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Generative AI Apps with LangChain and Python

Learn LangChain, Python, RAG, prompt engineering, and AI agents through hands-on projects designed for real deployment.

(GENAI-PYTHON.AU1) / ISBN : 979-8-90059-122-3
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About This Course

This course cuts through the hype, teaching you to build robust Generative AI applications with LangChain and Python. We'll start with integrating LLM APIs, then move to practical Q&A and chatbot construction. You'll explore various LLM models, master prompt engineering for effective outputs, and understand the critical role of LangChain Chains in complex workflows. We'll dive deep into Retrieval-Augmented Generation (RAG) for advanced search, then build and deploy real-world agents. Expect to confront common pitfalls like prompt injection and model hallucination, learning to mitigate them. This isn't about theoretical perfection; it's about building functional, deployable AI.

Skills You’ll Get

  • Architect and implement Generative AI applications using LangChain, integrating various LLM APIs effectively while managing API rate limits and cost implications.
  • Design and build robust Q&A systems and conversational chatbots, understanding the trade-offs between simple prompt-based and complex chain-driven interactions.
  • Master prompt engineering techniques, including few-shot prompting and output parsing, to control LLM behavior and mitigate common issues like hallucination or irrelevant responses.
  • Develop and deploy advanced agent-based applications and Retrieval-Augmented Generation (RAG) systems, navigating the complexities of document loading, text splitting, and vector store integration for enhanced accuracy.
Download Course Outline

1

Introduction to LangChain and LLMs

  • Understanding LangChain
  • Why Is LangChain Important?
  • Real-World Examples of LangChain
  • Integrating LLMs with LangChain
  • Exploring Core Components of LangChain
  • LLM Application Development Workflow
  • Key Takeaways
  • Looking Ahead
2

Integrating LLM APIs with LangChain

  • Understanding LLM APIs
  • Using Direct LLM API vs. LangChain
  • Preparing Your Dev Environment
  • Exercise 1: Calling an LLM API Directly
  • Key Takeaways
3

Building Q&A and Chatbot Apps

  • LangChain Framework Components
  • LangChain Ecosystem
  • Using LangChain Models with LLMs
  • Building a Simple Q&A Application
  • Building a Conversational App
  • Difference Between the Q&A and Chatbot Example
  • Error Handling and Troubleshooting
  • Development Playground
  • Maximize Your Learning Through Experimenting
  • Key Takeaways
4

Exploring Large Language Models (LLMs)

  • OpenAI’s Models
  • Google’s AI Model Overview
  • Anthropic’s Claude AI Models
  • Overview of Cohere AI Models
  • Meta AI Models
  • Key Learnings
5

Mastering Prompts for Creative Content

  • Importance of Prompt Engineering
  • Prompt Engineering Steps
  • Components of a Prompt
  • Few-Shot Prompt Template
  • Output Parsers
  • ChatPrompt Templates
  • Case Study: Streamlining Customer Service
  • Key Takeaways
6

Building Intelligent Chatbots and Automated Analysis Systems Using Chains

  • Introduction to LangChain Chains
  • Understanding the Components of Chains
  • Types of Chains
  • Difference Between LCEL and Legacy Chains
  • When to Use Different Types of Chains
  • Building with LCEL Chains
  • Types of LCEL Chains
  • Building with Legacy Chains
  • Types of Legacy Chains
  • Building Real-World Apps with Legacy Chains
  • More Complex Workflow Apps Using Chain Composition Strategies
  • Advanced Chain Techniques
  • Key Takeaways
7

Building Advanced Q&A and Search Applications Using Retrieval-Augmented Generation (RAG)

  • Importance of RAG
  • How Does RAG Work?
  • Document Loaders
  • Text Splitters
  • Vector Stores
  • Text Embedding Models
  • Retrievers
  • Indexing
  • Key Takeaways
  • References
8

Your First Agent App

  • Introduction
  • Why Agents Matter
  • Your First End-to-End Working Agent App
  • Key Takeaways
9

Building Different Types of Agents

  • Learning Objectives
  • Designing and Implementing an Agent
  • Agent Types
  • Key Takeaways
10

Projects: Building Agent Apps for Common Use Cases

  • Creating a Custom Agent
  • Practical Use Cases for Agents
  • Key Takeaways
11

Building and Deploying a ChatGPT-like App Using Streamlit

  • Setting Up Your Development Environment
  • Building the Streamlit LangChain UI App
  • Deploying the LangChain Application
  • Key Takeaways

1

Introduction to LangChain and LLMs

  • Building a Simple Generative App Using LangChain
2

Integrating LLM APIs with LangChain

  • Building a Real-Time Customer Service Chatbot
  • Building a Content Generation Platform with LangChain
  • Calling an LLM API Using Python
3

Building Q&A and Chatbot Apps

  • Using LangChain for a Retrieval Task
  • Building a Simple Q&A Application
  • Building a Conversational App Using Python
4

Exploring Large Language Models (LLMs)

  • Generating Custom Images from Text Prompts
5

Mastering Prompts for Creative Content

  • Creating a Custom Example Selector from Scratch
  • Crafting a Few-Shot Prompt Template for Question Answering
  • Building Structured Movie Data Using PydanticOutputParser
  • Creating a Chat Prompt Template
6

Building Intelligent Chatbots and Automated Analysis Systems Using Chains

  • Building and Executing LCEL Chains
  • Building Conversational Apps with ConversationChain
  • Building a Sentiment Analysis App Using Conditional Chains
  • Building a Data Summarization App with Sequential Chains
  • Handling Large Datasets with Chains
7

Building Advanced Q&A and Search Applications Using Retrieval-Augmented Generation (RAG)

  • Generating and Analyzing Text Embeddings
8

Your First Agent App

  • Building an End-to-End Agent Application
9

Building Different Types of Agents

  • Building a Structured Chat Agent
10

Projects: Building Agent Apps for Common Use Cases

  • Creating a Recommendation Engine Using LLM Agents

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LangChain is a framework designed to simplify the development of applications powered by large language models (LLMs). It's crucial because it provides modular components and chains to manage complex interactions, integrate external data sources, and build sophisticated applications like chatbots and agents more efficiently than direct API calls, despite its own learning curve.

This course covers integrating various LLM APIs, including those from OpenAI, Google, Anthropic, and Meta. You'll learn the practical steps for setting up your development environment, making direct API calls, and leveraging LangChain's abstractions to switch between different models, understanding the performance and cost trade-offs of each.

Mastering prompt engineering is critical for controlling LLM behavior. You'll learn to craft effective prompts, utilize few-shot templates, and implement output parsers to achieve desired responses, reduce hallucinations, and ensure your Generative AI applications deliver consistent, relevant, and structured outputs, avoiding common failure points of vague instructions.

Absolutely. The course dedicates significant sections to building advanced Q&A and search applications using Retrieval-Augmented Generation (RAG), and developing various types of agents. You'll learn to create custom agents for common use cases and deploy a ChatGPT-like application using Streamlit, understanding the practical challenges of deployment and scaling.

The course directly addresses these challenges through prompt engineering techniques, output parsers, and the implementation of Retrieval-Augmented Generation (RAG). You'll learn strategies to ground LLM responses in factual data, guide their behavior, and design systems that are more robust against generating incorrect or irrelevant information, acknowledging that complete elimination is often impractical.

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