"""Deterministic routing gate — policy check over the router's intent. After the LLM router picks an intent, the gate checks it against the per-analysis Analysis State and returns the **effective** intent: allow as-is, or redirect. No LLM, no I/O in `gate()` itself. Only one rule has teeth in v1: an analytical request (`structured_flow`) requires a validated problem statement (`problem_validated is True`); otherwise it is redirected to `problem_statement` so the user defines the goal first. Everything else passes through. `generate_report` is not a router intent (button / report API), so it is not gated here. `AnalysisState` is the locked 8-field contract (mirrors the `analysis_states` DB table). `get_analysis_state` reads the real per-analysis row via `AnalysisStateStore` (#9, landed); it fails closed to a not-validated stub on a missing row or read error. See `ORCHESTRATOR_REWORK_PLAN.md` §4. """ from __future__ import annotations from datetime import UTC, datetime from pydantic import BaseModel from src.agents.orchestration import Intent from src.middlewares.logging import get_logger logger = get_logger("gate") class AnalysisState(BaseModel): """Per-analysis state the gate + Help skill read every turn (locked contract). `problem_validated` is the gate driver; `report_id` is null until a report exists. Field names mirror the `analysis_states` table so the DB read swaps in without touching readers. """ id: str analysis_title: str problem_statement: str problem_validated: bool = False owner_id: str report_id: str | None = None created_at: datetime updated_at: datetime def gate(intent: Intent, state: AnalysisState) -> Intent: """Return the effective intent after applying the deterministic gate policy. `structured_flow` requires `problem_validated is True`; otherwise redirect to `problem_statement`. All other intents pass through unchanged. """ if intent == "structured_flow" and not state.problem_validated: logger.info( "gate redirect", requested=intent, effective="problem_statement", reason="problem_not_validated", ) return "problem_statement" return intent def stub_analysis_state(*, problem_validated: bool = False) -> AnalysisState: """Hardcoded Analysis State for building/testing before the DB lands (#9). Shared fixture so the gate, the Help skill, and tests all exercise the same shape. `problem_validated=True` simulates a passed interview. """ now = datetime.now(UTC) return AnalysisState( id="stub-analysis", analysis_title="Stub analysis", problem_statement="Stub problem statement" if problem_validated else "", problem_validated=problem_validated, owner_id="stub-user", report_id=None, created_at=now, updated_at=now, ) async def get_analysis_state(analysis_id: str) -> AnalysisState: """Load the Analysis State for an analysis (shared id with the chat room). Reads the `analysis_states` row via `AnalysisStateStore`. Never-throw seam: a missing row (e.g. a legacy room created before this table) or a read failure degrades to a **not-validated** stub, so the gate fails closed (→ steer to `problem_statement`) rather than running ungated analysis. The store import is lazy so this module stays import-safe without a DB. """ try: from src.agents.state_store import AnalysisStateStore state = await AnalysisStateStore().get(analysis_id) except Exception as exc: # noqa: BLE001 — never-throw; fail closed to not-validated logger.warning( "get_analysis_state read failed — default not-validated", analysis_id=analysis_id, error=str(exc), ) return stub_analysis_state(problem_validated=False) if state is None: logger.debug("analysis_state missing — default not-validated", analysis_id=analysis_id) return stub_analysis_state(problem_validated=False) return state