A modular framework for building and deploying Retrieval-Augmented Generation (RAG) systems with built-in evaluation and monitoring.

酒店评论分析与智能问答系统——基于花园酒店近三年上千条真实住客评论，提供评论查询、分析与智能问答服务。

🤖 Production-ready samples for building multi-modal AI agents that understand images, documents, videos, and text using Amazon Bedrock and Strands Agents. Features Claude integration, MCP tools, streaming responses, and enterprise-grade architecture.

Understand and build embedding models, focusing on word and sentence embeddings, dual encoder architectures. Learn to train embedding models using contrastive loss, implement them in semantic search and RAG systems.

Turn any LLM into a self-extending knowledge agent powered by a graph-structured memory - complete with PDF-to-graph ingestion, budget-aware optimisation, and dual-engine orchestration.

Four Tests Standard (4TS) - Vendor-neutral specification for verifiable AI governance

RAG Gateway Service 🚪🤖: FastAPI gateway that auto-detects query topics using OpenAI embeddings 🧠🔍 and routes requests to topic-specific RAG agents 🎯, with fallback support and Docker-ready 🚀🐳.

The course provides a comprehensive guide to optimizing retrieval systems in large-scale RAG applications. It covers tokenization, vector quantization, and search optimization techniques to enhance search quality, reduce memory usage, and balance performance in vector search systems.

This project implements a Retrieval-Augmented Generation (RAG) based chatbot designed to handle university-related queries using natural language understanding. It combines semantic search with generative AI to provide precise, context-aware answers to students, faculty, and visitors.

Experimenting with different kinds of RAGs Systems

A comprehensive surveillance-grade framework for monitoring the Information Labyrinth. Implements HLSP and AAIL protocols under the 2030 Directive logic.

PolySensor is an AI-powered agentic multi-modal content analysis tool that analyzes textual information from different 108 file formats or more. Using Google Gemini and advanced RAG techniques, it transforms documents, images, audio, and video into actionable insights through intelligent summarization and contextual understanding.

⚡ Generate dynamic CRUD and Auth routes effortlessly with FastAPI Auto Routes for SQLModel—no repetitive boilerplate needed.

Production-grade RAG system for Singapore government documents with OpenAI integration

This project processes and retrieves information from PDF file or PDF collection. It leverages Qdrant as a vector database for similarity searches and employs a Retrieval-Augmented Generation (RAG).

Training Data Generator for SPLADE Model Fine-tuning

A comprehensive Asset Administrative Shell (AAS) data modeling platform for Quality Infrastructure systems. Features AASX package processing, digital twin management, AI-powered analytics with RAG, and multi-format data transformation capabilities.

🚀 Complete AI Development Toolkit Template - Add RAG, MCP, and AI assistance to any project in 2 minutes

Decision-level observability for LLM pipelines, making system behavior explainable even when no outputs exist.