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apecloud/ApeRAG

apecloud/ApeRAG

🐍 ☁️ 🏠 - RAG platform combining Graph RAG, vector search and full-text search for building knowledge graphs…
在 GitHub 查看 ↗直链 · 无中转页
官方说明🐍 ☁️ 🏠 - RAG platform combining Graph RAG, vector search and full-text search for building knowledge graphs and context engineering.
配置模板(请按你的客户端调整)
claude mcp add aperag -- npx -y aperag
这是根据仓库名生成的可粘贴骨架,命令行参数与包名请以该项目 README 为准。
★ 数取值
1,322 ★ · apecloud/ApeRAG · 取自该仓库的 GitHub 页面 · (2026-10-16 20:00:20 核对)
来源明细
收录自 punkpeye/awesome-mcp-servers 榜单(我们从中收集的条目之一) · 条目类型:MCP 服务
收录与活跃
收录于 2026-10-09 · 最近见到 2026-10-16 · 共见到 32 次 · 被 10 个来源列出
评分构成
综合分 1,003 · 1,322 ★ · 10 个来源 · mcp
常见问题(答案都来自我们的记录)
怎么安装?
我们【没有】从该仓库取到安装命令(我们的 install 覆盖率只有约 4%)。请看它的 README 或 releases 页。
它适合谁 / 用来做什么?
我们把它归在这些标签下: MCP 服务。标签来自条目自身,不是我们强加的分类。
谁维护它、还在更新吗?
仓库 apecloud/ApeRAG;我们收录于 2026-10-09;最近见到 2026-10-16。这里没有"官方认证"或"质量评级"——我们只记录来源与时间。
第三方质量标记

ApeRAG

以下摘录自该仓库的 README(片段) · 在 GitHub 读完整 README →

ApeRAG is a production-ready RAG (Retrieval-Augmented Generation) platform that combines Graph RAG, vector search, and full-text search with advanced AI agents. Build sophisticated AI applications with hybrid retrieval, multimodal document processing, intelligent agents, and enterprise-grade management features.
ApeRAG is the best choice for building your own Knowledge Graph, Context Engineering, and deploying intelligent AI agents that can autonomously search and reason across your knowledge base.
阅读中文文档
- Quick Start
- Key Features
- Kubernetes Deployment (Recommended for Production)
- Development
- Build Docker Image
- Acknowledgments
- License
Quick Start
> Before installing ApeRAG, make sure your machine meets the following minimum system requirements:
>
> - CPU >= 2 Core
> - RAM >= 4 GiB
> - Docker & Docker Compose
The easiest way to start ApeRAG is through Docker Compose. Before running the following commands, make sure that Docker and Docker Compose are installed on your machine:
After running, you can access ApeRAG in your browser at:
- **Web Interface**: http://localhost:3000/web/
- **API Documentation**: http://localhost:8000/docs
MCP (Model Context Protocol) Support
ApeRAG supports MCP (Model Context Protocol) integration, allowing AI assistants to interact with your knowledge base directly. After starting the services, configure your MCP client with:
**Authentication** (by priority):
1. **HTTP Authorization Header** (Recommended): Authorization: Bearer your-api-key
2. **Environment Variable** (Fallback): APERAG_API_KEY=your-api-key
**Important**: Use your deployed API origin if not local (e.g. https://your-host/mcp/). Replace your-api-key-here with a valid API key from your ApeRAG settings.
The MCP server provides:
- **Collection browsing**: List and explore your knowledge collections
- **Hybrid search**: Search using vector, full-text, and graph methods
- **Intelligent querying**: Ask natural language questions about your documents
Enhanced Document Parsing
For enhanced document parsing capabilities, ApeRAG supports an **advanced document parsing service** powered by MinerU, which provides superior parsing for complex documents, tables, and formulas.
Enhanced Document Parsing Commands
Or use the Makefile shortcuts (requires GNU Make):
Development & Contributing
For developers interested in source code development, advanced configurations, or contributing to ApeRAG, please refer to our Development Guide for detailed setup instructions.
Key Features
**1. Advanced Index Types**:
Five comprehensive index types for optimal retrieval: **Vector**, **Full-text**, **Graph**, **Summary**, and **Vision** - providing multi-dimensional document understanding and search capabilities.
**2. Intelligent AI Agents**:
Built-in AI agents with MCP (Model Context Protocol) tool support that can automatically identify relevant collections, search content intelligently, and provide web search capabilities for comprehensive question answering.
**3. Enhanced Graph RAG with Entity Normalization**:
Deeply modified LightRAG implementation with advanced entity normalization (entity merging) for cleaner knowledge graphs and improved relational understanding.
**4. Multimodal Processing & Vision Support**:
Complete multimodal document processing including vision capabilities for images, charts, and visual content analysis alongside traditional text processing.
来源
awesome · https://github.com/apecloud/ApeRAG
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