🦜⛓️ LangChain Learning Hub

A comprehensive collection of LangChain tutorials, examples, and implementations — from basic concepts to advanced AI agent workflows.✨ Curated and built by Sourabh Kumar with ❤️ as part of my hands-on journey exploring the real-world power of LangChain and open-source LLMs.

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📖 About This Repository

This repository is a learning and experimentation hub for LangChain, designed to guide you from the basics to production-ready AI systems.

It covers:

• 🧱 Core components of LangChain
• 🤖 LLM integrations (OpenAI, Hugging Face, Anthropic, Google Gemini, etc.)
• 🔗 Chains, Agents, and Tools
• 🔍 RAG systems (Retrieval-Augmented Generation)
• 🛠️ Production practices (error handling, monitoring, optimization)

If you want to learn LangChain hands-on or bootstrap your own AI projects, this repo is for you.

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🏗️ Repository Structure

LangChain/
├── 📚 Foundation
│   ├── 01_Introduction_to_LangChain.md
│   └── 02_LangChain_Components.md
│
├── 🤖 Models & Integration
│   ├── 03_LangChain_Models/
│   ├── 04_Prompts_in_LangChain/
│   ├── 05_Structured_Outputs/
│   └── 06_Output_Parsers/
│
├── ⛓️ Chains & Workflows
│   ├── 07_Chains/
│   └── 08_Runnables/
│
├── 🔍 RAG Implementation
│   ├── 09_Document_Loader/
│   ├── 10_Text_Splitters/
│   ├── 11_Vector_Store/
│   ├── 12_Retrievers/
│   └── 13_RAG/
│
├── 🛠️ Advanced Features
│   ├── 14_Tools_in_LangChain/
│   └── 15_Agents/
│       └── 01_web_search_agent.ipynb
│
└── 📋 Configuration
    ├── requirements.txt
    ├── pyproject.toml   # Managed by uv
    ├── .env.example
    └── README.md

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🚀 Quick Start

🔑 Prerequisites

• Python 3.10+

• uv package manager (recommended over pip)

• API keys for providers:

  • OPENAI_API_KEY (OpenAI & GPT-OSS-20B)
  • HUGGINGFACEHUB_API_TOKEN
  • ANTHROPIC_API_KEY
  • GOOGLE_API_KEY
  • (Optional) SERPAPI_API_KEY or DUCKDUCKGO for search tools

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⚡ Installation

1. Clone the repository

   git clone https://github.com/Sourabh-Kumar04/LangChain.git
   cd LangChain

2. Install dependencies using uv

   uv venv
   source .venv/bin/activate   # Linux/Mac
   .venv\Scripts\activate      # Windows
   
   uv pip install -r requirements.txt

3. Set environment variables

   cp .env.example .env
   # Add your API keys in .env

4. Run Jupyter

   uv pip install jupyter
   jupyter notebook

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🤖 LLM Integrations

This repo demonstrates multiple LLM providers:

• OpenAI → gpt-4, gpt-3.5
• Hugging Face → openai/gpt-oss-20b
• Anthropic Claude → claude-3.5-sonnet
• Google Gemini → gemini-2.5-flash
• Local Models via transformers + langchain

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📚 Learning Path

• 🌱 Beginner → Foundations, prompts, basic chains
• 🔧 Intermediate → RAG, structured outputs, multi-step workflows
• 🎯 Advanced → Agents, multi-LLM orchestration, production best practices

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🎮 Example: Hugging Face gemini-2.5-flash Agent

from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.agents import create_react_agent, AgentExecutor
from langchain_core.tools import tool
from langchain_community.tools import DuckDuckGoSearchRun
from langchain_core.messages import HumanMessage
from langchain import hub

from dotenv import load_dotenv
import requests

load_dotenv()

# Hugging Face model
llm = ChatGoogleGenerativeAI(
    model="gemini-2.5-flash",              # or "gemini-1.5-pro" if you have access
    temperature=0,
    convert_system_message_to_human=True
)

# Web search tool
search = DuckDuckGoSearchAPIWrapper()
tools = [Tool(name="WebSearch", func=search.run, description="Search the internet")]

# Initialize agent
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
print(agent.run("3 ways to reach Goa from Delhi"))

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📋 Requirements

Core

langchain>=0.1.0
python-dotenv
jupyter

LLMs

openai
huggingface_hub
anthropic
google-generativeai

Tools

duckduckgo-search
google-search-results
faiss-cpu
chromadb
pypdf

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🛠️ Development

uv pip install -r requirements-dev.txt
pre-commit install
pytest
black .
isort .
flake8 .

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📈 Roadmap

• ✅ LangChain basics
• ✅ Hugging Face OSS LLMs (openai/gpt-oss-20b)
• ✅ Agent workflows
• 🚧 LangGraph orchestration
• 🚧 Advanced RAG (re-ranking, citations)
• 🚧 Production deployment (Docker, Kubernetes, cloud)

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👤 Author

Hey 👋, I’m Sourabh Kumar — a CS undergrad at the University of Delhi. I’m passionate about AI, LangChain, and open-source LLMs and this repo is a way of documenting + sharing my journey with the community.

• 🐙 GitHub/Sourabh-Kumar04
• 💼 LinkedIn/sourabh-kumar04
• 💌 Always open to collaboration, feedback, or just a good tech chat!

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⭐ Support

If this repo resonates with you or saves you time:

• ⭐ Star it (makes my day 🌟)
• 🍴 Fork it (use it as your learning base)
• 🐛 Contribute (PRs welcome)

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💡 “Learning is best when shared — this repo is my small contribution to make LangChain approachable for everyone.”

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