docs(tutorial): add minimal LoRA routing guide

Signed-off-by: bitliu <[email protected]>

Author: Xunzhuo

config/intelligent-routing/in-tree/lora_routing_example.yaml renamed to config/intelligent-routing/in-tree/lora_routing.yaml

@@ -0,0 +1,178 @@
+# LoRA Routing
+
+This guide shows how to enable intent-aware LoRA (Low-Rank Adaptation) routing in the Semantic Router:
+
+- Minimal configuration for LoRA routing
+- vLLM server setup with LoRA adapters
+- Example request/response showing automatic LoRA selection
+- Verification steps
+
+## Prerequisites
+
+- A running vLLM server with LoRA support enabled
+- LoRA adapter files (fine-tuned for specific domains)
+- Envoy + the router (see [Start the router](../../getting-started/quickstart.md) section)
+
+## 1. Start vLLM with LoRA Adapters
+
+First, start your vLLM server with LoRA support enabled:
+
+```bash
+vllm serve meta-llama/Llama-2-7b-hf \
+  --enable-lora \
+  --lora-modules \
+    technical-lora=/path/to/technical-adapter \
+    medical-lora=/path/to/medical-adapter \
+    legal-lora=/path/to/legal-adapter \
+  --host 0.0.0.0 \
+  --port 8000
+```
+
+**Key flags**:
+
+- `--enable-lora`: Enables LoRA adapter support
+- `--lora-modules`: Registers LoRA adapters with their names and paths
+- Format: `adapter-name=/path/to/adapter`
+
+## 2. Minimal Configuration
+
+Put this in `config/config.yaml` (or merge into your existing config):
+
+```yaml
+# Category classifier (required for intent detection)
+classifier:
+  category_model:
+    model_id: "models/category_classifier_modernbert-base_model"
+    use_modernbert: true
+    threshold: 0.6
+    use_cpu: true
+    category_mapping_path: "models/category_classifier_modernbert-base_model/category_mapping.json"
+
+# vLLM endpoint hosting your base model + LoRA adapters
+vllm_endpoints:
+  - name: "vllm-primary"
+    address: "127.0.0.1"
+    port: 8000
+    weight: 1
+
+# Define base model and available LoRA adapters
+model_config:
+  "llama2-7b":
+    reasoning_family: "llama2"
+    preferred_endpoints: ["vllm-primary"]
+    # IMPORTANT: Define all available LoRA adapters here
+    loras:
+      - name: "technical-lora"
+        description: "Optimized for programming and technical questions"
+      - name: "medical-lora"
+        description: "Specialized for medical and healthcare domain"
+      - name: "legal-lora"
+        description: "Fine-tuned for legal questions"
+
+# Default model for fallback
+default_model: "llama2-7b"
+
+# Categories with LoRA routing
+categories:
+  - name: "technical"
+    description: "Programming, software engineering, and technical questions"
+    system_prompt: "You are an expert software engineer."
+    model_scores:
+      - model: "llama2-7b"           # Base model name
+        lora_name: "technical-lora"  # LoRA adapter to use
+        score: 1.0
+        use_reasoning: true
+        reasoning_effort: "medium"
+
+  - name: "medical"
+    description: "Medical and healthcare questions"
+    system_prompt: "You are a medical expert."
+    model_scores:
+      - model: "llama2-7b"
+        lora_name: "medical-lora"    # Different LoRA for medical
+        score: 1.0
+        use_reasoning: true
+        reasoning_effort: "high"
+
+  - name: "legal"
+    description: "Legal questions and law-related topics"
+    system_prompt: "You are a legal expert."
+    model_scores:
+      - model: "llama2-7b"
+        lora_name: "legal-lora"      # Different LoRA for legal
+        score: 1.0
+        use_reasoning: true
+        reasoning_effort: "high"
+
+  - name: "general"
+    description: "General questions"
+    system_prompt: "You are a helpful assistant."
+    model_scores:
+      - model: "llama2-7b"           # No lora_name = uses base model
+        score: 0.8
+        use_reasoning: false
+```
+
+## 3. How It Works
+
+```mermaid
+graph TB
+    A[User Query] --> B[Semantic Router]
+    B --> C[Category Classifier]
+    
+    C --> D{Classified Category}
+    D -->|Technical| E[technical-lora]
+    D -->|Medical| F[medical-lora]
+    D -->|Legal| G[legal-lora]
+    D -->|General| H[llama2-7b base]
+    
+    E --> I[vLLM Server]
+    F --> I
+    G --> I
+    H --> I
+    
+    I --> J[Response]
+```
+
+**Flow**:
+
+1. User sends a query to the router
+2. Category classifier detects the intent (e.g., "technical")
+3. Router looks up the best `ModelScore` for that category
+4. If `lora_name` is specified, it becomes the final model name
+5. Request is sent to vLLM with `model="technical-lora"`
+6. vLLM routes to the appropriate LoRA adapter
+7. Response is returned to the user
+
+### Test Domain Aware LoRA Routing
+
+Send test queries and verify they're classified correctly:
+
+```bash
+# Technical query
+curl -X POST http://localhost:8080/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{"model": "MoM", "messages": [{"role": "user", "content": "Explain async/await in JavaScript"}]}'
+
+# Medical query
+curl -X POST http://localhost:8080/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{"model": "MoM", "messages": [{"role": "user", "content": "What causes high blood pressure?"}]}'
+```
+
+Check the router logs to confirm the correct LoRA adapter is selected for each query.
+
+
+## Benefits
+
+- **Domain Expertise**: Each LoRA adapter is fine-tuned for specific domains
+- **Cost Efficiency**: Share base model weights across adapters (lower memory usage)
+- **Easy A/B Testing**: Compare adapter versions by adjusting scores
+- **Flexible Deployment**: Add/remove adapters without restarting the router
+- **Automatic Selection**: Users don't need to know which adapter to use
+
+## Next Steps
+
+- See [complete LoRA routing example](https://github.com/vllm-project/semantic-router/blob/main/config/intelligent-routing/in-tree/lora_routing_example.yaml)
+- Learn about [category configuration](../../overview/categories/configuration.md#lora_name-optional)
+- Explore [reasoning routing](./reasoning.md) to combine with LoRA adapters