Scalable agent registry for AI agents using A2A protocol AgentCard with semantic search. AWS serverless (Lambda, S3 Vectors, Bedrock). Python SDK & React Web UI.

MedSage is a multimodal healthcare assistant that combines LLMs, vector search, and real-time reasoning to deliver fast, reliable medical insights. It supports symptom analysis, medical document Q&A, universal file RAG, multilingual interactions, and emergency SOS with live location.

Hybrid RAG with three retrievers—Lexical (BM25), Semantic (embeddings), and Hybrid (Reciprocal Rank Fusion) — parallel retrieval, Llama 3 generation, and side-by-side evaluation with reproducible notebooks.

Building production-grade Retrieval-Augmented Generation (RAG) systems. Explore advanced chunking strategies, hybrid search with Weaviate, LLM task orchestration, and fine-grained generation control.

An end-to-end RAG system that grounds LLMs in factual reality, using semantic search on real-time news to provide verifiable, context-aware answers.

DigiBrain is a second brain web platform that stores links (tweets, YouTube videos, documents, etc.) enriched with metadata such as title, description, and tags. The system integrates an AI assistant that retrieves contextually relevant content using embeddings and a vector database.

An AI-powered Fashion Assistant built using Retrieval-Augmented Generation (RAG). Combines semantic search, reranking, and LLM-based reasoning to deliver intelligent fashion recommendations, product discovery, and 24/7 customer support.

A hands-on exploration of Retrieval-Augmented Generation's core components: semantic search, retriever evaluation, and context-augmented LLM prompting.

"When memories are scattered... always leads to DreamTheater awakens",, "We take photos to stop time. DreamTheater takes photos to make time flow again."

📖 A Retrieval-Augmented Generation (RAG) MCP server for markdown documentation with semantic search capabilities

A local-first, privacy-focused personal context engine that lets you chat with your documents using offline AI models. Built with Electron, Python, and Ollama.

Production RAG system for automated enterprise support using Vertex AI embeddings, Neo4j knowledge graphs, and LangChain/LangGraph agentic workflows. Achieves 95%+ accuracy through semantic search, multi-hop reasoning, and confidence-based escalation with comprehensive evaluation frameworks.

Fetch all the details from given url and user can get accurate responses to the given question.

An experiment around figuring out best way to provide code context to coding agents

SementicCore: A transformer-based text embedding model trained with contrastive learning (SimCSE approach) for generating high-quality sentence embeddings.

Production-ready RAG framework using FastAPI + ChromaDB + OpenAI, with doc_id filtering, similarity scoring, and enterprise-ready patterns

⭐ A modern AGNCI – Webflow HTML website template, rebuilt from Webflow using pure HTML, CSS, and responsive design properties

Codebase indexing and search tool in command line and Neovim.

🔍 Optimize RAG systems by exploring Lexical, Semantic, and Hybrid Search methods for better context retrieval and improved LLM responses.