# WeKnora — Open Source LLM Knowledge Platform by Tencent

> An open-source knowledge management platform by Tencent that transforms raw documents into a queryable RAG system, autonomous reasoning agent, and self-maintaining wiki.

## Install

Save as a script file and run:

# WeKnora — Open Source LLM Knowledge Platform by Tencent

## Quick Use
```bash
git clone https://github.com/Tencent/WeKnora.git
cd WeKnora
cp .env.example .env
# Configure database and LLM provider in .env
docker compose up -d
# Open http://localhost:8080 in your browser
```

## Introduction
WeKnora is an open-source knowledge platform developed by Tencent that turns unstructured documents into intelligent, queryable knowledge bases. It combines RAG retrieval, autonomous reasoning agents, and auto-generated wiki pages to make organizational knowledge accessible through natural language, going beyond simple document chat to provide structured, maintained knowledge systems.

## What WeKnora Does
- Ingests documents in PDF, Markdown, HTML, and other formats into a structured knowledge base
- Provides RAG-powered Q&A with source citations and confidence scoring
- Generates and maintains wiki pages automatically from ingested documents
- Supports autonomous reasoning agents that chain multiple knowledge lookups
- Offers multi-tenant access control for team and enterprise deployments

## Architecture Overview
WeKnora is built with a Go backend serving a React frontend. Documents are processed through a pipeline that extracts text, chunks it intelligently, generates embeddings, and stores them in a vector database alongside metadata. The query engine uses a hybrid retrieval strategy combining dense vector search with sparse keyword matching, followed by a reranking step. An agent layer orchestrates multi-hop reasoning by breaking complex questions into sub-queries and synthesizing answers from multiple retrieved passages.

## Self-Hosting & Configuration
- Deploy with Docker Compose for quick setup with all dependencies included
- Requires PostgreSQL for metadata and Milvus or Weaviate for vector storage
- Supports OpenAI, Anthropic, and Ollama as LLM backends
- Configurable chunking strategies including semantic, fixed-size, and recursive splitting
- Multi-tenant mode with organization-level isolation and RBAC

## Key Features
- Hybrid RAG retrieval combining vector similarity and keyword search with reranking
- Auto-generated wiki that stays synchronized with source document updates
- Multi-hop reasoning agent for complex questions spanning multiple documents
- Support for 20+ document formats with intelligent structure preservation
- Built-in evaluation tools for measuring retrieval quality and answer accuracy

## Comparison with Similar Tools
- **Dify** — LLMOps platform with RAG; WeKnora focuses specifically on knowledge management with auto-wiki generation
- **AnythingLLM** — All-in-one knowledge base; WeKnora offers more sophisticated multi-hop reasoning and enterprise multi-tenancy
- **Quivr** — RAG framework; WeKnora adds autonomous agents and self-maintaining wiki pages beyond simple retrieval
- **Onyx** — Broad AI chat with connectors; WeKnora specializes in deep knowledge structuring and reasoning

## FAQ
**Q: How many documents can WeKnora handle?**
A: The architecture scales horizontally. Production deployments handle millions of document chunks across distributed vector stores.

**Q: Does the auto-wiki feature require manual curation?**
A: Wiki pages are generated automatically and updated when source documents change. Manual editing is supported for refinements.

**Q: Can I use local LLMs instead of cloud APIs?**
A: Yes, WeKnora supports Ollama and any OpenAI-compatible endpoint for fully private deployments.

**Q: Is there an API for programmatic access?**
A: Yes, all features are accessible via a REST API with OpenAPI documentation.

## Sources
- https://github.com/Tencent/WeKnora
- https://weknora.tencent.com

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Source: https://tokrepo.com/en/workflows/asset-145a2f26
Author: Script Depot