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Gao Dawei's starred repositories

JimuReport

「开源可视化报表，商业BI替代方案」积木报表是一款类似excel操作风格，在线拖拽完成设计的报表工具。低代码产品的臂膀！功能涵盖: 报表设计、图形报表、打印设计、大屏设计等，完全免费！秉承“简单、易用、专业”的产品理念，极大的降低报表开发难度、缩短开发周期、解决各类报表难题。

Language:JavaGPL-3.0628900

rosedb

Lightweight, fast and reliable key/value storage engine based on Bitcask.

Language:GoApache-2.0451400

gomvcc

A little, pedagogical implementation of mvcc and transaction isolation levels.

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fabric

fabric is an open-source framework for augmenting humans using AI. It provides a modular framework for solving specific problems using a crowdsourced set of AI prompts that can be used anywhere.

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GoMate

GoMate：RAG Framework within Reliable input,Trusted output

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FlashRAG

⚡FlashRAG: A Python Toolkit for Efficient RAG Research

Language:PythonMIT104400

langfuse

🪢 Open source LLM engineering platform: LLM Observability, metrics, evals, prompt management, playground, datasets. Integrates with LlamaIndex, Langchain, OpenAI SDK, LiteLLM, and more. 🍊YC W23

Language:TypeScriptNOASSERTION533600

fortune-sheet

A drop-in javascript spreadsheet library that provides rich features like Excel and Google Sheets

Language:TypeScriptMIT237100

univer

Univer is an open-source alternative to Google Sheets, Slides, and Docs

Language:TypeScriptApache-2.0632800

report

AJ-Report是一个完全开源，拖拽编辑的可视化设计工具。三步快速完成大屏：配置数据源---->写SQL配置数据集---->拖拽生成大屏。让管理层随时随地掌控业务动态，让每个决策都有数据支撑。

Language:JavaScriptApache-2.0102600

dataease

🔥 人人可用的开源数据可视化分析工具，帆软、Tableau 等商业 BI 工具的开源替代。

Language:JavaGPL-3.01675100

superset

Apache Superset is a Data Visualization and Data Exploration Platform

Language:TypeScriptApache-2.06136100

agentops

Python SDK for agent monitoring, LLM cost tracking, benchmarking, and more. Integrates with most LLMs and agent frameworks like CrewAI, Langchain, and Autogen

Language:PythonMIT153500

chat-with-your-data-solution-accelerator

A Solution Accelerator for the RAG pattern running in Azure, using Azure AI Search for retrieval and Azure OpenAI large language models to power ChatGPT-style and Q&A experiences. This includes most common requirements and best practices.

Language:PythonMIT73600

RAG-Survey

Collecting awesome papers of RAG for AIGC. We propose a taxonomy of RAG foundations, enhancements, and applications in paper "Retrieval-Augmented Generation for AI-Generated Content: A Survey".

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