Build and manage intelligent multi-model LLM routing with vLLM inference engine, dynamic model coordination, and enterprise AI services.
Comprehensive guidance for building and maintaining an intelligent multi-model LLM cooperation system using vLLM inference engine with dynamic routing, coordination, and enterprise-grade AI services.
This system implements intelligent multi-model routing and coordination using vLLM inference engine. It provides enterprise-grade AI services with automatic model selection, distributed inference, and cooperative processing capabilities across multiple language models.
The system is organized into five core layers:
Create `API_Key_DeepSeek.env` with:
```bash
BASE_URL=https://api2.aigcbest.top/v1
API_KEY=your_api_key_here
MODEL=Qwen/Qwen3-32B
```
Configure OpenAI-compatible API endpoints:
```bash
python api_config.py preset --name aigcbest --api-key YOUR_API_KEY
python api_config.py global --base-url https://api2.aigcbest.top/v1 --api-key YOUR_KEY
python api_config.py add-model --name custom_model --path Qwen/Qwen3-32B --tasks reasoning math
python api_config.py test
python api_config.py list
```
**Supported Providers:**
Configure models in `config.py`:
Adjust routing strategies, performance thresholds, task type mappings, cooperation modes, model parameters, and monitoring settings as needed.
```bash
python start_system.py --dev
python start_system.py
docker-compose up -d
```
```bash
python test_system.py
python example_client.py --example all
python example_client.py --example cooperation
python example_client.py --example services
```
```bash
curl http://localhost:8080/status
curl http://localhost:8080/models
curl http://localhost:8080/routing/stats
```
```python
POST /query
Content-Type: application/json
{
"query": "Explain quantum entanglement",
"preferences": {
"strategy": "auto",
"quality_priority": 0.8
}
}
```
```python
POST /service
Content-Type: application/json
{
"service_type": "document_analysis",
"content": "Your document text here...",
"parameters": {
"analysis_type": "comprehensive"
}
}
```
```python
POST /cooperation/task
Content-Type: application/json
{
"query": "Complex multi-step analysis request",
"mode": "sequential",
"models": ["qwen3_32b_reasoning", "qwen2_5_7b"]
}
```
1. **Async Patterns**: All components implement comprehensive async patterns for optimal concurrency
2. **Logging**: Extensive logging and metrics collection throughout the system for observability
3. **Resource Management**: GPU memory and compute resources are automatically managed by vLLM
4. **Hot-Swappable Configs**: Model configurations can be changed without system restart
5. **Performance Optimization**: Built-in performance optimization and load balancing across models
6. **Version Requirements**: System requires vLLM v0.6.0+ as inference engine foundation
**Adding a new model:**
1. Configure in `config.py` with GPU allocation
2. Add model capabilities and task mappings
3. Update routing rules in `intelligent_router.py`
4. Test with `python api_config.py test`
**Implementing a new cooperation mode:**
1. Add mode logic in `cooperation_scheduler.py`
2. Update task decomposition strategies
3. Implement result integration logic
4. Add comprehensive tests in `test_system.py`
**Creating a new application service:**
1. Define service in `application_service.py`
2. Implement processing pipeline
3. Add API endpoint in main router
4. Document usage in `example_client.py`
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