Interactive Query Expansion service

Ceng596 Information Retrieval Course Project, InfoNinjas

Evaluation code for Automated Query Expansion using High Dimensional Clustering

Information Retrieval system

Summarizing my findings on Query Expansion using Generative AI by evaluating and comparing the results of different query expansion methods.

For of a repository for a project of the course Information Retrieval IN4325 at TU Delft.

Group Project for CS7IS3 Assignment 2

Information retrieval project - UniPD 2021

Code pertaining to the paper: "Reducing Bias in Online Datasets"

Information Retreival Course Repository, 7th Semester, IITD, 2023-24

LLM-based Listwise Reranker for Retrieval-Augmented Generation (RAG) over GitHub code repositories. Includes advanced retrieval, reranking, summarization, and evaluation with Recall@K.

Translating JSON into Solr requests with support for sharding, mtas, caching and query expansion

An advanced information retrieval system using TF-IDF, BM25, and query expansion (RM3) for accurate document search and ranking. Features NLP models (Elmo, BERT) for enhanced semantic understanding. Includes a Flask UI with Ngrok for Colab, allowing query input, result exploration, and performance evaluation.

Search Engine for Information Retrieval Coursework

This project implements a financial research assistant tool that extracts insights from a PDF Report by generating augmented queries and performing semantic document search.

Advanced DB Project 1

Application and evaluation of relevance feedback and thesaurus

Bachelor Thesis as part of the TU Delft Computer Science and Engineering Programme

This project implements a Document Retrieval System that integrates GPT-3.5-turbo for query expansion and answer generation. It fetches and ranks documents based on user queries, leveraging MongoDB for document storage, Redis for caching, and web scraping to keep documents updated. The system is designed to provide fast and accurate search results

A multi-phase Information Retrieval system built for IR2025, exploring query expansion techniques using BM25, WordNet, and Word2Vec. Includes full evaluation using TREC metrics (MAP, avgPre@k) and implemented in Python with Elasticsearch, Gensim, and NLTK.