This repository provides code examples for a beginner-friendly LangChain Master Class, enabling users to build AI agents, RAG chatbots, and automate tasks. It's targeted at developers new to LangChain, offering practical guidance through structured code and video content.

How It Works

The project demonstrates LangChain's core components: Chat Models for interacting with LLMs, Prompt Templates for structured input, Chains for sequential task execution, Retrieval-Augmented Generation (RAG) for context-aware responses using embeddings and vector stores, and Agents & Tools for dynamic task completion. This modular approach simplifies complex AI application development.

Quick Start & Requirements

• Install: Clone the repo, install dependencies with poetry install --no-root, set environment variables by renaming .env.example to .env, and activate the shell with poetry shell.
• Prerequisites: Python 3.10 or 3.11, Poetry.
• Resources: Requires API keys for LLM providers (e.g., OpenAI, Anthropic, Google).
• Docs: LangChain Master Class for Beginners video, Poetry installation tutorial.

Highlighted Details

• Covers foundational LangChain concepts from chat models to advanced RAG and agents.
• Includes deep dives into RAG components like text splitting, embeddings, and retrievers.
• Demonstrates building custom tools and ReAct agents.
• Features practical examples for saving chat history to Firestore.

Maintenance & Community

• Community support is available via a FREE Skool community.
• Contributions are welcome via GitHub issues and pull requests.

Licensing & Compatibility

• Licensed under the MIT License.
• Permissive licensing allows for commercial use and integration into closed-source projects.

Limitations & Caveats

The repository is designed as a learning resource and may not represent production-ready code. Specific LLM API keys and potentially other service configurations (like Firestore) are required for execution, necessitating external setup beyond the code itself.