The Tool Desk
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Configure vLLM Semantic Router as a policy layer in front of your model backends: declare each provider model and its backend_refs, mirror the selectable models in routing.modelCards, then define ordered entries in routing.decisions[]. Each decision combines rules, candidate modelRefs, and a selection algorithm. A static decision is the safest starting point; trained selectors are appropriate only after you have representative, measured query-to-model results.
Use the canonical v0.3 YAML structure, validate the complete file with vllm-sr config validate --config config.yaml, and start the service with vllm-sr serve --config config.yaml. The Router forwards requests to configured services; it does not load model weights itself.
Understand what the Semantic Router is configuring
vLLM Semantic Router sits between your client and one or more inference services. A request reaches the Router, the policy evaluates its attributes and conditions, an eligible model is selected, and the request is forwarded to that model’s configured backend. The Router is not a model server and does not download, place, or execute model weights. Those responsibilities remain with the backend services you connect.
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- Backend inventory: provider definitions expose model aliases and the backend endpoints able to serve them.
- Routing inventory: model cards describe the models that routing policy can consider.
- Decision policy: decisions determine when a route applies, which candidates are allowed, and how one candidate is selected.
A route cannot select an alias that is absent from the configured provider models, and a decision cannot safely refer to a model that is missing from the routing model-card inventory. Treat aliases as shared identifiers and keep their spelling and capitalization consistent.
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Use the v0.3 configuration shape
The current CLI documentation describes a canonical v0.3 YAML file with five top-level areas:
| Section | Purpose | What to check |
|---|---|---|
version |
Identifies the configuration format. | Use the version expected by the installed CLI rather than copying an older example. |
listeners |
Defines where the Router accepts client traffic. | Confirm the address, exposure, and any listener-specific settings required by your deployment. |
providers |
Describes model aliases and their backend references. | Every candidate alias needs a usable backend reference, endpoint, protocol, and required authentication configuration. |
routing |
Contains model cards and the route decisions. | Keep routing.modelCards, routing.decisions, and each decision’s modelRefs aligned. |
global |
Holds configuration that applies across the service. | Use the fields supported by your installed release; do not assume settings from an older schema are still accepted. |
The vllm-sr init command was removed in v0.3. If you have an older file, migrate it with vllm-sr config migrate --config old-config.yaml, then inspect the resulting YAML instead of assuming the migration covers every operational choice.
Declare the models and backends first
Begin with the models you can actually serve. Give each one a stable alias and associate that alias with one or more backend references. A backend reference identifies the endpoint and connection details the Router will use; it is not the model’s display name.
Keep aliases consistent
The following fragment shows the relationship without pretending to be a complete file. The surrounding provider fields, endpoint syntax, and protocol values must follow the schema for your release and backend:
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providers:
models:
- name: local-model
backend_refs:
- local-backend
- name: hosted-model
backend_refs:
- hosted-backendAfter declaring the provider models, add matching entries to routing.modelCards using the canonical structure from the current CLI documentation. Then reference those same aliases in decisions. Do not create a route that points directly at an arbitrary URL while bypassing the provider and model-card inventories; that breaks the Router’s model eligibility checks.
Record endpoint and protocol requirements
For each backend reference, establish which endpoint it serves, which protocol it speaks, and which credential environment variables it requires. Test the endpoint independently before blaming routing policy. A syntactically valid route still fails if the backend is unreachable, speaks an unexpected protocol, or rejects the credential.
Keep credentials out of YAML
Store secrets in the environment or the secret-management mechanism used by your deployment. Reference the required environment-variable names from configuration as documented, rather than writing tokens into the file. If you use a recipe-specific workflow that requires additional variables, explicitly authorize those variables for that recipe. This keeps the YAML portable and prevents accidental secret disclosure in source control, logs, and configuration backups.
Build decisions in routing.decisions[]
Route policy belongs in routing.decisions[]. Each decision has a name, description, priority, rules, candidate model references, and an algorithm.
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| Field | Role in a decision | Design guidance |
|---|---|---|
name |
Stable identifier for the decision. | Use a name that explains the policy’s intent, such as a workload or fallback role. |
description |
Human-readable explanation. | State what requests the decision is intended to handle and why its candidates are suitable. |
priority |
Orders decisions when more than one could apply. | Reserve a low-priority catch-all for requests not matched by more specific policies. |
rules |
Conditions that determine whether the decision applies. | Use the documented operator and condition fields for your release; do not invent condition names. |
modelRefs |
Restricts the models eligible for this decision. | Every referenced alias must exist in both the provider model inventory and the routing model-card inventory. |
algorithm |
Chooses among the eligible candidates. | Start with a deterministic method until you have evidence that a more complex selector improves your workload. |
Start with a deterministic fallback
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: staticThis fragment is not a standalone configuration. Merge it into a complete v0.3 file containing version, listeners, providers, routing.modelCards, and global. The local-model alias must already be declared in the provider and model-card sections.
Use priority deliberately
Put narrow, high-confidence policies ahead of broad policies, and leave the empty-condition static decision as the safety net. A catch-all route prevents an unmatched request from having no eligible model, while its low priority keeps it from stealing traffic from more specific decisions. If your deployment has several overlapping rules, inspect the effective priority order in logs and test a request that could satisfy more than one policy.
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The rule answers “when does this decision apply?” The modelRefs list answers “which models may be selected if it applies?” Do not list every model in every decision. A narrow candidate set makes the policy explainable, limits accidental data movement, and gives algorithms a meaningful choice. If a selector returns a model name that is not in the decision’s candidates, the runtime reports an error rather than silently substituting another model.
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Choose a selection algorithm that matches your evidence
There is no universally correct model or algorithm. Select according to the quality, latency, cost, privacy, safety, and operational constraints of your own requests.
| Algorithm family | When it fits | Important limitation |
|---|---|---|
static |
The route already identifies one suitable model, or you need a predictable fallback. | It does not adapt to request variation. |
router_dc |
You want a documented non-trained routing option and can evaluate its behavior on your workload. | Do not assume its trade-offs match another deployment. |
automix |
You want a documented non-trained option for mixing candidate models. | Measure quality and latency rather than relying on the name of the strategy. |
multi_factor |
Your policy must balance several operational factors. | Define and measure the factors that matter; a generic weighting is not a workload guarantee. |
latency_aware |
Response time is a primary routing objective and backend latency is observable. | Latency optimization can reduce quality if quality constraints are not enforced. |
knn, kmeans, svm, mlp |
You have representative query-to-model outcome data and can validate a trained selector. | The runtime documentation labels these learned selectors experimental; they require workload-specific evaluation. |
For a first deployment, make the route correct and observable with static or another non-trained strategy. Complexity is justified only when measured gains outweigh additional data, training, and operational failure modes.
Configure the Router step by step
- Choose the deployment boundary. Decide which client traffic the Router will accept and which inference services it may call. Remember that model execution remains in those services.
- Inventory candidate models. Give each model a stable alias. For every alias, identify its backend reference, endpoint, protocol, health requirements, and credential environment-variable names.
- Populate
providers.models. Add the aliases and theirbackend_refsusing the current v0.3 schema. - Populate
routing.modelCards. Add a matching model-card entry for every alias that a decision may select. Keep the alias text identical across sections. - Define the listener and global settings. Use the fields supported by the installed release, and restrict exposure to the network boundary you intend to serve.
- Add specific decisions. For each workload class, write a description, assign a priority, express the documented rules, list only eligible
modelRefs, and select an algorithm. - Add one fallback decision. Give it a low priority, an empty condition set using the documented form, and a known-good static model.
- Provide credentials through the environment. Set and authorize the variables required by each backend or recipe before starting the service.
- Validate the complete file. Run the CLI validator against the file you will actually deploy, not a shortened fragment.
- Start the Router. Launch it with the same configuration path you validated, then send representative requests through the listener.
- Review behavior and adjust. Check which decision matched, which candidate was selected, backend latency, failures, and response quality. Change one policy dimension at a time so the effect is attributable.
Validate before sending production traffic
Use these commands from the environment where the Router will run:
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vllm-sr serve --config config.yamlValidation catches schema and reference problems before they become request-time failures, but it cannot prove that an endpoint is reachable, a credential is valid, or a model produces acceptable answers. Perform both configuration validation and live backend checks.
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Test the route matrix
- Send at least one request intended for every specific decision.
- Send a request that should match no specific rule and confirm the fallback handles it.
- Test overlapping conditions to verify priority behavior.
- Remove or disable one candidate backend temporarily and confirm the resulting error or fallback behavior is understood.
- Check that the selected alias is one of the decision’s
modelRefs. - Record quality, latency, errors, and any privacy or location constraint for each request class.
Keep representative test prompts outside production secrets. A routing policy can appear correct with a handful of easy requests while failing on long context, code, structured output, multilingual, or safety-sensitive traffic.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use the Dashboard for an initial interactive setup
The installation workflow describes starting the stack and using the Dashboard to 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 required connection details, but export and review the resulting configuration before treating it as an audited deployment artifact.
For repeatable environments, keep the reviewed YAML under version control, inject credentials at runtime, validate it in the deployment pipeline, and use the Dashboard as an operational aid rather than the only record of routing policy.
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Train a selector only when you have the right data
Learned selectors can classify queries or choose among candidates, but model names alone do not create trustworthy labels. The training documentation emphasizes the query distribution and scoring method: a selector trained on one workload can be misleading on another.
Build the training set
- Define the workload. Sample the request types, lengths, languages, and constraints that the Router will actually receive.
- Benchmark every candidate. Run those queries against each candidate model under comparable conditions.
- Choose a scoring method. Decide how quality, latency, cost, privacy, and safety will be represented. Document tie handling and unacceptable failures.
- Inspect benchmark output. Verify that labels reflect measured outcomes rather than a model’s name, reputation, or an unrelated public benchmark.
- Train an artifact. Use the supported training path for the selected algorithm and retain the metadata needed to reproduce it.
- Evaluate held-out requests. Compare the selector with a static baseline and other simple strategies on requests not used for training.
- Validate through the runtime path. Load the artifact through the Go/native runtime path used in deployment, not only the training environment.
- Deploy with a rollback. Keep a known-good static decision available if the artifact is incompatible, stale, or performs poorly after a workload change.
The learned choices listed by the project are knn, kmeans, svm, and mlp. They are experimental in the runtime documentation. A selector must return one of the decision’s candidate aliases; an out-of-set result is an error, not an automatic fallback.
Troubleshoot common failures
| Symptom | Likely cause | Fix |
|---|---|---|
| Validation rejects an unknown field | The file follows an older sample or a different release. | Check the installed CLI’s v0.3 schema, migrate an older file, and remove unsupported fields. |
| A decision never matches | Its conditions use the wrong field names, operator, or value shape, or its priority is shadowed by another decision. | Compare the rule with the canonical schema, test a request designed to satisfy it, and inspect effective priority. |
| The route reports an unknown model | An alias is missing or spelled differently in providers.models, routing.modelCards, or modelRefs. |
Normalize the alias in all three locations and validate the complete file. |
| The backend is unreachable | The endpoint, protocol, network path, or service health is wrong. | Test the backend independently, verify the backend reference, and confirm listener-to-backend network access. |
| Authentication fails | The required environment variable is unset, unauthorized for the recipe, or contains an expired credential. | Set the documented variable in the runtime environment, authorize it where required, and rotate the secret without putting it in YAML. |
| A trained selector returns an invalid model | The artifact predicts an alias outside the decision’s candidate list. | Retrain or remap the artifact so its output vocabulary exactly matches the route’s modelRefs; do not expect silent substitution. |
| Quality drops after training | The training queries or scoring method do not represent the deployed workload. | Rebuild the dataset, evaluate held-out requests, and compare against a static baseline before redeploying. |
| The Dashboard works but a deployment does not | Interactive setup supplied environment, network, or state that the service process lacks. | Export the reviewed configuration, reproduce required variables and connectivity in the deployment environment, then run CLI validation there. |
Operate the policy safely
- Version the YAML and selector artifacts together. A model alias change can invalidate a decision or a trained artifact.
- Separate policy changes from backend changes. This makes it easier to identify whether a quality or latency regression came from routing or inference.
- Log decision, candidate set, selected alias, latency, and failure reason. Avoid logging prompts or credentials unless your data-governance policy explicitly permits it.
- Set an explicit fallback. A known static model is easier to diagnose than an accidental “no route” state.
- Re-evaluate after workload shifts. New languages, longer contexts, different output formats, or changed privacy requirements can invalidate a selector trained on historical traffic.
- Recheck compatibility before upgrades. Repository documentation is mutable; verify command syntax, schema fields, algorithm status, and provider support against the release you install.
A practical decision framework
Before adding another model or algorithm, write down the acceptance criteria for each request class:
| Question | Measurement to collect | Routing implication |
|---|---|---|
| Is the answer correct enough? | Task-specific quality score on representative held-out requests. | Exclude candidates that fail the minimum quality bar. |
| How quickly must it respond? | End-to-end latency under realistic concurrency. | Use latency-aware policy only when backend measurements are available and quality remains acceptable. |
| What may it cost? | Measured per-request or per-token operational cost for the deployment. | Use a cheaper candidate only where its quality and safety meet the route’s requirements. |
| Where may data go? | Region, network, retention, and provider constraints. | Restrict modelRefs to compliant backends. |
| How much policy complexity can the team operate? | Number of rules, training refresh frequency, observability, and rollback effort. | Prefer static or other simple strategies when measured gains from learning are small. |
The “right model” is therefore a property of a request class and its constraints, not a permanent ranking of model names. Start with transparent candidates and a validated fallback, then introduce adaptive selection only when your own measurements show that it improves the service you operate.
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