vector-databases

High-performance late-interaction retrieval engine for on-prem AI. ColBERT/ColPali multi-vector search with Rust fused MaxSim, Triton GPU kernels, ROQ quantization, LEMUR routing, WAL-backed CRUD, and a FastAPI server — single machine, CPU or GPU.

High-performance database management system

This repository is dedicated to my learning journey through the LangChain & Vector Databases in Production course offered by Activeloop. The course is part of the Gen AI 360 Foundational Model Certification and focuses on mastering Large Language Models (LLMs) and Vector Databases.

Air-gapped, production-ready RAG workspace featuring hybrid search, semantic reranking, cross-encoder reranking, and verifiable citations.

A repository containing all internship assignments completed as part of the FlyRank AI Fluency and Backend AI Internship Programs, covering AI fundamentals, AI tools, backend development, and practical AI engineering tasks.

Production-ready enterprise multi-agent AI orchestration platform with distributed memory architecture, intelligent task routing, semantic search, and comprehensive monitoring using modern software architecture patterns.

An offline RAG system that bridges Vietnamese-English medical knowledge. Ask questions in Vietnamese, get answers from English medical literature—translated back to Vietnamese. Built for doctors, students, and health enthusiasts who need quick access to medical information while maintaining complete data privacy.

RAGenix is a production-ready RAG system that enables accurate, context-aware conversations with PDFs using hybrid search, reranking, and conversational memory.

Transform your legal document review from days to minutes with LegalRAG. Our AI assistant understands complex legal questions, searches thousands of documents with semantic precision, and delivers professional answers with source citations. Experience faster case prep and thorough due diligence.

A comprehensive, hands-on tutorial repository for learning and mastering LangChain - the powerful framework for building applications with Large Language Models (LLMs). This codebase provides a structured learning path with practical examples covering everything from basic chat models to advanced AI agents, organized in a progressive curriculum.

A Retrieval-Augmented Generation application with built-in evaluation capabilities.

This project aims to develop an AI-powered research assistant that can process academic papers (PDFs), store them using vector embeddings, and retrieve relevant information to answer user queries. The system will also generate structured outputs such as literature reviews, research summaries, and comparative analysis of multiple papers.

Agentic RAG with Reasoning is a sophisticated AI system built with Agno, Gemini, and OpenAI that provides transparent, step-by-step insights into its research process. It enables users to query web sources while observing the agent's real-time thought process and logical reasoning steps.

This project is a Streamlit-based AI search engine that uses Agentic Retrieval-Augmented Generation (RAG) powered by LangGraph and Google Gemini. It allows users to input a blog URL, which it crawls and indexes in a vector database, to intelligently answer specific questions based on the article's content.

Comprehensive medical imaging management system with a DICOM viewer, HU conversion, 28 image filters, and dual database architecture (Qdrant + SQLite). Supports radiology report generation, patient data management, and vector similarity search.

🤖 Hub for autonomous AI agents, multi-agent orchestration, and memory systems. Showcases self-directed concepts and production-ready implementations using LangChain, CrewAI, AutoGen, and LlamaIndex with advanced tool use, vector databases, and contextual memory.

Production RAG document assistant — LLaMA 3.3 70B, FAISS, LangChain. 6 file formats, 8 languages, multi-document Q&A with citations.

AI-powered semantic product search using Sentence Transformers and ChromaDB.

KITE unifies technical knowledge across documents, logs, and multimodal evidence to enable grounded retrieval, explainable assistance, and scalable decision support

A practical and critical evaluation of Retrieval-Augmented Generation (RAG) systems on legal/insurance documents using RAGAS. This project analyzes metric failures, false negatives, retrieval pitfalls, and proposes a more realistic composite evaluation score.