LangChain Crash Course

Welcome to the LangChain Crash Course repository! This repo contains all the code examples you'll need to follow along with the LangChain Master Class for Beginners video. By the end of this course, you'll know how to use LangChain to create your own AI agents, build RAG chatbots, and automate tasks with AI.

Course Outline

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

Getting Started

Prerequisites

• Python 3.10 or 3.11
• Poetry (Follow this Poetry installation tutorial to install Poetry on your system)

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.

Acknowledgements

I would like to express my gratitude to Brandon Hancock, for providing the foundational codebase and learning resources that greatly facilitated my understanding and engagement with the project material. This repository is a fork of langchain-crash-course and includes modifications and extensions made by me as part of my learning process.