endee

Endee.io – A high-performance vector database, designed to handle up to 1B vectors on a single node, delivering significant performance gains through optimized indexing and execution. Also available in cloud https://endee.io/

Endee.io – A high-performance vector database, designed to handle up to 1B vectors on a single node, delivering significant performance gains through optimized indexing and execution. Also available in cloud https://endee.io/

Agentic Insurance Claims Analyzer — Endee Hybrid Search (Dense + BM25) + 3-step RAG pipeline + Groq LLaMA3 + Streamlit UI

This project is a semantic search system using transformer embeddings and the Endee vector database. It turns text documents into vectors and finds the most relevant ones based on meaning, enabling smarter search beyond keywords.

An intelligent codebase Q&A system powered by Endee vector database that allows you to upload code, ask questions in plain English, and get context-aware, cited answers. Implements RAG, semantic search, file recommendations, and agentic AI for complex queries.

AI Semantic Search powered by Endee Vector Database — Next.js 14, React, Tailwind CSS

RAG-powered document Q&A system built using Endee Vector Database and FastAPI

AI-powered Multi-Agent Research Assistant using LangGraph, Endee Vector Database, Groq, and Tavily for automated research workflows

Agentic AI movie recommendation engine using Endee vector database + Groq LLaMA 3.3

Production-ready RAG system with Endee vector database featuring hybrid search (semantic + BM25), neural re-ranking, and Streamlit UI for AI/ML knowledge base querying

RAG-powered Document Q&A system built with Endee Vector Database, sentence-transformers, Groq LLaMA 3, and Flask. Supports multi-document indexing, semantic search, query caching, and per-file filtering.

A production-ready Retrieval-Augmented Generation (RAG) assistant built with the Endee vector database, Streamlit, and Google Gemini 2.0 Flash.

👨‍💻 About the Author I am a BSc Data Science student passionate about building efficient, data-driven solutions. My work focuses on the intersection of Machine Learning, Software Engineering, and Embedded Systems. Interests: Natural Language Processing, Vector Databases, and Model Deployment. Tech Stack: Python, C++, SQL, and Git.

Echo-Ops: Autonomous Infrastructure Observability Agent powered by Endee Vector DB and LLM. Detects architectural entropy in log streams via semantic drift analysis.

A production-ready Retrieval-Augmented Generation (RAG) system enabling intelligent, context-aware document Q&A. Built with Python, Endee Vector Database, and Streamlit.

Production-ready RAG AI Assistant built using Node.js, React, Ollama, and Endee Vector Database.

Semantic Search + Question Answering RAG system using Endee Vector DB

🏦 AI-powered Financial Document Intelligence using RAG, Semantic Search & Agentic AI — built on Endee Vector Database