use-jev

Jev is a newly released general classifier. Give it text or application state and typed questions; it returns decisions and probabilities your code can use for routing, ranking, extraction, or verification.

Read the upstream llms.txt to find current documentation, then follow the pages relevant to the task:

• Use cases: explore applications; see worked examples for function calling, reranking, and citation checks.
• Best practices: provide relevant state, ask focused questions, define clear criteria, and batch independent questions. Keep workflow logic in code.
• Confidence: use uncertainty to decide when to escalate, and validate thresholds on representative data.
• Python SDK, JavaScript SDK, or HTTP API: read before integrating.

Choice, Score, and Noul

Install typesafe-sdk and check that JEV_API_KEY is set in the environment, otherwise ask the user to provide it. This Python example asks one question of each type in a single call:

import os

from typesafe_sdk import Choice, Noul, Score, TypeSafeClient

with TypeSafeClient(api_key=os.environ["JEV_API_KEY"]) as client:
    result = client.system_one(
        state={"ticket": "PDF export fails, but CSV works. Please refund this month."},
        questions={
            "team": Choice(
                instructions="Which team should handle the main issue in the ticket?",
                criteria={"billing": "Charges and payments",
                          "technical": "Broken product features", "other": "Neither"},
            ),
            "impact": Score(
                instructions="How much does the reported bug affect functionality?",
                criteria=["Cosmetic only", "Feature broken; workaround available",
                          "Work blocked; no workaround available"],
            ),
            "refund": Noul(instructions="Does the ticket explicitly request a refund?"),
        },
    )

print(result.choices["team"].choice)
print(result.scores["impact"].score)
print(result.nouls["refund"].noul)