A web-based tool for visualizing and exploring artifacts from Microsoft's GraphRAG.

Awesome Papers About Performing Prompting On Graphs

A Unified Python Library for Graph Prompting

Implementation of GraphPrompter (The Web Conference 2024 Short Paper)

Official Implementation of TokenFormer: Rethinking Transformer Scaling with Tokenized Model Parameters

Power CLI and Workflow manager for LLMs (core package)

Large Language Model based Multi-Agents: A Survey of Progress and Challenges

A programming framework for agentic AI 🤖 PyPi: autogen-agentchat Discord: https://aka.ms/autogen-discord Office Hour: https://aka.ms/autogen-officehour

收集GPTS的prompt / Collect the prompt of GPTS

An open-source visual programming environment for battle-testing prompts to LLMs.

Official implementation of "DS-Agent: Automated Data Science by Empowering Large Language Models with Case-Based Reasoning" in ICML'24

Awesome-Remote-Sensing-Vision-Language-Models

An Autonomous LLM Agent for Complex Task Solving

<⚡️> SuperAGI - A dev-first open source autonomous AI agent framework. Enabling developers to build, manage & run useful autonomous agents quickly and reliably.

An integration of Sigma.js with Neo4j and some custom render

Visual query builder for Neo4j graph database

Dify is an open-source LLM app development platform. Dify's intuitive interface combines AI workflow, RAG pipeline, agent capabilities, model management, observability features and more, letting yo…

Agent framework and applications built upon Qwen>=2.0, featuring Function Calling, Code Interpreter, RAG, and Chrome extension.

Qwen2.5 is the large language model series developed by Qwen team, Alibaba Cloud.

An open-source RAG-based tool for chatting with your documents.

The simplest, fastest repository for training/finetuning medium-sized GPTs.

Transformer Explained Visually: Learn How LLM Transformer Models Work with Interactive Visualization

A noGDAL tool for reading and writing geotiff files

Grammar Prompting for Domain-Specific Language Generation with Large Language Models

北京航空航天大学大数据高精尖中心自然语言处理研究团队开展了智能问答的研究与应用总结。包括基于知识图谱的问答（KBQA），基于文本的问答系统（TextQA），基于表格的问答系统（TableQA）、基于视觉的问答系统（VisualQA）和机器阅读理解（MRC）等，每类任务分别对学术界和工业界进行了相关总结。

The Memory layer for your AI apps

IDM Activation & Trail Reset Script

A Fine-Grained Instruction Tuning Dataset and Model for Remote Sensing Vision-Language Understanding