Router DC

Overview

router_dc embeds the request and each model description, then selects the candidate with the strongest semantic similarity.

Paper: Query-Based Router by Dual Contrastive Learning

Key Advantages

• Uses the configured embedding runtime for both requests and model profiles.
• No explicit ranking rules needed — selection is driven by description similarity.
• Useful when prompt semantics matter more than static priority or cost.

Algorithm Principle

This selector is inspired by RouterDC, but the request path does not train a dual encoder. It uses the same configured embedding function for requests and model descriptions:

1. Query Embedding: Each user query is encoded into a dense vector via the configured embedding provider.
2. Model Embedding: Each model is represented by an embedding derived from its description and optional capability tags.
3. Similarity: It computes cosine similarity, divides by temperature, and applies a sigmoid.
4. Selection: It chooses the highest score above min_similarity, then applies a second temperature-scaled softmax for the returned score map.

$$s_i = \sigma(\cos(q,m_i)/\tau), \qquad P_i = \frac{\exp((s_i-\max_j s_j)/\tau)}{\sum_j \exp((s_j-\max_k s_k)/\tau)}$$

Where $\tau$ is the temperature (temperature, default 0.07).

Select Flow

Model Embedding Initialization

Models need descriptions for embedding-based matching. Configure descriptions in modelCards:

routing:
  modelCards:
    - name: llama-3.2-1b
      description: "Fast small model for simple tasks, low cost"
      capabilities: ["summarization", "simple_qa"]
    - name: codellama-7b
      description: "Code generation specialist, good at programming tasks"
      capabilities: ["code_generation", "debugging"]

When use_capabilities: true, capability tags are concatenated with descriptions to enrich embeddings.

What Problem Does It Solve?

Some workloads are primarily semantic matching problems where the best model depends on the request meaning more than explicit heuristics. router_dc matches that request to operator-written model descriptions instead of relying only on static priority or cost rules.

When to Use

• The best candidate depends on semantic similarity between prompt and model profile.
• You want a learned selector without full online exploration.
• One route should route by semantic fit rather than only cost or latency.
• Models have descriptive profiles or capability tags.

Known Limitations

• Requires model descriptions: If models lack descriptions, embedding quality degrades.
• Cold query problem: Rare query types may not match well with any model embedding.
• Temperature sensitivity: Very low temperature makes the selector near-greedy; very high temperature makes it near-uniform.

Configuration

algorithm:
  type: router_dc
  router_dc:
    temperature: 0.07           # Softmax temperature (lower = sharper)
    min_similarity: 0.3         # Minimum similarity threshold
    require_descriptions: false # Fail if models lack descriptions
    use_capabilities: true      # Include capability tags in embeddings

Parameters

Parameter	Type	Default	Description
temperature	float	0.07	Softmax temperature (lower = more confident selection)
dimension_size	int	768	Accepted compatibility field; the current selector uses the vectors returned by the embedding function
min_similarity	float	0.3	Minimum similarity threshold for valid matches (0–1)
use_query_contrastive	bool	true	Accepted compatibility field; it does not enable request-time training
use_model_contrastive	bool	true	Accepted compatibility field; it does not enable request-time training
require_descriptions	bool	false	Require all models to have descriptions
use_capabilities	bool	true	Include capability tags in embedding text

Outcome Feedback

Use the Router Learning outcome endpoint to record replay-linked feedback for offline analysis and learning diagnostics. The router response includes x-vsr-replay-id; send that value back with the model outcome:

curl -sS -X POST http://localhost:8080/v1/router/outcomes \
  -H "Authorization: Bearer ${VSR_MGMT_TOKEN}" \
  -H "Content-Type: application/json" \
  -H "Idempotency-Key: router-dc-feedback-001" \
  -d '{
    "replay_id": "replay_01J...",
    "source": "user",
    "target": "model",
    "target_ref": "codellama-7b",
    "verdict": "good_fit",
    "reason": "good_code_response",
    "score": 1.0,
    "metadata": {
      "decision": "coding"
    }
  }'

This endpoint records data for replay and offline analysis. It does not change RouterDC's request-time similarity scores.

Router DC sends request text through the configured embedding runtime. With a remote embedding provider, that text crosses the provider boundary. Model-card descriptions and embedding thresholds must be evaluated together. See a complete example: config/fragments/algorithm/selection/router-dc.yaml.