The official implementation of "RouteExplainer: An Explanation Framework for Vehicle Routing Problem" (PAKDD 2024, oral)

Automated LLM-based Prompt Engineering for Structured Data Processing

Powerful framework for building applications with Large Language Models (LLMs), enabling seamless integration with memory, agents, and external data sources.

RAG enhances LLMs by retrieving relevant external knowledge before generating responses, improving accuracy and reducing hallucinations.

A powerful CLI tool using vector embeddings and LLMs to help developers understand codebases through natural language. Ask questions in plain English, get context-aware responses, analyze GitHub repos, and generate documentation. Your AI coding companion for quick codebase exploration.

ScholarLens analyzes research papers using RAG with AI models from OpenAI, Anthropic, and Google. It identifies research gaps, assesses novelty, extracts key concepts, visualizes citations, and enables natural language queries of academic content. Features include PDF processing, arXiv/Semantic Scholar integration, batch processing, and intelligent

Master’s Thesis at TU Vienna, assessing state-of-the-art LLMs for automating BPO tasks. Features a custom Action Research-Based Compliance Testing (ARCT) framework, exploring LLM capabilities, context impact, and limitations in optimizing complex workflows.

An interactive Jupyter Notebook demonstrating AI agent collaboration using CrewAI. This project explores how multiple AI agents can research, generate content, and automate workflows through task orchestration.

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.

LLMGrep combines the precision of Semgrep's static analysis with the power of Large Language Models to deliver comprehensive security scanning, interactive vulnerability discussions, and intelligent rule generation capabilities.