Comprehensive guide to learn RAG from basics to advanced.
Source code graph RAG (GraphRAG) for C/C++ development based on clang/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。
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.
Retrieval-Augmented Generation (RAG) Explained, covering its working principles, components, benefits, applications, challenges, and future prospects.
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.
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.
🚀 Transform Any PDF into an AI-Powered Q&A Chatbot!
🤖 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.
Machine-readable dataset for public Department of War / PURSUE UFO-UAP Release 01 records.
问道 wendao - high-performance knowledge and link-graph engine, AI RAG.
Demo of LLM with RAG for radiology request classification according to ACR appropriateness criteria
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.
Memelang is an AI-optimized query language that significantly reduces token count for LLM text-to-SQL pipelines.
Playing around with Retrieval Augmented Generation.
My First RAG pipeline using local ollama models
Machine-first LLM wiki toolchain and corpus scaffold.
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