Configure vLLM Semantic Router as a policy layer in front of your model backends: declare every backend and model alias, define a default, add ordered decisions under routing.decisions[], restrict each decision to eligible modelRefs, and validate the complete YAML before serving. Start with deterministic rules and a static selector; move to a learned selector only after you have representative, measured query-to-model results.
What vLLM Semantic Router does—and what it does not do
vLLM Semantic Router accepts a request, evaluates your routing policy, and forwards the request to one of the model services you have configured. It can choose among hosted or self-managed endpoints according to rules such as request type, metadata, or other supported conditions. The Router is not a model server: it does not load model weights or replace the inference backends themselves. Those backends must already be running and reachable.
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That separation determines the configuration order. First make each backend reachable and give it a stable model alias. Then describe which aliases are eligible for each route and how the selector should choose among them. A route cannot select a model that is missing from the provider declarations or from the route’s candidate references.
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The current CLI documentation describes a portable YAML file with five top-level sections:
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versionidentifies the configuration format.listenersdefines where the Router accepts client traffic.providersdeclares model aliases, backend references, protocols, endpoints, and credential variable names.routingcontains model cards and the ordered decisions that select among them.globalholds deployment-wide settings supported by your installed release.
Use the field names and nesting from the current v0.3 schema. Older examples can use a different structure, so do not paste an earlier sample into a new deployment without checking it against the version of the CLI you installed.
A minimal route fragment
This is the documented shape of a final static fallback decision:
routing:
decisions:
- name: local-fallback
description: Handle requests not matched earlier.
priority: 0
rules:
operator: AND
conditions: []
modelRefs:
- model: local-model
algorithm:
type: staticThe fragment is not a complete configuration. Before it can work, local-model must be declared in providers.models with a valid backend reference and also represented in routing.modelCards. Add it to the full file that contains your listener and global settings.
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Prepare the model backends and aliases
1. Decide which services the Router may call
List the models that can actually serve traffic for this deployment. A common arrangement is a fast, inexpensive model for routine prompts and a larger model for requests that need more capability. The services can be local or remote; the Router only needs a supported endpoint, protocol, and network path.
2. Give each backend a stable alias
Use an alias such as local-model or another name that is unique within the configuration. The alias is the name used by modelRefs; it is not necessarily the provider’s public model identifier. Keep the mapping unambiguous so an operator can tell which endpoint will receive a request.
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3. Keep credentials out of YAML
Declare the names of credential environment variables where the provider schema requires them, but do not put secret values in the file. Export the variables in the Router’s runtime environment or inject them through your secret-management system. If you use a recipe-specific workflow, authorize the variables that recipe is allowed to read rather than granting access to every environment variable.
4. Check connectivity before adding complex policy
- Confirm that each backend is listening on the configured address and port.
- Verify the protocol and request format expected by that endpoint.
- Check that the Router host can resolve and reach the endpoint.
- Confirm that the credential variable exists in the service environment and has the required permission.
A routing rule cannot repair an unavailable backend. Prove basic connectivity first, then troubleshoot policy separately.
Declare candidate models in both routing locations
The provider declaration answers “where can this alias be served?” The routing model card answers “which model does the policy know about?” The decision’s modelRefs then limits the eligible set for that decision. Keep the names identical in all three places.
| Configuration location | Purpose | What must line up |
|---|---|---|
providers.models |
Maps a model alias to one or more backend references. | The alias must be unique and its backend references must be valid. |
routing.modelCards |
Describes the model to the routing layer. | The card’s model name must match the provider alias. |
routing.decisions[].modelRefs |
Defines the candidates a particular decision may select. | Every referenced model must exist in the provider and model-card declarations. |
When a selector returns a model name that is not in the decision’s candidate references, the runtime reports an error instead of silently substituting another model. Treat that behavior as a safety feature: it exposes a broken mapping before traffic is sent to an unintended backend.
Build decisions in priority order
Routing policy belongs in routing.decisions[]. Each decision has a name, description, priority, rules, candidate modelRefs, and an algorithm. Arrange priorities so the most specific cases are considered before broad fallbacks. Reserve a low-priority decision with an empty condition set for requests that do not match an earlier rule.
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Write narrow rules first
Start with a rule that clearly identifies one workload, such as a request class or an explicitly supplied attribute supported by your schema. Use the documented condition fields and combine them with the specified operator. An AND rule should require every listed condition; an empty condition list is useful for a catch-all fallback, not for a high-priority specialist route.
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Put only models that are appropriate for that decision in modelRefs. A translation route might include only models you have evaluated for that language pair; a low-latency route might include only endpoints that meet your response-time objective. Candidate lists are policy boundaries, not merely hints.
Add a final fallback
Give the fallback the lowest applicable priority and an empty condition set, as in the fragment above. Its model should be healthy, authorized, and capable of handling requests that do not match specialist routes. Without a fallback, unmatched traffic can fail even though individual specialist routes are valid.
Choose an algorithm that matches your evidence
The algorithm is selected inside each decision. It should reflect what you know about the eligible models and what you can measure in production.
| Algorithm family | Use it when | Main requirement or caution |
|---|---|---|
static |
The rule has already narrowed the route to a known model or a deliberately fixed fallback. | Simple and predictable; it does not optimize among candidates. |
router_dc |
You want a documented non-trained routing strategy for the eligible candidates. | Measure its behavior on your own workload rather than assuming a universal winner. |
automix |
Your policy calls for a documented non-trained mixture strategy. | Confirm the candidate models and operational assumptions in the release documentation. |
multi_factor |
You need to account for multiple operational factors when selecting among candidates. | Define and measure the factors that matter to your service. |
latency_aware |
Observed response time is a primary routing constraint. | Use measurements from the same deployment conditions and traffic pattern. |
knn, kmeans, svm, or mlp |
You have representative query-to-model outcome data and can evaluate a trained selector. | These selectors are experimental; they require an artifact, validation, and held-out testing. |
For a first deployment, use deterministic rules and a static algorithm. That gives you an observable baseline and makes configuration errors easier to isolate. Add a non-trained strategy only when its objective is clear. Consider a learned selector only after collecting outcomes for the actual request distribution.
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Validate the complete YAML before serving
- Save the complete configuration, including all top-level sections, provider declarations, model cards, decisions, and environment-variable references.
- Run
vllm-sr config validate --config config.yaml. - Fix every schema, reference, or value error reported by the CLI. Do not treat a warning about an unresolved model or credential as harmless.
- Start the Router with
vllm-sr serve --config config.yaml. - Send representative requests through the listener and inspect which backend handled each request.
Validate the file you will actually serve, not a shortened decision fragment. A route can look correct while the full document fails because a provider reference, model card, listener, or global field is missing.
Set up interactively with the Dashboard
The installation workflow also supports an initial Dashboard setup. Start the stack, open the Dashboard, connect a model, and select a default route. When no configuration file is present, the Dashboard can run in setup mode while the Router waits for configuration. This is useful for discovering the expected provider connection details, but export and review the resulting policy before treating it as production configuration.
The vllm-sr init command was removed in v0.3. For an older file, use vllm-sr config migrate --config old-config.yaml, then validate the migrated result. Migration is not a substitute for reviewing model aliases, credentials, priorities, and candidate lists.
Train a selector only when the workload justifies it
A learned selector is not created by listing model names. It needs examples that connect a query to measured outcomes from candidate models. The available experimental choices are K-nearest neighbors (knn), K-means (kmeans), support vector machines (svm), and a multilayer perceptron (mlp).
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Sample the kinds of prompts your service actually receives, including short and long inputs, languages, domains, safety-sensitive cases, and peak-period traffic if those affect model behavior. A dataset made only from synthetic or unusually easy prompts can teach the selector the wrong boundary.
Benchmark every candidate
Run each representative query against every candidate model. Preserve the model output, the scoring method, elapsed time, failures, token usage where available, and any operational cost measure you intend to optimize. The label must describe an outcome that matters to your service, not merely the model’s name.
Train and inspect the artifact
Use the project’s training workflow to create the selector artifact, inspect its benchmark output and scoring method, and ensure the artifact references model names that exist in the decision’s modelRefs. The runtime must be able to load the artifact through the Go/native execution path used by the Router.
Evaluate against simple baselines
Hold out queries that were not used for training. Compare the learned policy with a fixed-model baseline and with a straightforward static or non-trained route. Examine quality, latency, failure rate, operational cost, privacy or location constraints, and safety behavior. A selector that improves one metric while violating a hard requirement is not an acceptable improvement.
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There is no model that is universally right for every request. Define the criteria before tuning the policy:
| Criterion | Questions to answer | How to use it in policy work |
|---|---|---|
| Task quality | Does the response meet your accuracy, instruction-following, and safety requirements on held-out requests? | Reject routes that fail a minimum quality bar even if they are fast or inexpensive. |
| Latency | What are the observed response times at the concurrency and prompt lengths your users generate? | Use measured values for latency-aware decisions and capacity planning. |
| Operational cost | What does each backend cost under your actual token, request, or infrastructure mix? | Use cost as an optimization factor only after quality and hard constraints are satisfied. |
| Privacy and location | May a request leave your network or region, and which data classes are permitted on each endpoint? | Keep ineligible models out of the route’s candidate list rather than relying on a late check. |
| Safety and reliability | How does each model handle refusal, sensitive content, overload, and endpoint failure? | Record failures and define a safe fallback or an explicit error path. |
| Policy complexity | Can operators explain, validate, and roll back the decision? | Prefer the simplest policy that meets the measured objective. |
Test routing before exposing it to users
Check the happy path
- Send a request that should match each specialist decision.
- Confirm that the expected candidate set is used and that the selected backend receives the request.
- Send a request that matches no specialist condition and confirm that the fallback handles it.
Check negative and boundary cases
- Remove or alter a condition attribute and verify that the request does not accidentally enter a specialist route.
- Test empty, unusually long, multilingual, and malformed inputs that your listener accepts.
- Temporarily make one candidate unavailable and observe whether the configured behavior is an explicit error, another eligible choice, or the fallback you intended.
- Restart the Router and confirm that environment variables, artifacts, and backend connections are restored.
Record decisions for operations
Log the decision name, selected model alias, request identifier, latency, and outcome fields that your privacy policy permits. Avoid logging prompts or secrets by default. These records let you discover a priority mistake or a degraded backend without guessing from aggregate latency alone.
Troubleshoot common configuration failures
| Symptom | Likely cause | Correction |
|---|---|---|
| Validation reports an unknown field or invalid nesting. | The file uses an older schema or a misspelled v0.3 field. | Compare the complete file with the current v0.3 schema; migrate an older file, then edit and validate it. |
| A decision references a model that cannot be selected. | The alias is absent from providers.models, routing.modelCards, or modelRefs uses a different spelling. |
Make the alias identical in all required locations and validate again. |
| The runtime rejects a learned selector’s result. | The artifact returns a model outside the decision’s candidate references. | Retrain or configure the artifact with the exact aliases allowed by that decision. |
| Requests reach the Router but not the backend. | The endpoint, protocol, network route, or credential variable is wrong. | Test backend reachability from the Router host and verify the injected environment variable and protocol settings. |
| All requests use the fallback. | A specialist condition never matches, its priority is lower than the fallback, or the request lacks the expected attribute. | Test the condition with a known matching request, inspect priorities, and keep the fallback last in logical order. |
| The migrated file validates but behaves unexpectedly. | Migration converted syntax but not your intended aliases, priorities, or candidate boundaries. | Review each decision semantically and run representative requests before production use. |
| The trained route looks good in a benchmark but performs poorly in production. | The training distribution, endpoint conditions, or scoring method differs from live traffic. | Rebuild the dataset from representative traffic, use held-out evaluation, and compare with a simple baseline. |
Production checklist
- Every backend is reachable from the Router and uses the intended protocol.
- Every provider model alias is unique and appears consistently in model cards and candidate references.
- Secrets are supplied through environment variables, not committed to YAML.
- Specific decisions have clear priorities, conditions, and candidate lists.
- A healthy, authorized fallback exists for unmatched requests.
- The complete file passes
vllm-sr config validate --config config.yaml. - The served process uses the same file that was validated.
- Representative matching, non-matching, boundary, and backend-failure requests have been tested.
- Any learned selector is labeled experimental, validated through the Go/native runtime path, and compared with held-out simple baselines.
- Operational logs and rollback procedures are ready before exposing the listener to users.
A practical rollout plan
- Start with one backend and a fallback. Validate the listener, credentials, and request path without routing complexity.
- Add a second candidate. Give it a provider alias and model card, then route a narrow, easily measured workload to it.
- Introduce explicit priorities. Add specialist decisions above the catch-all fallback and test both matching and non-matching requests.
- Measure the baseline. Capture quality, latency, failures, cost, privacy constraints, and safety outcomes for each workload.
- Try a non-trained algorithm if needed. Keep the candidate list constrained and compare results with the static baseline.
- Train only with sufficient evidence. Build representative labels, hold out evaluation data, validate the artifact, and deploy it behind a reversible change.
- Review after model or endpoint changes. A new model version, region, concurrency limit, or scoring rule can invalidate previous routing results.
The reliable path is therefore incremental: make aliases and references correct, use transparent rules first, validate the complete YAML, and let measured workload results—not a generic leaderboard—determine when a more sophisticated selector is worthwhile.
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