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layout default
title LangChain Tutorial
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LangChain Tutorial: Building AI Applications with Large Language Models

LangChainView Repo is a powerful framework for building applications powered by large language models. Whether you're creating chatbots, question-answering systems, or complex AI workflows, LangChain provides the building blocks to chain together different components seamlessly.

Imagine you're building a smart assistant that can not only answer questions but also remember previous conversations, search through documents, and even take actions on your behalf. LangChain makes this possible by providing a standardized way to connect language models with other sources of data and functionality.

Mental Model

flowchart TD
    A[User Input] --> B[Prompt Template]
    B --> C[Language Model]
    C --> D[Output Parser]
    D --> E[Final Response]

    C --> F[Memory System]
    F --> B

    C --> G[Tool/Agent]
    G --> H[External API]
    H --> C

    I[Document Loader] --> J[Text Splitter]
    J --> K[Vector Store]
    K --> L[Retriever]
    L --> C

    classDef core fill:#e1f5fe,stroke:#01579b
    classDef data fill:#f3e5f5,stroke:#4a148c
    classDef external fill:#e8f5e8,stroke:#1b5e20

    class A,B,C,D,E core
    class I,J,K,L data
    class G,H external
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Why This Track Matters

LangChain is increasingly relevant for developers working with modern AI/ML infrastructure. Pydantic 2 Required: LangChain v0.3 fully migrated to Pydantic 2. Code using langchain_core.pydantic_v1 should be updated to native Pydantic 2 syntax, and this track helps you understand the architecture, key patterns, and production considerations.

This track focuses on:

  • understanding getting started with langchain
  • understanding prompt templates & chains
  • understanding memory systems
  • understanding document loading & processing

Chapter Guide

Welcome to your journey through LangChain! This tutorial is structured to take you from basic concepts to advanced implementations:

  1. Chapter 1: Getting Started with LangChain - Installation, basic setup, and your first LLM interaction
  2. Chapter 2: Prompt Templates & Chains - Creating reusable prompts and basic chaining
  3. Chapter 3: Memory Systems - Adding conversation memory to your applications
  4. Chapter 4: Document Loading & Processing - Working with PDFs, websites, and other data sources
  5. Chapter 5: Vector Stores & Retrieval - Implementing semantic search and RAG
  6. Chapter 6: Agents & Tools - Building autonomous agents that can take actions
  7. Chapter 7: Advanced Chains - Complex workflows and custom chain implementations
  8. Chapter 8: Production Deployment - Scaling, monitoring, and best practices

Current Snapshot (auto-updated)

What You Will Learn

By the end of this tutorial, you'll be able to:

  • Build conversational AI applications with memory and context
  • Implement Retrieval-Augmented Generation (RAG) systems
  • Create autonomous agents that can use tools and APIs
  • Process and analyze documents at scale
  • Deploy LangChain applications to production environments
  • Customize and extend LangChain components for your specific needs

Prerequisites

  • Python 3.9+ (Python 3.8 no longer supported as of v0.3)
  • Basic Python programming knowledge
  • Understanding of APIs and web services
  • Familiarity with command-line tools
  • (Optional) Experience with machine learning concepts

What's New in LangChain v0.3 (2024-2025)

Pydantic 2 Required: LangChain v0.3 fully migrated to Pydantic 2. Code using langchain_core.pydantic_v1 should be updated to native Pydantic 2 syntax.

Stars License: MIT Python

Simplified Tool Definition: New streamlined APIs for defining and using tools with LLMs.

Key changes in v0.3:

  • 🔄 Pydantic 2 Migration - Full upgrade from Pydantic 1 (EOL June 2024)
  • 📦 Integration Packages - Integrations moved to langchain-{name} packages
  • 🛠️ Chat Model Utilities - New tools for message trimming, filtering, and merging
  • 📊 Rate Limiter - Built-in rate limiting for API calls
  • 🎯 Custom Events - Dispatch and handle custom events in chains

Learning Path

🟢 Beginner Track

Perfect for developers new to AI and LangChain:

  1. Chapters 1-3: Core concepts and basic applications
  2. Focus on understanding LangChain's philosophy and basic patterns

🟡 Intermediate Track

For developers ready to build more complex applications:

  1. Chapters 4-6: Data processing, retrieval, and agents
  2. Learn to integrate external data sources and APIs

🔴 Advanced Track

For experienced developers building production systems:

  1. Chapters 7-8: Custom chains, deployment, and scaling
  2. Master advanced patterns and production best practices

Ready to start building with LangChain? Let's begin with Chapter 1: Getting Started!

Related Tutorials

Navigation & Backlinks

Generated by AI Codebase Knowledge Builder

Full Chapter Map

  1. Chapter 1: Getting Started with LangChain
  2. Chapter 2: Prompt Templates & Chains
  3. Chapter 3: Memory Systems
  4. Chapter 4: Document Loading & Processing
  5. Chapter 5: Vector Stores & Retrieval
  6. Chapter 6: Agents & Tools
  7. Chapter 7: Advanced Chains
  8. Chapter 8: Production Deployment
  9. Chapter 9: Evaluation, Monitoring, and Observability

Source References