Run Jev-style decision models locally with Ollama

# bash / zsh / PowerShell — same command everywhere
ollama -v

# bash
curl -fsSL https://ollama.com/install.sh | sh

ollama pull nimble      # 9B, Bespoke Labs, 9.5 GB — the most accurate of the three
ollama pull tev1        # 4B, Together AI, experimental, 4.5 GB
ollama pull tev1:0.8b   # 0.8B, Together AI, experimental, 812 MB

curl http://localhost:11434/v1/systemone \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "nimble",
    "state": {"ticket": "I was charged twice. Please refund the extra payment."},
    "questions": {
      "team": {
        "type": "choice",
        "instructions": "Which team should handle this ticket?",
        "criteria": {
          "billing": "Payments and refunds",
          "technical": "Bugs and integrations",
          "other": "None of the above"
        }
      },
      "refund": {
        "type": "noul",
        "instructions": "Does the customer explicitly ask for a refund?"
      },
      "urgency": {
        "type": "score",
        "instructions": "How urgent is this ticket?",
        "criteria": ["Routine", "Soon", "Urgent"]
      }
    }
  }'

# PowerShell 5.1 or 7+
$body = [ordered]@{
  model = "nimble"
  state = [ordered]@{ ticket = "I was charged twice. Please refund the extra payment." }
  questions = [ordered]@{
    team = [ordered]@{
      type = "choice"
      instructions = "Which team should handle this ticket?"
      criteria = [ordered]@{
        billing   = "Payments and refunds"
        technical = "Bugs and integrations"
        other     = "None of the above"
      }
    }
    refund = [ordered]@{
      type = "noul"
      instructions = "Does the customer explicitly ask for a refund?"
    }
    urgency = [ordered]@{
      type = "score"
      instructions = "How urgent is this ticket?"
      criteria = @("Routine", "Soon", "Urgent")
    }
  }
} | ConvertTo-Json -Depth 8

Invoke-RestMethod -Uri "http://localhost:11434/v1/systemone" -Method Post `
  -ContentType "application/json" -Body $body | ConvertTo-Json -Depth 8

{
  "model": "nimble",
  "answers": {
    "team": {
      "type": "choice",
      "choice": "billing",
      "probabilities": {"billing": 0.985, "technical": 0.012, "other": 0.003},
      "confidence": 0.922
    },
    "refund": {"type": "noul", "noul": 0.997},
    "urgency": {
      "type": "score",
      "score": 0.815,
      "legend": {"0": "Routine", "1": "Soon", "2": "Urgent"},
      "probabilities": {"0": 0.378, "1": 0.429, "2": 0.193},
      "confidence": 0.046
    }
  },
  "usage": {"input_tokens": 841, "output_tokens": 4}
}

# bash / zsh
pip install typesafe-sdk          # or: uv add typesafe-sdk
export TYPESAFE_BASE_URL=http://localhost:11434
export TYPESAFE_API_KEY=ollama    # any non-empty value; local Ollama doesn't check it
export TYPESAFE_DEFAULT_MODEL=nimble

# PowerShell equivalent
pip install typesafe-sdk
$env:TYPESAFE_BASE_URL = "http://localhost:11434"
$env:TYPESAFE_API_KEY = "ollama"
$env:TYPESAFE_DEFAULT_MODEL = "nimble"

from typesafe_sdk import Choice, Noul, Score, TypeSafeClient

questions = {
    "team": Choice(
        instructions="Which team should handle this ticket?",
        criteria={
            "billing": "Payments and refunds",
            "technical": "Bugs and integrations",
            "other": "None of the above",
        },
    ),
    "refund": Noul(instructions="Does the customer explicitly ask for a refund?"),
    "urgency": Score(
        instructions="How urgent is this ticket?",
        criteria=["Routine", "Soon", "Urgent"],
    ),
}

with TypeSafeClient(timeout=120) as client:  # generous timeout: the first call loads the model
    result = client.system_one(
        state={"ticket": "I was charged twice. Please refund the extra payment."},
        questions=questions,
    )

print(result.choices["team"].choice)   # billing
print(result.nouls["refund"].noul)     # ~0.997
print(result.scores["urgency"].score)  # ~0.8

// Node 18+ or any modern browser context that can reach localhost
const res = await fetch("http://localhost:11434/v1/systemone", {
  method: "POST",
  headers: { "Content-Type": "application/json" },
  body: JSON.stringify({
    model: "tev1",
    state: "Our checkout has returned 500 errors since 9am.",
    questions: {
      label: {
        type: "choice",
        instructions: "Which label fits this ticket?",
        criteria: {
          billing: "Payments and refunds",
          bug: "Software errors",
          account: "Login and account access",
        },
      },
    },
  }),
});
if (!res.ok) throw new Error(`${res.status}: ${(await res.json()).error}`);
const { answers } = await res.json();
console.log(answers.label.choice, answers.label.probabilities);

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