🤖 vs-code extension that suggest commit messages by AI & context
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Updated
Jul 6, 2026 - TypeScript
🤖 vs-code extension that suggest commit messages by AI & context
Retrieval & Reasoning Engine for Qure. A modular, RAG-based AI agentic core for automated medical telephony and patient triage.
A starter repo on how to create your first simple LLM application using LangChain
A collection of LangChain pipelines using Groq API with LLaMA and DeepSeek models – featuring sequential, parallel, conditional, and simple chains.
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.
Click below to checkout the website
🤖 Multi-persona AI chatbot built with Streamlit & LangChain, powered by LLaMA 3.3 70B via Groq. Switch between Professional, Friendly, Humorous, and Expert response styles.
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.
AI-powered resume parser and JD matcher built with LangChain, Groq, Pydantic v2, and Streamlit — structured LLM output, zero keyword hacks.
This project combines the power of vector databases, large language models, and chat history management to create an interactive PDF chatbot
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.
Click below to checkout the website
This project implements a Retrieval-Augmented Generation (RAG) chatbot that can answer medical questions—especially focused on Anatomy and Forensics—based on uploaded PDF documents. It uses Hugging Face models for embedding and language generation, FAISS for vector storage, and React frontend for an interactive chat interface.
The AI agents that execute different tasks with assistance of LangChain, CrewAI and AutoGen
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.
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