GPT Interviewer - Practice interview with AI interviewer based on job descriptions and resume

Start building practical applications that allow you to interact with data using LangChain and LLMs.

Docker-DB is a Python library for managing database containers via Docker: supporting fast, scriptable setup of MongoDB, PostgreSQL, MySQL, and SQL Server for development and testing.

This LLM enabled chatbot that helps answer customer questions for Paderno kitchen products on Canadian Tire Corporation website.

Successfully developed an LLM application which generates a summary, a list of citations and references and response to a user's query based on the research paper's content.

A comprehensive framework to create, test, and benchmark Retrieval-Augmented Generation (RAG) pipelines, supporting multiple architectures (e.g., Graph RAG and Agentic RAG), document splitters, embedding models, vectorstores, retrievers, rerankers, and LLM providers, with an interactive Gradio UI and experiment logging.

A lightweight Retrieval Augmented Generation pipeline using quantized small language models and efficient indexers built for edge-like, low‑memory, and low‑compute environments.

OpenAI Assistant Project: Learning to create assistant, vector store, scrape articles, and dockerize

A minimal, modular LangChain project showcasing key components like ChatPromptTemplate, output parsing, retriever setup, and LCEL chaining — ideal for building structured LLM pipelines and RAG systems.

Dive into LangChain, a powerful platform that lets you interact with your data like never before. This guide offers insights on its unique capabilities, helping you tap into your data in conversational ways.