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LobeChat Integration Skill

lobechat

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

SKILL.md

Full skill instructions

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

ServiceInternal URLPurpose
LobeChathttp://lobe-chat:3210AI chat interface
Casdoorhttp://lobe-casdoor:8000SSO authentication
MinIOhttp://lobe-minio:9000S3-compatible storage
PostgreSQLlobe-postgres:5432Database 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:

  1. Sign in: Go to $LOBE_URL in browser
  2. Create Knowledge Base: Settings → Knowledge Base → Create
  3. Upload Files: Add PDF, MD, TXT, or other documents
  4. Wait for Processing: LobeChat will chunk and embed the documents
  5. 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

ScriptPurpose
/​srv/​paas/​scripts/​lobe-status.shFull LobeChat status
/​srv/​paas/​scripts/​lobe-rag-query.shQuery 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

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