Claude-skill-registry lobechat

Access LobeChat for AI chat, knowledge base queries, and multi-model routing.

install

source · Clone the upstream repo

git clone https://github.com/majiayu000/claude-skill-registry

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/lobechat" ~/.claude/skills/majiayu000-claude-skill-registry-lobechat && rm -rf "$T"

manifest: skills/data/lobechat/SKILL.md

LobeChat Integration Skill

Access LobeChat for AI chat, knowledge base queries (RAG), and multi-model routing.

Quick Reference

# Health check
curl -s "$LOBE_URL/api/health"

# Check via internal network
curl -s "http://lobe-chat:3210/api/health"

Required env var:

LOBE_URL

Services

Service	Internal URL	Purpose
LobeChat	http://lobe-chat:3210	AI chat interface
Casdoor	http://lobe-casdoor:8000	SSO authentication
MinIO	http://lobe-minio:9000	S3-compatible storage
PostgreSQL	lobe-postgres:5432	Database with pgvector

Use Cases

1. Knowledge Base Queries (RAG)

LobeChat has PostgreSQL with pgvector for semantic search:

# Query the knowledge base
bash /srv/paas/scripts/lobe-rag-query.sh "What is X?" 5

Technical Details:

Embedding Model: Cloudflare Workers AI
```
@cf/baai/bge-large-en-v1.5
```
(1024 dimensions)
Vector Storage: PostgreSQL with pgvector extension
File Storage: MinIO (S3-compatible)

2. Multi-Model Routing

LobeChat supports 40+ model providers. Use when:

Different tasks need different models (Claude for reasoning, GPT for coding)
Comparing model outputs
Cost optimization (route to cheaper models for simple tasks)

3. Image Generation & Vision

Supports:

DALL-E 3 for image generation
Vision models (GPT-4V, Claude Vision, Gemini) for image analysis

Health Checks

Quick Status

# LobeChat
curl -s "$LOBE_URL/api/health" && echo " - LobeChat OK"

# MinIO
curl -s "http://lobe-minio:9000/minio/health/live" && echo " - MinIO OK"

Full Status

bash /srv/paas/scripts/lobe-status.sh

Database Operations

Knowledge Base Stats

docker exec -i lobe-postgres psql -U postgres -d lobechat -c "
SELECT 
    (SELECT COUNT(*) FROM knowledge_bases) as kb_count,
    (SELECT COUNT(*) FROM files) as files,
    (SELECT COUNT(*) FROM chunks) as chunks;
"

RAG Query Direct

# Usage: lobe-rag-query.sh "query" [limit]
bash /srv/paas/scripts/lobe-rag-query.sh "How does authentication work?" 5

First-Time Setup

Before using RAG queries, upload documents to LobeChat:

Sign in: Go to
```
$LOBE_URL
```
in browser
Create Knowledge Base: Settings → Knowledge Base → Create
Upload Files: Add PDF, MD, TXT, or other documents
Wait for Processing: LobeChat will chunk and embed the documents
Query: Use the RAG query script

API Endpoints

Health

curl -s "$LOBE_URL/api/health"

Internal Network Access

OpenClaw can reach LobeChat via internal Docker network:

# Internal URL (from containers)
curl -s "http://lobe-chat:3210/api/health"

Scripts

Script	Purpose
`/srv/paas/scripts/lobe-status.sh`	Full LobeChat status
`/srv/paas/scripts/lobe-rag-query.sh`	Query knowledge base

Configuration

LobeChat is configured with direct provider access:

OpenRouter: Primary provider (access Claude, GPT, Gemini via single key)
Gemini: Direct Google AI access
DeepSeek: Direct DeepSeek access
Cloudflare Workers AI: Embeddings for RAG

Add API keys in LobeChat: Settings → Language Model → Enable providers