02-langchain-beginners

A comprehensive, hands-on guide to mastering LangChain fundamentals through practical Jupyter notebooks.

This repository is the second module in a progressive learning series on Generative AI and LangChain, designed to take you from basic concepts to building production-ready RAG (Retrieval-Augmented Generation) systems.

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📚 Overview

This course provides a structured, step-by-step introduction to LangChain using modern patterns and tools:

• Progressive Learning: 15 notebooks covering everything from prompt templates to vector stores
• Modern Stack: Built with LCEL (LangChain Expression Language), OpenAI, FAISS, Chroma
• Practical Examples: Each concept includes working code and real-world applications
• Production-Ready: Learn patterns used in professional LLM applications

Perfect for developers, data scientists, and AI enthusiasts who want to build robust LLM-powered applications.

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📚 Course Structure

Notebook	Topic	Description
00	API Keys & Environment Setup	Secure management of credentials and configuration
01	Fundamentals Concepts	Core LLM ideas: context, tokens, prompts, hallucinations
02	LLM App Architecture	Understanding how LLM-based applications are structured
03	Load Env & Basic Model	Load environment variables and call OpenAI / Groq models
04	Prompt Templates	Build and format prompts with PromptTemplate and ChatPromptTemplate
05	Chains	What is a chain? Build your first LCEL pipeline
06	Chain Types	Explore RunnablePassthrough, RunnableLambda, and RunnableParallel
07	Built-in Functions	Using .bind(), .bind_tools(), and .assign() for modular logic
08	Composing Chains	Nested and dependent chains (fan-out / fan-in)
09	RAG Introduction	Conceptual introduction to Retrieval-Augmented Generation
10	Data Loaders	Loading data with LangChain’s community loaders
11	Text Splitters	Chunking large documents with RecursiveCharacterTextSplitter
12	Text Embeddings	Converting text into vector embeddings
13	Vector Stores	Storing and querying embeddings with Chroma or FAISS
14	VectorStore Retriever	Using vector stores as retrievers for semantic search

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🚀 Getting Started

Prerequisites

• Python: 3.10, 3.11, or 3.12
• uv: Fast Python package installer (install guide)
• OpenAI API Key: Required for running examples (get one here)

Installation

1. Clone the repository:

   git clone https://github.com/JaimeLucena/02-langchain-beginners.git
   cd 02-langchain-beginners

2. Install dependencies:

   uv sync

3. Set up environment variables:

   Create a .env file in the root directory:

   OPENAI_API_KEY=your_openai_api_key_here
   GROQ_API_KEY=your_groq_api_key_here  # Optional

4. Launch Jupyter:

   uv run jupyter lab

5. Start with notebook 00 and work through them sequentially.

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🛠️ Technology Stack

• LangChain Core: >=0.3.0 (modern LCEL patterns)
• LLM Providers: OpenAI, Groq (optional OSS models)
• Vector Databases: FAISS, ChromaDB
• Data Processing: LangChain text splitters and community loaders

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📖 Learning Path

Phase 1: Foundations (Notebooks 00-04)

Learn the basics of prompts, templates, and environment management.

Phase 2: Chains & Composition (Notebooks 05-08)

Master LCEL chains, parallel execution, and advanced composition patterns.

Phase 3: RAG Systems (Notebooks 09-14)

Build complete retrieval-augmented generation pipelines with vector stores.

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🎯 What You'll Build

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

• ✅ Create dynamic prompt templates with ChatPromptTemplate
• ✅ Build and compose LCEL chains for complex workflows
• ✅ Implement RAG systems with semantic search
• ✅ Use vector stores (FAISS/Chroma) for efficient document retrieval
• ✅ Structure production-ready LLM applications
• ✅ Apply modern LangChain patterns and best practices

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📋 Prerequisites Knowledge

This is the second course in the series. You should be comfortable with:

• Python fundamentals (covered in 01-python-fundamentals)
• Basic understanding of APIs and JSON
• Familiarity with Jupyter notebooks

No prior LLM or LangChain experience required!

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🤝 Contributing

Found a bug or have a suggestion? Contributions are welcome!

1. Fork the repository
2. Create a feature branch (git checkout -b feature/improvement)
3. Commit your changes (git commit -am 'Add new example')
4. Push to the branch (git push origin feature/improvement)
5. Open a Pull Request

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📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

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🔗 Related Resources

• LangChain Documentation
• LangChain Python API Reference
• LangGraph Documentation
• OpenAI API Reference

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👨‍💻 Author

Jaime Lucena

• GitHub: @JaimeLucena

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⭐ Support

If you find this course helpful, please consider giving it a star! ⭐

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Ready to master LangChain? Start with notebook 00 and begin your journey! 🚀