Comprehensive guide to learn RAG from basics to advanced.

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。

搜索引擎API python版

Chat with AI

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

🚀 Transform Any PDF into an AI-Powered Q&A Chatbot!

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.

Demo of LLM with RAG for radiology request classification according to ACR appropriateness criteria

NLP Transformer pipeline + LLM QA with RAG - Homeworks for BigData Team NLP course on LLMs

CSE572-DataMining

LLM Experimentation

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.

Playing around with Retrieval Augmented Generation.