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:

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

| 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