LangChain Learning Lab

A hands-on repository containing my practice, experiments, and learning implementations using LangChain and LLM application development concepts.

Topics Covered

• Chains
• Agents
• Prompt Templates
• Output Parsers
• Runnables
• Tool Calling
• RAG (Retrieval-Augmented Generation)
• Retrievers
• Vector Stores
• Document Loaders
• Text Splitters
• Structured Output
• Models & Integrations

Repository Structure

langchain-learning-lab/
│
├── agents/
├── chains/
├── models/
├── outputparsers/
├── prompts/
├── rag/
├── retrievers/
├── runnables/
├── toolcalling/
├── vectorstore/
├── langchain-document-loaders-main/
├── textsplittermain/
└── langchainstructuredoutput/

Purpose

This repository is part of my learning journey in:

• LLM application development
• AI engineering
• LangChain framework
• Prompt engineering
• Retrieval systems
• AI agent workflows

Tech Stack

• Python
• LangChain
• OpenAI API
• Vector Databases
• RAG Pipelines

Notes

This repository contains practice implementations, experiments, and concept-based examples created while learning LangChain and modern AI application development.

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