🤖 vs-code extension that suggest commit messages by AI & context

A collection of LangChain pipelines using Groq API with LLaMA and DeepSeek models – featuring sequential, parallel, conditional, and simple chains.

A starter repo on how to create your first simple LLM application using LangChain

An intelligent chatbot that allows users to upload text-based Ayurveda PDFs and ask questions based on the content using RAG (Retrieval-Augmented Generation) combining semantic search and LLM-based responses.

In this project I've built a streamlit web app that leverages the LLAMA3.1 LLM to create an AI-powered blog content

AI chatbot powered by Groq (Llama 3.3 70B) + LangChain + Streamlit with personality switching, conversation history, and a modern dark UI.

FastMail is an AI-powered email management tool designed to streamline email responses

AI cold email generator · paste a job URL + upload your resume → get a personalized cold email · LangChain · Llama 3.1 70B · Groq · Streamlit

Local RAG Pipeline – "Where Did I Put That File?". This project is a local Retrieval-Augmented Generation (RAG) pipeline built to help users locate files and folders using natural language.

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App Builder is an AI-powered coding assistant built with LangGraph that works like a multi-agent developer team. It converts natural language requests into complete, functional projects — file by file. Using Planner, Architect, and Coder agents, it plans, designs, and writes code just like a real developer.

A Streamlit web application that lets you upload PDF documents and have a conversational Q&A session with their content. Built using Retrieval-Augmented Generation (RAG) with LangChain, Groq, and ChromaDB.

This project combines the power of vector databases, large language models, and chat history management to create an interactive PDF chatbot

A Streamlit-based application that extracts key financial metrics — Revenue (actual & expected) and EPS (actual & expected) — from textual financial news or articles using LLM (LLaMA 3) powered by LangChain and GROQ API.

A hands-on project exploring LangChain's core capabilities through two interactive Jupyter notebooks: a conversational chatbot with session memory and a retrieval-augmented generation (RAG) pipeline using vector stores.

Guia prático de integração entre LangChain e Groq API. Contém um tutorial completo (Notebook) e uma aplicação de Chatbot Contábil com memória, streaming e RAG utilizando modelos Llama 3.

The application makes a processes a resume (from a PDF) and a job posting (scraped via WebBaseLoader) to extract structured JSON data using langchain-groq. It employs NLP techniques (nltk, scikit-learn) to preprocess text and calculate an ATS compatibility score (36.28%) via cosine similarity, indicating a moderate resume-job match.

The Streamlit App for improving Google SEO ranking of Persian Contents with help of Groq Langchain

A comprehensive RAG (Retrieval-Augmented Generation) learning project with LangChain, AWS Bedrock, FAISS, and ChromaDB. Includes notebooks, production apps, and complete pipeline implementation for document Q&A systems.

Welcome to the On-Device AI RAG project! This repository demonstrates how to utilize the ObjectBox Vector Database and LangChain to build a robust Retrieval-Augmented Generation (RAG) system directly on your device.