"""Domain Context v4 - the scope-level semantic model.

Design and rationale: `docs/knowledge/KNOWLEDGE_DOMAIN_CONTEXT_V4.md`.

**One per company (`scope_id`), composed from that company's approved entries.**
This is the object the name always promised: not a description of a file, but a
model of the business an agent can reason about. v3's per-document object keeps
its fields and becomes a `DocumentBrief`, demoted to a source record.

The split that matters, and the reason this is cheap to build:

- **Derived** - `measures`, `surface_forms`, `policies`, `units`, `coverage` all
  fall out of entries the pipeline already extracts. Deterministic, checkable,
  and no prompt is involved.
- **Declared** - `identity`, `boundary`, `conventions`, `entities`, `dimensions`
  are the TBox: the model proposes and an expert approves them through the
  review loop (decided 2026-09-08). Absent until someone rules on them, which is
  why every one of those fields is Optional and the object is useful without
  them.

`catalog_binding` is deliberately **not stored** - it is resolved at read time
against whichever catalog the caller has, the same stopgap shape as
`fk_inference.py`, and self-disabling when a binding cannot be made.
"""

from __future__ import annotations

from datetime import datetime

from pydantic import BaseModel, Field


class Definition(BaseModel):
    """One document's account of a measure, with the document that said it.

    A list of these, rather than a single winning string, because when two
    documents disagree the disagreement IS the finding. Picking a winner is an
    expert judgement and the pipeline exists to put that in front of a human.
    """

    text: str
    doc_id: str
    term_id: str
    approved: bool = False


class Measure(BaseModel):
    """One thing the business counts, assembled from three entry kinds.

    A measure is the join that makes the pipeline's output usable: the glossary
    says what it *is*, the formula says how it is *computed*, and the rules say
    when that computation is *valid*. Any one alone is documentation; together
    they are something a planner can act on.
    """

    # Stable across documents: the canonical surface, normalised by the same
    # rule that clusters mentions inside one document. `term_id` is
    # document-scoped by design, which is right for review and wrong here - two
    # documents defining PA would otherwise be two measures.
    key: str
    # Every document-scoped id that contributed, for provenance back to review.
    term_ids: list[str] = Field(default_factory=list)
    name: str
    # Every way the documents write it, including the ones that disagree with
    # each other - the BUMA standard heads a section "Physical *of* Availability
    # (PA)" while its legend says "Physical Availability". Surfacing the
    # disagreement is a locked product decision, so both forms travel together.
    surface_forms: list[str] = Field(default_factory=list)
    definition: str | None = None
    # Populated only when documents DISAGREE. One entry means one account and
    # `definition` carries it; more than one means the measure is contested and
    # a consumer must not pick silently.
    definitions: list[Definition] = Field(default_factory=list)
    contested: bool = False
    source_docs: list[str] = Field(default_factory=list)
    unit: str | None = None
    grain: str | None = None
    formula_id: str | None = None
    formula_latex: str | None = None
    # What the formula consumes, by symbol. Resolved to `term_id`s where the
    # linker managed it; a bare symbol is expected and not a failure.
    inputs: list[str] = Field(default_factory=list)
    # Rules that constrain this measure - "use joint-survey production, else
    # truck count". This is the field that turns a correct calculation on the
    # wrong input into a correct answer.
    governed_by: list[str] = Field(default_factory=list)
    # Resolved at read time when a catalog is supplied. None means the measure
    # is described but not bound to any column the user actually has.
    catalog_binding: str | None = None
    mention_count: int = 0
    # False when the entry is being read ahead of expert approval (dev only).
    approved: bool = False


class DomainEntity(BaseModel):
    """A thing the business measures - a unit, a shift, a site. Declared."""

    name: str
    grain: str | None = None
    synonyms: list[str] = Field(default_factory=list)
    catalog_binding: str | None = None


class Dimension(BaseModel):
    """How measures are sliced. Declared."""

    name: str
    synonyms: list[str] = Field(default_factory=list)
    catalog_binding: str | None = None


class Unit(BaseModel):
    symbol: str
    meaning: str | None = None


class Conventions(BaseModel):
    """How the operator counts time and quantity. Declared, except `units`."""

    time_grain: str | None = None
    calendar: str | None = None
    units: list[Unit] = Field(default_factory=list)


class Identity(BaseModel):
    """What this domain is, and what it is not."""

    domain_name: str | None = None
    subdomains: list[str] = Field(default_factory=list)
    # What the domain does NOT cover. The field that makes refusal first-class:
    # the planner's honest-data-gap path fires today when the CATALOG cannot
    # answer, and should also fire when the DOMAIN cannot. "This standard
    # governs equipment availability, not cost" is a better answer than a forced
    # mapping onto a cost column that happens to exist.
    boundary: str | None = None
    # The expert's elaboration, when they add one. Free text, so it is theirs
    # to write and never derived.
    boundary_note: str | None = None


class SourceRef(BaseModel):
    """One document this context was built from."""

    doc_id: str
    title: str | None = None
    n_pages: int = 0
    approved_at: datetime | None = None


class Coverage(BaseModel):
    """How much of this domain is actually populated and reviewed.

    The honesty field. A context assembled from one 9-page standard must not
    present itself with the authority of a reviewed library, and a consumer
    should be able to weigh it accordingly.
    """

    n_documents: int = 0
    n_measures: int = 0
    n_policies: int = 0
    n_entries_total: int = 0
    n_entries_approved: int = 0
    # None when nothing has been reviewed - deliberately not 0.0, because "no
    # reviews yet" and "everything was rejected" are different situations.
    approved_ratio: float | None = None
    declared: bool = False


class Authority(BaseModel):
    sources: list[SourceRef] = Field(default_factory=list)
    as_of: datetime | None = None
    coverage: Coverage = Field(default_factory=Coverage)


class DomainContext(BaseModel):
    """The scope-level semantic model. One per company."""

    scope_id: str
    identity: Identity = Field(default_factory=Identity)
    measures: list[Measure] = Field(default_factory=list)
    entities: list[DomainEntity] = Field(default_factory=list)
    dimensions: list[Dimension] = Field(default_factory=list)
    conventions: Conventions = Field(default_factory=Conventions)
    # Domain-wide rules: approved rules governing no specific term. A rule tied
    # to a measure travels on that measure instead, so nothing is listed twice.
    policies: list[str] = Field(default_factory=list)
    authority: Authority = Field(default_factory=Authority)

    @property
    def is_empty(self) -> bool:
        """No measures and nothing declared - there is nothing to give a planner.

        Worth checking explicitly rather than letting an empty card into a
        prompt: a domain context that says nothing still costs tokens and still
        implies authority it does not have.
        """
        return not self.measures and not self.identity.domain_name