llm-rag

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.

搜索引擎API python版

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!

A minimal example for in-memory RAG using ChromaDB and Ollama LLM

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.

🤖 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.

Live tracker of new RAG implementations, tools, and patterns — updated every 15 minutes

Memelang is an AI-optimized query language that significantly reduces token count for LLM text-to-SQL pipelines.

Archive the first official U.S. Department of War UAP declassification, containing 162 verified technical files, photographs, and videos.

Playing around with Retrieval Augmented Generation.

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.

AutoRAG: An Open-Source Framework for Retrieval-Augmented Generation (RAG) Evaluation & Optimization with AutoML-Style Automation

Machine-first LLM wiki toolchain and corpus scaffold.

Evaluation script for automatic evaluation of JCrRAG benchmark

Coordination repo for the LLM Wiki family (Base Model + registered child checkouts)

advanced, scalable, no-code RAG

My First RAG pipeline using local ollama models