💡 WeKnora — Turn Documents into Living Knowledge with RAG, Agents and Auto-Wiki

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| <b>English</b> | <a href="./README_CN.md"><b>简体中文</b></a> | <a href="./README_JA.md"><b>日本語</b></a> | <a href="./README_KO.md"><b>한국어</b></a> |
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  [Overview](#-overview) • [Architecture](#-architecture) • [Key Features](#-key-features) • [Getting Started](#-getting-started) • [API Reference](#-api-reference) • [Developer Guide](#-developer-guide)
  
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# 💡 WeKnora — Turn Documents into Living Knowledge with RAG, Agents and Auto-Wiki

## 📌 Overview

[**WeKnora**](https://weknora.weixin.qq.com) is an open-source, LLM-powered knowledge framework built for enterprise-grade document understanding, semantic retrieval, and autonomous reasoning.

It is organized around three core capabilities: **RAG-based Quick Q&A** for everyday lookups, a **ReAct Agent** that autonomously orchestrates retrieval, MCP tools and web search to handle complex multi-step tasks, and a brand-new **Wiki Mode** in which agents distill raw documents into a self-maintaining, interlinked markdown knowledge base with an interactive knowledge graph, complete with manual editing, revision history and one-click rollback. Knowledge curation is equally hands-on: a **tree-structured folder view** preserves the directory layout of uploads, and **chunk editing with revision history** lets retrieval chunks be edited, diffed and reverted like documents. Combined with multi-source ingestion (Feishu wiki / Feishu Drive / Notion / Yuque / RSS, and growing), **website embed widgets** for publishing agents to external sites, **scoped API keys with a principal model** for programmatic integrations, **multi-instance storage backends** per workspace for flexible data placement, 20+ LLM provider integrations, full Langfuse observability plus a **runtime task-queue dashboard with worker-pool governance**, **enterprise-ready multi-workspace RBAC** (4-tier role matrix + per-resource ownership + per-workspace audit log), and a fully self-hostable modular architecture, WeKnora turns scattered documents into a queryable, reasoning-capable, continuously evolving knowledge asset.

The framework supports auto-syncing knowledge from Feishu, Notion, and Yuque (more data sources coming soon), handles 10+ document formats including PDF, Word, images, and Excel, and can serve Q&A directly through IM channels like WeCom, Feishu, Slack, and Telegram. It is compatible with major LLM providers including OpenAI, DeepSeek, Qwen (Alibaba Cloud), Zhipu, Hunyuan, Gemini, MiniMax, NVIDIA, and Ollama. Its fully modular design allows swapping LLMs, vector databases, and storage backends, with support for local and private cloud deployment ensuring complete data sovereignty. WeKnora also integrates with **Langfuse** for comprehensive observability into agent reasoning, token usage, and pipeline tracing.


## ✨ Latest Updates

- **v0.7.2** — Launched the **official product documentation site** (VitePress; six sections, ~50 pages covering ~360 API endpoints and ~150 environment variables, with standalone Docker/Nginx deployment, quickstart sample data and a local MCP demo); **knowledge base folder tree** (upload paths stored as first-class data, browse/rename/re-file documents like a file manager); **chunk editing with revision history** (edit retrieval chunks in the UI, per-version diff and rollback, automatic reindexing, plus custom document metadata); **Wiki page revision history** (snapshots + line-level dif

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