You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Training code for advanced RAG techniques - Adaptive-RAG, Corrective RAG, RQ-RAG, Self-RAG, Agentic RAG, and ReZero. Reproduces paper methodologies to fine-tune LLMs via SFT and GRPO for adaptive retrieval, corrective evaluation, query refinement, self-reflection, and agentic search behaviors.
Agentic Adaptive RAG is a production-ready framework for building self-correcting, reasoning-based LLM systems that dynamically choose between retrieval, web search, and generation.
"A resilient RAG agent built with LangGraph and LlamaIndex that self-corrects by rewriting queries when retrieval quality is low. Features a cyclical graph architecture for adaptive search.
Production-style Adaptive RAG chatbot for university course selection (PolyU). Hybrid search (BM25 + dense vector via ChromaDB), Reciprocal Rank Fusion, parent-child chunking, query decomposition, multi-turn conversation. Built with FastAPI + LangChain + Qwen. Includes evaluation suite + ablation study + bundled Claude tutor skill.
Adaptive RAG is an advanced retrieval-augmented generation system that intelligently combines dynamic query analysis with self-corrective mechanisms to choose the most effective strategy for answering user queries.
This project implements an advanced Adaptive Retrieval-Augmented Generation (RAG) agent using FastAPI and LangGraph. Unlike static RAG pipelines, this agent employs a cognitive architecture that dynamically selects data sources, optimizes retrieval through query translation and fusion, and verifies its own outputs using self-reflection mechanisms.
Adaptive RAG with LangGraph - Self-correcting question-answering system with dynamic query routing, document retrieval, grading, and web search fallback.
An attempt to production-ready Retrieval-Augmented Generation (RAG) system with advanced features including hybrid retrieval, adaptive feedback loops, comprehensive evaluation, and explainable AI logging.
This repository serves as knowledge base. The main focus is on the practical application of large models, including industry data fine-tuning, large model evaluation, and large model applications. All content is structured for easy navigation and learning.