AI knowledge base

Local Knowledge Graph is a Flask-based web application that uses the local Llama language model to process user queries, generate step-by-step reasoning, and visualize thought processes in the form of an interactive knowledge graph. It also finds and displays related questions and answers based on semantic similarity. The main advantages of the application include real-time display of the reasoning process, dynamic knowledge graph visualization, calculation and display of the strongest reasoning path, and related question and answer based on semantic similarity.

MemoRAG is a memory-based RAG framework that provides support for various applications through an efficient ultra-long memory model. Unlike traditional RAG, MemoRAG leverages its memory model to achieve a global understanding of the entire database, enhancing evidence retrieval by recalling query-specific clues from memory, thereby generating more accurate and richer contextual responses. The development of MemoRAG is active, and resources and prototypes are constantly released in this warehouse.

GraphRAG-Ollama-UI is a local model adaptation version based on Microsoft GraphRAG, which supports the use of Ollama for local model support. It provides an interactive user interface through Gradio UI, making it easier for users to manage data, run queries and visualize results. Key advantages of this model include native model support, cost-effectiveness, interactive user interface, real-time graph visualization, file management, settings management, output exploration and logging.

Awesome-ChatTTS is an open source project that aims to provide FAQs and related resource collections for the ChatTTS project to help users get started quickly and solve problems they may encounter during use. This project not only compiles detailed installation guides and parameter descriptions, but also provides examples of various tone seeds, as well as auxiliary materials such as video tutorials.

Verba is an open source application designed to provide an end-to-end, smooth and user-friendly Retrieval Augmentation Generation (RAG) interface. It combines state-of-the-art RAG technology with Weaviate's context-aware database, which can be deployed on-premises or via the cloud, making it easy to explore data sets and extract insights.