LightningRAG is a full-stack Vue + Gin starter with a decoupled frontend and backend, plus built-in, extensible RAG (retrieval-augmented generation): knowledge bases, vector search, and integrations with many LLM and vector-store providers

LightningRAG

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GitHub: https://github.com/LightningRAG/LightningRAG

1. Basic Introduction

1.1 Project introduction

LightningRAG is built around RAG: knowledge bases (ingest, parse, chunk, vector retrieval), pluggable LLMs, embeddings, vector stores, and rerankers, and Agent orchestration on a canvas (retrieval, LLM, tools, control flow)—with optional webhook channel connectors (Feishu, DingTalk, Slack, etc.). It ships as a Vue + Gin full-stack starter with JWT, dynamic routes/menus, Casbin, a form builder, and code generation.

Online Demo: https://demo.LightningRAG.com

username：admin

password：123456

Public preview is not deployed yet; remove the strikethrough and turn the demo link back on when the server is ready.

2. Features

• Knowledge bases & RAG: Ingest documents, parse and chunk, embed into pluggable vector stores; hybrid / multi-path retrieval (vector, keyword, PageIndex, and related retriever types); conversation and streaming chat APIs with optional references; pluggable LLMs, embeddings, rerankers, and rag: defaults in config.yaml (Section 3 below; server/rag/README.md).
• Agent orchestration: Visual canvas flows with nodes such as Begin, Retrieval, LLM, Message, and Agent (with tools) for multi-step and branching automation; extensible tool registry for RAG chat (server/rag/tools/README.md).
• Channel connectors (optional): Bind published agents to webhook endpoints for Feishu, DingTalk, WeChat, Slack, Teams, and other platforms (Section 3.5 below; docs/THIRD_PARTY_CHANNEL_CONNECTORS.md).
• Authority management: Authority management based on jwt and casbin.
• File upload and download: imp