Decisions¶

A decision is a spec paired with a context. model.decide(context, spec, risk=0.01) returns the result type that matches the spec. Each spec also has a shortcut method on the model so you can skip the spec constructor for simple cases.

Spec	Shortcut	Answer
`Choice(question, options)`	`choose`	an option id, or `None` when no option applies
`MultiChoice(question, options)`	—	the option ids that apply
`YesNo(question)`	`yes_no`	`True` or `False`
`Verify(claim)`	`verify`	`supported`, `contradicted`, or `not_enough_information`
`Rank(question, candidates)`	`rank`	candidate ids ordered best first
`Rate(question, levels)`	`rate`	a level id; levels are given lowest first
`Estimate(question, low, high, unit)`	`estimate`	a number in `[low, high]`, with a confidence interval

Options, candidates, and levels are either a list of ids or a mapping from id to a description the model reads as context for that option.

Context¶

A context is anything the model reads as the evidence for the decision. It can be:

a str — a single passage of text;
a list[str] — multiple passages, each treated as a separate unit;
a dict — read as a flat JSON state, with keys as field names;
a Context — a structured combination of typed passages, tables, and fields.

from mimir import Context, Field, Passage, Rate, Table

context = Context(
    passages=[Passage(title="Ticket #4412", text="The export has failed every night this week.")],
    tables=[Table.from_rows([["2026-03-02", "failed"]], header=["date", "status"])],
    fields=Field.from_json({"customer": {"plan": "enterprise", "seats": 240}}),
)
result = model.decide(context, Rate("How urgent is this?", ["low", "medium", "high"]), risk=0.01)

Numbers and dates in tables and fields are read as typed values, not as plain text.
Table.from_dataframe(frame) reads a pandas or polars DataFrame: its columns become the header and missing values become blank cells.
Field.from_json(value) flattens nested JSON into fields named by their path (customer.plan, customer.seats).

Results¶

Every result carries: status, answer, confidence, relevant_context, deferral, certificate, and latency_ms.

All types except Estimate also carry probabilities. Choice, yes/no, and verify results add abstain_probability and a conformal prediction_set. A rating result includes the contiguous levels in its set. An estimate result includes its interval at alpha.

relevant_context lists passages, tables, table rows, and fields by their share of the evidence, highest first, so you can show users exactly what the model relied on.

Batching and async¶

decide_many takes a list of (context, spec) pairs and batches them by token length for efficiency. Every method has an async counterpart: adecide, adecide_many, achoose, ayes_no, averify, arank, arate, aestimate.

decide_uncertified returns the raw model answer with no policy applied — no certificate, no deferral. Use it when you want the model's view without any threshold enforcement.

Limits¶

The maximum number of options, levels, candidates, and context tokens for a given release are reported by model.info(). A request that exceeds any limit raises InputLimitError, which names the value that exceeded the limit and the limit itself.