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

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.

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 🚀🐳.

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

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.

🤖 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.

Training Data Generator for SPLADE Model Fine-tuning

This repository covers extensive tutorials on how to integrate LangSmith with LangChain with LangGraph to incorporate observability, monitoring, alerting, evaluation, etc. within complex LLM workflows and applications.

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

Advanced Retrieval-Augmented Generation system supporting multimodal document processing (text, tables, images) with multiple reasoning strategies and comprehensive evaluation framework.

Implements a Retrieval-Augmented Generation (RAG) system.

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).

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