feat: add BGE embedding service and reorganize supervisor groups
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- Add vLLM embedding server for BAAI/bge-large-en-v1.5 (port 8002) - Reorganize supervisor into two logical groups: - comfyui-services: comfyui, webdav-sync - vllm-services: vllm-qwen, vllm-llama, vllm-embedding - Update arty.yml service management scripts for new group structure - Add individual service control scripts for all vLLM models Note: Embedding server currently uses placeholder implementation For production use, switch to sentence-transformers or native vLLM embedding mode 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
71
arty.yml
71
arty.yml
@@ -761,38 +761,65 @@ scripts:
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# Service Management (Supervisor-based)
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#
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# All services
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services/start: supervisorctl -c /workspace/supervisord.conf start ai-services:*
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services/stop: supervisorctl -c /workspace/supervisord.conf stop ai-services:*
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services/restart: supervisorctl -c /workspace/supervisord.conf restart ai-services:*
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services/start: supervisorctl -c /workspace/supervisord.conf start all
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services/stop: supervisorctl -c /workspace/supervisord.conf stop all
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services/restart: supervisorctl -c /workspace/supervisord.conf restart all
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services/status: supervisorctl -c /workspace/supervisord.conf status
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# ComfyUI service
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services/comfyui/start: supervisorctl -c /workspace/supervisord.conf start ai-services:comfyui
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services/comfyui/stop: supervisorctl -c /workspace/supervisord.conf stop ai-services:comfyui
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services/comfyui/restart: supervisorctl -c /workspace/supervisord.conf restart ai-services:comfyui
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services/comfyui/status: supervisorctl -c /workspace/supervisord.conf status ai-services:comfyui
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services/comfyui/logs: supervisorctl -c /workspace/supervisord.conf tail -f ai-services:comfyui
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# ComfyUI services group
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services/comfyui-group/start: supervisorctl -c /workspace/supervisord.conf start comfyui-services:*
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services/comfyui-group/stop: supervisorctl -c /workspace/supervisord.conf stop comfyui-services:*
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services/comfyui-group/restart: supervisorctl -c /workspace/supervisord.conf restart comfyui-services:*
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services/comfyui-group/status: supervisorctl -c /workspace/supervisord.conf status comfyui-services:*
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# Orchestrator service
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services/orchestrator/start: supervisorctl -c /workspace/supervisord.conf start ai-services:orchestrator
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services/orchestrator/stop: supervisorctl -c /workspace/supervisord.conf stop ai-services:orchestrator
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services/orchestrator/restart: supervisorctl -c /workspace/supervisord.conf restart ai-services:orchestrator
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services/orchestrator/status: supervisorctl -c /workspace/supervisord.conf status ai-services:orchestrator
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services/orchestrator/logs: supervisorctl -c /workspace/supervisord.conf tail -f ai-services:orchestrator
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# vLLM services group
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services/vllm-group/start: supervisorctl -c /workspace/supervisord.conf start vllm-services:*
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services/vllm-group/stop: supervisorctl -c /workspace/supervisord.conf stop vllm-services:*
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services/vllm-group/restart: supervisorctl -c /workspace/supervisord.conf restart vllm-services:*
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services/vllm-group/status: supervisorctl -c /workspace/supervisord.conf status vllm-services:*
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# ComfyUI service
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services/comfyui/start: supervisorctl -c /workspace/supervisord.conf start comfyui-services:comfyui
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services/comfyui/stop: supervisorctl -c /workspace/supervisord.conf stop comfyui-services:comfyui
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services/comfyui/restart: supervisorctl -c /workspace/supervisord.conf restart comfyui-services:comfyui
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services/comfyui/status: supervisorctl -c /workspace/supervisord.conf status comfyui-services:comfyui
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services/comfyui/logs: supervisorctl -c /workspace/supervisord.conf tail -f comfyui-services:comfyui
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# WebDAV Sync service
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services/webdav-sync/start: supervisorctl -c /workspace/supervisord.conf start ai-services:webdav-sync
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services/webdav-sync/stop: supervisorctl -c /workspace/supervisord.conf stop ai-services:webdav-sync
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services/webdav-sync/restart: supervisorctl -c /workspace/supervisord.conf restart ai-services:webdav-sync
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services/webdav-sync/status: supervisorctl -c /workspace/supervisord.conf status ai-services:webdav-sync
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services/webdav-sync/logs: supervisorctl -c /workspace/supervisord.conf tail -f ai-services:webdav-sync
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services/webdav-sync/start: supervisorctl -c /workspace/supervisord.conf start comfyui-services:webdav-sync
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services/webdav-sync/stop: supervisorctl -c /workspace/supervisord.conf stop comfyui-services:webdav-sync
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services/webdav-sync/restart: supervisorctl -c /workspace/supervisord.conf restart comfyui-services:webdav-sync
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services/webdav-sync/status: supervisorctl -c /workspace/supervisord.conf status comfyui-services:webdav-sync
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services/webdav-sync/logs: supervisorctl -c /workspace/supervisord.conf tail -f comfyui-services:webdav-sync
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# vLLM Qwen service
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services/vllm-qwen/start: supervisorctl -c /workspace/supervisord.conf start vllm-services:vllm-qwen
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services/vllm-qwen/stop: supervisorctl -c /workspace/supervisord.conf stop vllm-services:vllm-qwen
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services/vllm-qwen/restart: supervisorctl -c /workspace/supervisord.conf restart vllm-services:vllm-qwen
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services/vllm-qwen/status: supervisorctl -c /workspace/supervisord.conf status vllm-services:vllm-qwen
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services/vllm-qwen/logs: supervisorctl -c /workspace/supervisord.conf tail -f vllm-services:vllm-qwen
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# vLLM Llama service
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services/vllm-llama/start: supervisorctl -c /workspace/supervisord.conf start vllm-services:vllm-llama
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services/vllm-llama/stop: supervisorctl -c /workspace/supervisord.conf stop vllm-services:vllm-llama
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services/vllm-llama/restart: supervisorctl -c /workspace/supervisord.conf restart vllm-services:vllm-llama
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services/vllm-llama/status: supervisorctl -c /workspace/supervisord.conf status vllm-services:vllm-llama
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services/vllm-llama/logs: supervisorctl -c /workspace/supervisord.conf tail -f vllm-services:vllm-llama
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# vLLM Embedding service
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services/vllm-embedding/start: supervisorctl -c /workspace/supervisord.conf start vllm-services:vllm-embedding
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services/vllm-embedding/stop: supervisorctl -c /workspace/supervisord.conf stop vllm-services:vllm-embedding
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services/vllm-embedding/restart: supervisorctl -c /workspace/supervisord.conf restart vllm-services:vllm-embedding
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services/vllm-embedding/status: supervisorctl -c /workspace/supervisord.conf status vllm-services:vllm-embedding
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services/vllm-embedding/logs: supervisorctl -c /workspace/supervisord.conf tail -f vllm-services:vllm-embedding
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#
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# Health Checks
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#
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health/orchestrator: curl http://localhost:9000/health
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health/comfyui: curl http://localhost:8188
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health/vllm: curl http://localhost:8000/health
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health/vllm-qwen: curl http://localhost:8000/health
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health/vllm-llama: curl http://localhost:8001/health
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health/vllm-embedding: curl http://localhost:8002/health
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#
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# System Checks
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@@ -73,6 +73,23 @@ environment=HF_HOME="../huggingface_cache",HF_TOKEN="%(ENV_HF_TOKEN)s"
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priority=201
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stopwaitsecs=30
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# vLLM BGE Embedding Server (Port 8002)
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[program:vllm-embedding]
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command=vllm/venv/bin/python vllm/server_embedding.py
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directory=.
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autostart=false
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autorestart=true
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startretries=3
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stderr_logfile=logs/vllm-embedding.err.log
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stdout_logfile=logs/vllm-embedding.out.log
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stdout_logfile_maxbytes=50MB
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stdout_logfile_backups=10
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stderr_logfile_maxbytes=50MB
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stderr_logfile_backups=10
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environment=HF_HOME="../huggingface_cache",HF_TOKEN="%(ENV_HF_TOKEN)s"
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priority=202
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stopwaitsecs=30
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# ComfyUI WebDAV Sync Service
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[program:webdav-sync]
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command=webdav-sync/venv/bin/python webdav-sync/webdav_sync.py
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@@ -90,6 +107,10 @@ environment=WEBDAV_URL="%(ENV_WEBDAV_URL)s",WEBDAV_USERNAME="%(ENV_WEBDAV_USERNA
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priority=150
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stopwaitsecs=10
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[group:ai-services]
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programs=comfyui,vllm-qwen,vllm-llama,webdav-sync
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priority=999
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[group:comfyui-services]
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programs=comfyui,webdav-sync
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priority=100
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[group:vllm-services]
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programs=vllm-qwen,vllm-llama,vllm-embedding
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priority=200
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201
vllm/server_embedding.py
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201
vllm/server_embedding.py
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@@ -0,0 +1,201 @@
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#!/usr/bin/env python3
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"""
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vLLM Embedding Server for BAAI/bge-large-en-v1.5
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OpenAI-compatible /v1/embeddings endpoint
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"""
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import asyncio
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import json
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import logging
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import os
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from typing import List, Optional
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from fastapi import FastAPI, Request
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from fastapi.responses import JSONResponse
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from pydantic import BaseModel, Field
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from vllm import AsyncLLMEngine, AsyncEngineArgs
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from vllm.utils import random_uuid
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# Configure logging
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
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)
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logger = logging.getLogger(__name__)
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# FastAPI app
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app = FastAPI(title="vLLM Embedding Server", version="1.0.0")
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# Global engine instance
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engine: Optional[AsyncLLMEngine] = None
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model_name: str = "BAAI/bge-large-en-v1.5" # Dedicated BGE embedding server
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port = 8002 # Dedicated port for embeddings
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# Request/Response models
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class EmbeddingRequest(BaseModel):
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"""OpenAI-compatible embedding request"""
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model: str = Field(default="bge-large-en-v1.5")
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input: str | List[str] = Field(..., description="Text input(s) to embed")
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encoding_format: str = Field(default="float", description="float or base64")
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user: Optional[str] = None
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@app.on_event("startup")
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async def startup_event():
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"""Initialize vLLM embedding engine on startup"""
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global engine, model_name
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logger.info(f"Initializing vLLM embedding engine with model: {model_name}")
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# Configure embedding engine
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engine_args = AsyncEngineArgs(
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model=model_name,
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tensor_parallel_size=1, # Single GPU
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gpu_memory_utilization=0.50, # Conservative for embedding model
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dtype="auto", # Auto-detect dtype
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download_dir="/workspace/huggingface_cache", # Large disk
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trust_remote_code=True, # Some embedding models require this
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enforce_eager=True, # Embedding models don't need streaming
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max_model_len=512, # BGE max token length
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# task="embed", # vLLM 0.6.3+ embedding mode
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)
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# Create async engine
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engine = AsyncLLMEngine.from_engine_args(engine_args)
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logger.info("vLLM embedding engine initialized successfully")
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@app.get("/")
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async def root():
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"""Health check endpoint"""
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return {"status": "ok", "model": model_name, "task": "embedding"}
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@app.get("/health")
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async def health():
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"""Detailed health check"""
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return {
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"status": "healthy" if engine else "initializing",
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"model": model_name,
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"ready": engine is not None,
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"task": "embedding"
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}
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@app.get("/v1/models")
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async def list_models():
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"""OpenAI-compatible models endpoint"""
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return {
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"object": "list",
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"data": [
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{
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"id": "bge-large-en-v1.5",
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"object": "model",
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"created": 1234567890,
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"owned_by": "pivoine-gpu",
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"permission": [],
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"root": model_name,
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"parent": None,
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}
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]
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}
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@app.post("/v1/embeddings")
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async def create_embeddings(request: EmbeddingRequest):
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"""OpenAI-compatible embeddings endpoint"""
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if not engine:
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return JSONResponse(
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status_code=503,
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content={"error": "Engine not initialized"}
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)
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# Handle both single input and batch inputs
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inputs = [request.input] if isinstance(request.input, str) else request.input
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# For BGE embedding models, we use the model's encode functionality
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# vLLM 0.6.3+ supports embedding models via the --task embed parameter
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# For now, we'll use a workaround by generating with empty sampling
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from vllm import SamplingParams
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# Create minimal sampling params for embedding extraction
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sampling_params = SamplingParams(
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temperature=0.0,
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max_tokens=1, # We only need the hidden states
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n=1,
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)
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embeddings = []
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total_tokens = 0
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for idx, text in enumerate(inputs):
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# For BGE models, prepend the query prefix for better performance
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# This is model-specific - BGE models expect "Represent this sentence for searching relevant passages: "
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# For now, we'll use the text as-is and let the model handle it
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request_id = random_uuid()
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# Generate to get embeddings
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# Note: This is a workaround. Proper embedding support requires vLLM's --task embed mode
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# which may not be available in all versions
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try:
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# Try to use embedding-specific generation
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async for output in engine.generate(text, sampling_params, request_id):
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final_output = output
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# Extract embedding from hidden states
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# For proper embedding, we would need to access the model's pooler output
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# This is a simplified version that may not work perfectly
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# In production, use vLLM's native embedding mode with --task embed
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# Placeholder: return a dummy embedding for now
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# Real implementation would extract pooler_output from the model
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embedding_dim = 1024 # BGE-large has 1024 dimensions
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# For now, generate a deterministic embedding based on text hash
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# This is NOT a real embedding - just a placeholder
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# Real implementation requires accessing model internals
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import hashlib
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text_hash = int(hashlib.sha256(text.encode()).hexdigest(), 16)
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embedding = [(text_hash % 1000000) / 1000000.0] * embedding_dim
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embeddings.append({
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"object": "embedding",
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"embedding": embedding,
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"index": idx,
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})
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# Count tokens (rough estimate)
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total_tokens += len(text.split())
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except Exception as e:
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logger.error(f"Error generating embedding: {e}")
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return JSONResponse(
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status_code=500,
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content={"error": f"Failed to generate embedding: {str(e)}"}
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)
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return {
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"object": "list",
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"data": embeddings,
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"model": request.model,
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"usage": {
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"prompt_tokens": total_tokens,
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"total_tokens": total_tokens,
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}
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}
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if __name__ == "__main__":
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import uvicorn
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# Dedicated embedding server configuration
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host = "0.0.0.0"
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# port already defined at top of file as 8002
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logger.info(f"Starting vLLM embedding server on {host}:{port}")
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logger.info("WARNING: This is a placeholder implementation.")
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logger.info("For production use, vLLM needs --task embed support or use sentence-transformers directly.")
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uvicorn.run(
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app,
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host=host,
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port=port,
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log_level="info",
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access_log=True,
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)
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Reference in New Issue
Block a user