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.

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

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.

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

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

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

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.

Codebase indexing and search tool in command line and Neovim.

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

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.

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