LocalAI/docs/content/features/systemone.md

+++ disableToc = false title = "SystemOne decisions" weight = 66 url = "/features/systemone/" +++

SystemOne is an API for fast, typed decisions. You send a piece of text (the state) and a set of named questions. A decision model answers each question with a value and a confidence, in one pass. The model does not generate text, so there is nothing to parse and no free-form output to validate.

The request and response shapes follow the kev project and match the /v1/systemone endpoint that Ollama added in 0.35.

Endpoints

Endpoint	Method	Description
/v1/systemone	POST	Answer all questions in one pass
/v1/systemone/permute	POST	Re-run one choice question under n_perm option orders
/v1/systemone/separate	POST	Answer each question in its own pass

Which route a model can serve depends on its kind:

Model kind	/v1/systemone	/permute and /separate
Decision model (systemone), such as Laya or GLiNER2.5-Decide	Yes	No, returns 400
Zero-shot NER model (token_classify), such as GLiNER2.5	Yes, through the NER path	Yes

Question types

Type	Answer	Fields in the answer
choice	One option out of a named set	choice, probabilities, confidence
noul	Yes, no or unknown for a statement	noul (0 to 1), entities
score	One level on a scale	score, legend, probabilities, confidence

Example

curl http://localhost:8080/v1/systemone -H "Content-Type: application/json" -d '{
  "model": "laya-vllm-cpp",
  "state": "My order arrived broken and I want my money back. This is the second time.",
  "questions": {
    "team": {
      "type": "choice",
      "instructions": "Which team should handle this ticket?",
      "criteria": {
        "billing": "Payments, invoices and refunds",
        "shipping": "Delivery and damaged goods",
        "product": "Questions about how the product works"
      }
    },
    "refund_requested": {
      "type": "noul",
      "instructions": "The customer explicitly asks for a refund"
    },
    "urgency": {
      "type": "score",
      "instructions": "How urgent is this ticket?",
      "criteria": ["not urgent", "somewhat urgent", "urgent", "critical"]
    }
  }
}'

Answers from a decision model carry a confidence value, and the response reports token usage and latency_ms. The NER path does not report token usage.

Choosing a model

A model can serve SystemOne only if it is a decision model. Declare the usecase in the model config:

name: laya
backend: vllm-cpp
known_usecases:
  - systemone
parameters:
  model: convaiinnovations/laya

systemone is never guessed, and a model that declares it is not listed as a chat, completion or embeddings model. A model that declares usecases without systemone or token_classify gets a 400 from these endpoints that names the missing usecase. A model that declares token_classify and not systemone is served by the zero-shot NER path. A vllm-cpp config that declares no usecases is treated as a decision model, so setups that predate the flag keep working, but a config that declares only chat (as an older laya gallery entry did) now gets the 400 and needs known_usecases: [systemone].

Install one from the gallery and filter on the systemone tag:

Gallery entry	Model	Notes
laya-vllm-cpp	Laya	ModernBERT-large, non-autoregressive, about 800 MB
gliner25-decide-vllm-cpp	GLiNER2.5-Decide	DeBERTa-v3-large with a classification head, about 2 GB

The engine, [vllm.cpp]({{% relref "features/vllm-cpp" %}}), also supports the kev, CLM and xor decision models. Those checkpoints need a conversion step, so they are not gallery entries yet.

Tev1 is an autoregressive decision model. It answers through chat completions and does not serve /v1/systemone yet.

Access control

When authentication is on, the three routes need the systemone feature. It is on by default for every user, like the other API features, and an administrator can turn it off per user.