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LangChain Crash Course

LangChain exercises. This repo contains all the lang_chain examples to create:

  • AI agents
  • RAG chatbots
  • Automate tasks with AI

Exercise Outline

  1. Setup Environment
  2. Chat Models
  3. Prompt Templates
  4. Chains
  5. RAG (Retrieval-Augmented Generation)
  6. Agents & Tools

Getting Started

Prerequisites

Installation

  1. Clone the repository:

    <!-- TODO: UPDATE TO MY  -->
git clone https://github.com/bhancockio/langchain-crash-course
cd langchain-crash-course

  2. Install dependencies using Poetry:

    poetry install --no-root

  3. Set up your environment variables:

    • Rename the .env.example file to .env and update the variables inside with your own values. Example:
    mv .env.example .env

  4. Activate the Poetry shell to run the examples:

    poetry shell

  5. Run the code examples:

     python 1_chat_models/1_chat_model_basic.py

Repository Structure

Here's a breakdown of the folders and what you'll find in each:

1. Chat Models

  • 1_chat_model_basic.py
  • 2_chat_model_basic_conversation.py
  • 3_chat_model_alternatives.py
  • 4_chat_model_conversation_with_user.py
  • 5_chat_model_save_message_history_firestore.py

Learn how to interact with models like ChatGPT, Claude, and Gemini.

2. Prompt Templates

  • 1_prompt_template_basic.py
  • 2_prompt_template_with_chat_model.py

Understand the basics of prompt templates and how to use them effectively.

3. Chains

  • 1_chains_basics.py
  • 2_chains_under_the_hood.py
  • 3_chains_extended.py
  • 4_chains_parallel.py
  • 5_chains_branching.py

Learn how to create chains using Chat Models and Prompts to automate tasks.

4. RAG (Retrieval-Augmented Generation)

  • 1a_rag_basics.py
  • 1b_rag_basics.py
  • 2a_rag_basics_metadata.py
  • 2b_rag_basics_metadata.py
  • 3_rag_text_splitting_deep_dive.py
  • 4_rag_embedding_deep_dive.py
  • 5_rag_retriever_deep_dive.py
  • 6_rag_one_off_question.py
  • 7_rag_conversational.py
  • 8_rag_web_scrape_firecrawl.py
  • 8_rag_web_scrape.py

Explore the technologies like documents, embeddings, and vector stores that enable RAG queries.

5. Agents & Tools

  • 1_agent_and_tools_basics.py
  • agent_deep_dive/
    • 1_agent_react_chat.py
    • 2_react_docstore.py
  • tools_deep_dive/
    • 1_tool_constructor.py
    • 2_tool_decorator.py
    • 3_tool_base_tool.py

Learn about agents, how they work, and how to build custom tools to enhance their capabilities.

How to Use This Repository

  1. Watch the Video: Start by watching the LangChain Master Class for Beginners video on YouTube at 2X speed for a high-level overview.

  2. Run the Code Examples: Follow along with the code examples provided in this repository. Each section in the video corresponds to a folder in this repo.

  3. Join the Community: If you get stuck or want to connect with other AI developers, join the FREE Skool community here.

License

This project is licensed under the MIT License.