Jev

TypeSafe Jev is a decision model. It does not generate text; it answers only questions defined in advance. A request provides the content to be evaluated and a set of questions, and the model returns a structured answer with probabilities for each question, so that the application can branch directly on the result.

Use cases

Suitable for:

• Classification and routing: the team responsible for a ticket, or the category of a user’s intent.
• Yes/no decisions: whether content contains personal information, is a refund request, or is a prompt injection.
• Scoring and ranking: urgency, relevance, sentiment intensity, and filtering of retrieval results.
• Quality control of LLM output: whether a response is on topic or violates a policy.

Not suitable for:

• Generating text, summaries, translations, or code.
• Image decisions (Jev accepts only text and JSON input; for image decisions, use GPT-6 Luna).
• Open-ended questions whose answers cannot be defined in advance as options or levels.

Comparison with a chat LLM plus JSON output:

Quick start

1. Prepare the connection details: Create an API key in the OfoxAI console  and use the following values:

2. Install the SDK: When calling through the official OpenAI Python SDK, install a version that includes client.decisions:

pip install -U openai

3. Make the call:

import os
from openai import OpenAI
 
client = OpenAI(base_url="https://api.ofox.ai/v1", api_key=os.environ["OFOX_API_KEY"])
 
r = client.decisions.create(
    model="typesafe/jev-latest",
    input="Customer message: I was charged twice, nobody has answered my two emails, and I need a refund today.",
    questions=[{"type": "predicate", "name": "is_frustrated", "instructions": "Is the customer frustrated?"}],
)
print(r.answers[0].probability)  # 0.98

curl https://api.ofox.ai/v1/decisions \
  -H "Authorization: Bearer $OFOX_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "typesafe/jev-latest",
    "input": "Customer message: I was charged twice, nobody has answered my two emails, and I need a refund today.",
    "questions": [
      { "type": "predicate", "name": "is_frustrated", "instructions": "Is the customer frustrated?" }
    ]
  }'

Example response:

{
  "model": "typesafe/jev-latest",
  "answers": [{ "type": "predicate", "name": "is_frustrated", "probability": 0.98 }],
  "usage": { "prompt_tokens": 302, "completion_tokens": 23, "total_tokens": 325 }
}

probability is the probability that the answer is “yes”; 0.98 indicates that the model considers it highly likely that the customer is frustrated. To apply this to a specific use case, replace input with the actual content and questions with the actual questions.

Question types

Select the question type according to the form of the required result:

Examples: “Is this a refund request?” uses predicate; “Which team should handle it?” uses choice; “How urgent is it?” uses score. All three questions can be placed in the same request, and all answers are returned at once.

After selecting a question type, review the questions and options against Question design.

Common errors

For the complete list of errors, see Decisions API · Common errors.

Official documentation

• TypeSafe · Documentation 
• TypeSafe · Cookbooks 

FAQ