Reasoning

import os import jeff with jeff.Jeff.remote("https://api.jeffhub.ai", token=os.environ["JEFF_API_KEY"], timeout_s=30) as client:     decide = client.function("jeff.reasoning.entails.entails", context={        "setting": "The accounts payable team of a mid-sized furniture maker checks supplier claims before paying.",        "evidence": [            {                "source": "purchase order",                "text": "Purchase order 4471, issued 3 February 2026 to Calder Timber, for 120 oak boards at 18.50 each, delivery by 28 February 2026.",            },            {                "source": "delivery note",                "text": "Delivery note for purchase order 4471, signed at the loading bay on 26 February 2026. Received 120 oak boards, no damage noted.",            },        ],    })     decision = decide("Calder Timber delivered the full order for purchase order 4471 before the agreed date.")     print(decision.value, decision.confidence)

import os import jeff with jeff.Jeff.remote("https://api.jeffhub.ai", token=os.environ["JEFF_API_KEY"], timeout_s=30) as client:     decide = client.function("jeff.reasoning.contradicts.contradicts", context={        "setting": "The front desk team of a community health clinic reviews patient records before appointments.",        "statement_a": {            "source": "record entry",            "text": "Allergies: none known. Last reviewed 12 January 2026.",        },    })     decision = decide({        "source": "GP letter, 10 January 2026",        "text": "The patient has a confirmed penicillin allergy, recorded in 2019.",    })     print(decision.value, decision.confidence)

import os import jeff with jeff.Jeff.remote("https://api.jeffhub.ai", token=os.environ["JEFF_API_KEY"], timeout_s=30) as client:     decide = client.function("jeff.reasoning.equivalent.equivalent", context={        "setting": "The data team of an online bookshop compares supplier listings before merging duplicate products.",        "scope": "Every fact that identifies the edition counts (title, author, format, page count and year); wording and punctuation do not.",        "item_a": "The Lighthouse Keeper's Daughter by Ana Rivas, paperback, 312 pages, published 2021.",    })     decision = decide("Ana Rivas, The Lighthouse Keeper's Daughter (paperback edition, 2021), 312 pp.")     print(decision.value, decision.confidence)

import os import jeff with jeff.Jeff.remote("https://api.jeffhub.ai", token=os.environ["JEFF_API_KEY"], timeout_s=30) as client:     decide = client.function("jeff.reasoning.satisfies.satisfies", context={        "context": {            "organisation": "A regional housing association",            "checker": "Lettings officer",            "item_kind": "Proof of income",            "date": "2 November 2026",        },        "requirement": "The document must be dated in August, September or October 2026.",    })     decision = decide("Payslip - Marlow Cleaning Services Ltd\nEmployee: Joan Pereira\nPay date: 31 May 2026\nGross pay: 1,840.00  Net pay: 1,512.36")     print(decision.value, decision.confidence)

import os import jeff with jeff.Jeff.remote("https://api.jeffhub.ai", token=os.environ["JEFF_API_KEY"], timeout_s=30) as client:     decide = client.function("jeff.reasoning.is-complete.is_complete", context={        "context": {            "organisation": "A small business lender",            "reviewer": "Loan operations analyst",            "pack_kind": "Business loan application",            "purpose": "Check the application pack is complete before credit review",            "review_date": "2 November 2026",        },        "checklist": [            "Signed loan application form (all pages)",            "Business bank statements for the last three months",            "Photo identification of every director",            "Latest annual accounts (only for businesses trading more than two years)",        ],    })     decision = decide({        "case": {            "name": "Ferngrove Bakery Ltd",            "reference": "APP-20931",            "trading_since": "March 2025",            "directors": [                "Priya Natarajan",                "Tom Okafor",            ],        },        "documents_received": [            {                "kind": "Loan application form",                "date": "28 October 2026",                "pages": "4 of 4",                "signatures": "signed by Priya Natarajan",            },            {                "kind": "Business bank statements",                "date": "July, August and September 2026",                "pages": "complete",            },            {                "kind": "Passport",                "date": "valid until 2031",                "about": "Priya Natarajan",            },        ],    })     print(decision.value, decision.confidence)

import os import jeff with jeff.Jeff.remote("https://api.jeffhub.ai", token=os.environ["JEFF_API_KEY"], timeout_s=30) as client:     decide = client.function("jeff.knowledge.knowledge-qa.question", labels={        "A": "Antibiotics neutralise warfarin in the stomach, so the dose stops working.",        "B": "Some antibiotics can strengthen warfarin's effect and raise the risk of bleeding.",        "C": "Antibiotics thicken the blood, so the patient is more likely to form clots.",        "D": "Warfarin stops antibiotics from being absorbed, so the infection may not clear.",    })     decision = decide("A patient taking warfarin is prescribed a course of antibiotics. Why should the patient's INR (a blood clotting test) be checked more often during the course?")     print(decision.value, decision.confidence)