ClawRAG - Self-hosted RAG & Memory — 技能

技能详情（站内镜像，无评论）

Self-hosted RAG engine with hybrid semantic and keyword search, document ingestion, local privacy, and seamless OpenClaw integration via Docker.

媒体与内容

许可证：MIT-0

MIT-0 ·免费使用、修改和重新分发。无需归因。

版本：v1.2.0

统计：⭐ 0 · 1.2k · 5 current installs · 5 all-time installs

⭐ 0

安装量（当前） 5

🛡 VirusTotal ：可疑 · OpenClaw ：可疑

Package：2dogsandanerd/clawrag

安全扫描（ClawHub）

VirusTotal ：可疑
OpenClaw ：可疑

OpenClaw 评估

The skill's runtime instructions largely match a self‑hosted RAG connector, but metadata omits real requirements (Docker, possible API keys) and the runtime steps fetch and run third‑party code (GitHub repo, npm package, Docker images) that you should review before installing.

目的

The SKILL.md describes a ClawRAG connector (self‑hosted RAG via Docker, an MCP server) which aligns with the skill name. However the registry metadata provides no description and omits expected requirements (Docker, docker-compose). This mismatch is an authoring/metadata gap rather than an obvious functional mismatch.

说明范围

Instructions are narrowly scoped to: clone a GitHub repo, copy .env.example -> .env, run docker compose, add an MCP via npx, and verify the local HTTP health endpoint. They do not instruct reading unrelated host files or exfiltrating data, but they do require executing network‑fetched code and running containers on the host.

安装机制

There is no formal install spec, but runtime steps fetch code from a personal GitHub repo (https://github.com/2dogsandanerd/ClawRag.git), run docker compose (images unspecified), and invoke npx to fetch @clawrag/mcp-server from npm. Those are legitimate for this purpose but involve running unreviewed third‑party code and container images — higher risk than an instruction‑only skill that uses only built‑in tooling.

证书

Registry metadata declares no required environment variables, but SKILL.md states you may need OpenAI/Anthropic API keys (and creates a .env from .env.example). This is an inconsistency: the skill may require sensitive API keys and environment configuration that are not declared in metadata, so users might be surprised to need to provide secrets.

持久

The skill does not request always:true and does not claim to modify other skills or system settings. It does ask you to register an MCP connector via openclaw mcp add (expected for its purpose). Autonomous invocation is allowed (platform default) but not a new privilege here.

安装（复制给龙虾 AI）

将下方整段复制到龙虾中文库对话中，由龙虾按 SKILL.md 完成安装。

请把本段交给龙虾中文库（龙虾 AI）执行：为本机安装 OpenClaw 技能「ClawRAG - Self-hosted RAG & Memory」。简介：Self-hosted RAG engine with hybrid semantic and keyword search, document ingest…。
请 fetch 以下地址读取 SKILL.md 并按文档完成安装：https://raw.githubusercontent.com/openclaw/skills/refs/heads/main/skills/2dogsandanerd/clawrag/SKILL.md
（来源：yingzhi8.cn 技能库）

SKILL.md

打开原始 SKILL.md（GitHub raw）

# ClawRAG Connector

**The Brain for OpenClaw** - Self-hosted RAG engine with hybrid search.

> ⚠️ This skill requires Docker. It connects OpenClaw to your local ClawRAG instance.

## What is ClawRAG?

Production-ready RAG infrastructure that keeps your data local:
- 🔒 **Privacy-first**: Vector DB runs on your machine
- 🔍 **Hybrid Search**: Semantic + Keyword (BM25) + RRF ranking
- 📄 **Smart Ingestion**: PDFs, Office docs, Markdown via Docling
- 🧠 **MCP-native**: Seamless OpenClaw integration

## Installation

### Step 1: Start ClawRAG (Docker)
```bash
git clone https://github.com/2dogsandanerd/ClawRag.git
cd ClawRag
cp .env.example .env
docker compose up -d
```

Wait for http://localhost:8080/health to return OK.

### Step 2: Connect OpenClaw
```bash
openclaw mcp add --transport stdio clawrag npx -y @clawrag/mcp-server
```

### Verification
Test your setup:
```bash
curl http://localhost:8080/api/v1/rag/collections
```

## Features

| Capability | Description |
|------------|-------------|
| Document Upload | PDF, DOCX, TXT, MD via API or folder |
| Hybrid Query | Vector similarity + keyword matching |
| Citations | Source tracking for all answers |
| Multi-Collection | Organize knowledge by project |

## Requirements

- Docker + Docker Compose
- 4GB+ RAM (8GB recommended for local LLM)
- Or: OpenAI/Anthropic API key for cloud LLM

## Architecture

```
OpenClaw ◄──MCP──► @clawrag/mcp-server ◄──HTTP──► ClawRAG API (localhost:8080)
                                           │
                                           ▼
                                    ┌─────────────┐
                                    │  ChromaDB   │
                                    │  (vectors)  │
                                    └─────────────┘
```

## Links

- 📚 Full Docs: https://github.com/2dogsandanerd/ClawRag#readme
- 🔧 API Reference: http://localhost:8080/docs (when running)
- 🐛 Issues: https://github.com/2dogsandanerd/ClawRag/issues
- 📦 MCP Package: https://www.npmjs.com/package/@clawrag/mcp-server

## Tags

rag, vector, memory, search, documents, self-hosted, privacy, mcp, local-ai

---

## Metadata für ClawHub-Upload:

| Feld | Wert |
|------|------|
| **Slug** | `clawrag` |
| **Display name** | `ClawRAG - Self-hosted RAG & Memory` |
| **Version** | `1.2.0` |
| **Tags** | `rag`, `vector`, `memory`, `search`, `documents`, `self-hosted`, `privacy`, `mcp`, `local-ai` |

## Changelog für Version 1.2.0

### 1.2.0 - Initial ClawHub Release

- Connector skill for OpenClaw integration
- MCP server support (@clawrag/mcp-server v1.1.0)
- Docker-first deployment
- Hybrid search (Vector + BM25)