Installing and Setting Up LangChain in Python
  • Overview of LangChain’s Architecture: Pipelines, Input/Output Management
  • Basic Building Blocks: Prompts, Chains, and Agents

Module 3: Building Simple Chains

  • Creating a Basic Text Generation Chain with OpenAI or GPT Models
  • Using Chains to Combine Multiple Tasks
  • Automating Text Summarization Using LangChain

Module 4: Building a Summarization Pipeline

  • Setting Up a Summarization Workflow Using LangChain
  • Testing the Pipeline with Sample Documents
  • Modifying the Chain to Enhance Performance and accuracy

Module 5: Memory and Context Management in LangChain

  • Understanding LangChain Memory: Stateful Interactions with Language Models
  • Implementing Persistent Memory to Retain Context Across Multiple Interactions
  • Best Practices for Building Context Aware Application

Module 6: Agents and Tool Use in LangChain

  • What Are LangChain Agents? How to Design Agents for Complex Tasks
  • Using External Tools and APIs with Agents
  • Building Intelligent Systems that Combine Agents with Language Models

Module 7: Combining LangChain with Other NLP Libraries

  • Integrating Hugging Face Transformers with LangChain Workflows
  • Using LangChain with Other Python Libraries
  • Building a Custom Chatbot Using Multiple Agents and Memory

Module 8: Building a Contextual Chatbot

  • Designing a Chatbot that Remembers User Interactions
  • Integrating External APIs and Tools to Extend Functionality
  • Optimizing the Bot for Improved Conversation Flow

Module 9: Overview of LlamaIndex

  • What is LlamaIndex? How Does it Enhance LLMs?
  • Indexing and Querying Data Sources
  • Common Applications: QA Systems, Document Retrieval, and Structured Data Integration

Module 10: Building Indexes with LlamaIndex

  • Creating Indexes from Structured and Unstructured data
  • Querying Large Datasets with LlamaIndex for Real-Time Answers
  • Optimizing LlamaIndex for Fast and Accuract Data Retrieval

Module 11: Combining LangChain and LlamaIndex

  • How LangChain and LlamaIndex Complement Each Other in Building LLM Applications
  • Creating an Information Retrieval System Using LangChain and LlamaIndex
  • Indexing, Querying, and Generating Answers

Module 12: Building a Custom Information Retrieval System

  • Indexing a Set of Documents or a Database Using LlamaIndex
  • Designing a LangChain workflow that queries and retrieves information dynamically
  • Testing and Deploying the System in a Real-World Scenario