Comprehensive guide to learn RAG from basics to advanced.
Source code graph RAG (GraphRAG) for C/C++ development based on clangd
一个《原神》AI驱动视频项目，利用LLM API生成角色互动文案，VITS技术进行语音合成，并结合先进的文生图和视频合成技术，创造出游戏角色之间有趣的场景。最终产出为短视频。
Retrieval-Augmented Generation (RAG) Explained, covering its working principles, components, benefits, applications, challenges, and future prospects.
FileChat-RAG is a simple Retrieval-Augmented Generation (RAG) system that allows users to ask questions about the contents of various file formats. It extracts text from PDFs, JSON, text files(.txt, .docx, .odt, .md), and code files, then enables interactive conversations using an LLM powered by Ollama.
Chat With Documents is a Streamlit application designed to facilitate interactive, context-aware conversations with large language models (LLMs) by leveraging Retrieval-Augmented Generation (RAG). Users can upload documents or provide URLs, and the app indexes the content using a vector store called Chroma to supply relevant context during chats.
🚀 Transform Any PDF into an AI-Powered Q&A Chatbot!
Self-hosted AI powered knowledge base for SMBs: WikiJS + Qdrant Vector search, Chrome extension queries, single Ansible deploy. Unlimited users, no subs - reduce SaaS costs, own your data.
🤖 Full-stack conversational AI using a Letta (MemGPT) + RAG hybrid architecture for long-term memory, context persistence, and grounded responses. Built with FastAPI, React, FAISS, and MongoDB, featuring Isabella — a personality-driven assistant with document ingestion, structured memory, logging, and a terminal-style streaming chat UI.
Demo of LLM with RAG for radiology request classification according to ACR appropriateness criteria
Playing around with Retrieval Augmented Generation.
AutoRAG: An Open-Source Framework for Retrieval-Augmented Generation (RAG) Evaluation & Optimization with AutoML-Style Automation
NLP Transformer pipeline + LLM QA with RAG - Homeworks for BigData Team NLP course on LLMs
Evaluation script for automatic evaluation of JCrRAG benchmark
Search and interact locally with technical documents of any kind
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