Classifier Signal

Overview

classifier exposes reusable label scores from a local native sequence classifier or a configured external LLM. Decisions test a declared label with a required numeric predicate.

Specialized domain, PII, jailbreak, fact-check, KB, and preference signals remain the preferred interfaces for their respective domains.

Key Advantages

• integrates arbitrary sequence-classification heads without adding domain logic
• constrains LLM classifiers to declared labels and deterministic JSON output
• computes one label map that multiple decisions can gate at different scores

What Problem Does It Solve?

Some trained classifiers do not belong to the built-in signal taxonomies. The classifier signal exposes those labels and scores to decisions without mixing classification with route outcomes.

When to Use

Use this signal for a genuine reusable classification head or a prompted LLM labeler. Prefer embedding/KB signals for reference-phrase similarity and preference signals for response-style routing.

Configuration

routing:
  signals:
    classifiers:
      - name: phishing
        type: local
        model_path: models/phishing-email
        labels: [BENIGN, PHISHING]
        use_cpu: true

  decisions:
    - name: phishing-local
      description: Keep suspected phishing requests on the local model.
      priority: 200
      rules:
        operator: AND
        conditions:
          - type: classifier
            name: phishing
            label: PHISHING
            predicate:
              gte: 0.5
            on_error: no_match
      modelRefs:
        - model: local-small
          use_reasoning: false

LLM classifiers reference a named global.model_catalog.external entry and add instructions. The runtime fixes temperature, output schema, token bounds, and exact-label validation. Classifier leaves are the only decision predicates that accept on_error; failures expose the bounded classifier_evaluation_failed code in eval/replay diagnostics.

Local classifiers use model_path. One binary local classifier is supported per Router process, and its decision predicates use gte: 0.5 or higher on the winning-label confidence. Restart the Router after changing the model or label order. A management API update that requires this restart returns RESTART_REQUIRED.

The local path processes request text inside the Router. An llm classifier sends that text to its configured external model, so choose the provider and retention policy accordingly. Labels and thresholds must be evaluated as one versioned contract. See a complete example: config/fragments/signal/classifier/label-score.yaml.