The Plan derives its Valuation Uplift (ADR-0018) from its baseline -> post
band jump and works+contingency cost, given one external input — the
Property's current market value (a Property Valuation, mostly absent).
`Plan.valuation` / `Plan.baseline_epc_rating` are derived like the other
headline figures; `PlanModel.from_domain` maps the £ forms to the live
plan.valuation_* columns (NULL when no value — the percentage is not
persisted on those columns). `Property.current_market_value` is the new
optional source; the orchestrator threads it onto the Plan. `run_one`
takes a `current_market_value` so the harness can value the uplift, and
the sense-check table shows the average % (always) plus the £ forms when
known.
Sourcing the current market value (upload / default) remains deferred
(ADR-0018); it is None throughout until that lands, so the columns stay
NULL at scale.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Expand half of the recommendation_materials retirement (ADR-0017). A
Plan Measure installs a single Product, so thread its catalogue id end to
end — Product.id -> MeasureOption.material_id -> PlanMeasure.material_id
-> recommendation.material_id — replacing the per-material BOM child
table with one nullable column on the row. ProductPostgresRepository
reads the id from MaterialRow; the four fabric generators set it on their
Option; the orchestrator carries it onto the Plan Measure; the mirror
declares + maps the column. Optional throughout (the JSON stopgap
catalogue carries no ids -> NULL).
The multi-measure integration test now pins each persisted measure's
material_id to its seeded MaterialRow id. Migration spec (live column
must be added before this deploys; contraction is the owner's next step)
in docs/migrations/recommendation-material-id.md.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Slice 3. `harness.console.run_one(epc, goal_band=...)` wires the full
AraFirstRunPipeline against in-memory fakes — no Postgres, no network —
runs one property, prints the sense-check table, and returns the Plan
for interactive poking from a REPL at the worktree root. Defaults to the
committed harness sample catalogue.
Refactors the slice-1 integration test to delegate to run_one (dropping
~70 lines of duplicated wiring + the now-unused test catalogue fixture),
so it exercises the shipped entrypoint rather than a parallel copy. The
new console test covers run_one's print/return contract.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Slice 2. `harness.plan_table.format_plan_table(plan)` renders a Plan as a
plain-text table — one package summary line (baseline SAP/band -> post
SAP/band, CO2 saved, cost of works + contingency, bill saved) and one
line per Plan Measure (signed SAP points, cost, delivered kWh + £
savings). Pure presentation: reads the Plan, computes nothing. The
DB-less First Run test now prints it (visible under `pytest -s`) so the
modelled package can be eyeballed and debugged by hand.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Slice 1 of the DB-less inspection harness. Complete the in-memory
FakeUnitOfWork so the ModellingOrchestrator runs with no Postgres:
add FakeScenarioRepository + FakePlanRepository (idempotent, keyed by
(property_id, scenario_id)), expose scenario/product/plan on the fake
unit, and grow FakePropertyRepo to compose the effective EPC from the
EPC repo at read time — mirroring PropertyPostgresRepository, so the
EPC Ingestion persists is visible to Baseline + Modelling (the
through-repos hand-off, in memory).
The new integration test drives the full AraFirstRunPipeline
(Ingestion -> Baseline -> Modelling) against the FakeUnitOfWork — no
Session ever opened — on the uninsulated 000490 fixture with its lodged
recorded-performance filled in (it already carries the RHI block, so
Baseline can run) and asserts a multi-measure Plan is produced. The
committed product catalogue prices the wall/floor/ventilation measures
it fires.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Move the scenario and installed_measure tables into
infrastructure/postgres/modelling/ as full-parity SQLModel definitions
(ScenarioModel, InstalledMeasureModel + MeasureType), completing the cluster
consolidation. backend/app/db/models/recommendations.py is now a pure
re-export shim.
ScenarioModel.goal is the PortfolioGoal enum (legacy planning branches on it),
sourced from domain/modelling/portfolio_goal.py; the repo's to_domain maps it to
its value string, so domain Scenario.goal is now the value ("Increasing EPC")
consistent with the orchestrator's check — fixing the latent name-vs-value
inconsistency the old str column masked (the scenario repo test stored the enum
*name*). Parity columns are nullable (mirror convention; live NOT-NULLs owned by
Drizzle).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Standardise the modelling persistence classes on the …Model suffix (PlanModel,
RecommendationModel, RecommendationMaterialModel) — matching the epc_property
precedent and the legacy names the rest of backend/ already imports, so the
shim's plan re-export becomes literal (no alias) and the eventual shim deletion
needs zero renames. The …Row→…Model sweep for the non-cluster tables
(Property/Task/Material/…) waits until their live legacy …Model counterparts
are retired, to avoid reintroducing dual-definition collisions. No behaviour
change.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Move the live plan, recommendation, recommendation_materials and (retiring)
plan_recommendations tables into a new infrastructure/postgres/modelling/
subpackage as single SQLModel definitions (the epc_property pattern), absorbing
the rebuild's partial PlanRow/RecommendationRow mirrors and carrying full
legacy column parity plus recommendation.plan_id. Out-of-cluster references are
plain indexed ints (mirror convention); the live FKs are owned by the Drizzle
schema. backend/app/db/models/recommendations.py becomes a re-export shim
(ScenarioModel/InstalledMeasure stay for a later slice).
Fix the export conftest to create SQLModel-first (so Base funding_package's FK
to the now-SQLModel plan resolves) and skip the redundant drop_all on its
function-scoped throwaway DB (the epc enum type is now shared across both
metadatas). Resolves the pre-existing dual-definition collision: the rebuild
and legacy export suites are now co-runnable. No behaviour change.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
`_plan_for` now scores the baseline + every cumulative prefix once
(`cascade_scores`, best-practice order) and reuses those Scores for both the
role-3 marginal attribution and a per-measure bill cascade: bill each prefix at
one Fuel Rates snapshot and take consecutive Bill deltas as each measure's
marginal delivered-kWh and £ saving. Saving is signed (ventilation is
negative) and telescopes exactly to the Plan headline savings, because the
Plan's baseline/post Bills are now the same cascade endpoints (`bills[0]` /
`bills[-1]`) — which also drops the redundant standalone baseline `calculate`.
`recommendation.kwh_savings` / `energy_cost_savings` are filled from these.
Adds `Bill.total_consumption_kwh` (shared by Plan + the orchestrator). Pinned
end-to-end on the real calculator: Σ per-measure savings == the Plan totals
(ADR-0014 amendment).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
ModellingOrchestrator gains a constructor-injected FuelRatesRepository (mirrors
Baseline): run() resolves get_current() once and reuses one BillDerivation across
the batch. _plan_for prices the baseline and post-package end-states from the
SapResults already on their Scores (no extra calculate) and passes the Bills to
Plan. PlanRow mirror + from_domain gain the four live columns post_energy_bill /
energy_bill_savings / post_energy_consumption / energy_consumption_savings.
Pipeline/handler wire the fuel-rates repo. Integration tests assert the columns
persist: the multi-measure (fallback) plan shows positive bill+consumption
savings; the already-at-target zero-measure plan shows the current bill with
exactly zero savings. Fuel-switch measures price at the new fuel for free (we
bill the simulated end-state). 183 modelling/billing/orchestration/repo tests
pass, pyright strict clean. Plan-level only; per-measure savings next.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Bill / EnergyBreakdown / BillDerivation / sap_fuel were under
domain/property_baseline/ only because Baseline was built first. The Modelling
stage now needs them too, so move them (and their tests) to a neutral
domain/billing/ — Fuel/FuelRates already live in the shared domain/fuel_rates/.
Avoids a modelling -> property_baseline cross-stage import and a package name
that wrongly implies ownership (ADR-0011, ADR-0014 amendment). Pure git mv +
import rewrite across 10 files; 40 billing/baseline/repo tests pass, pyright
strict clean. CONTEXT.md Bill Derivation location updated.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The orchestrator already threads budget/target_sap/dependencies into
optimise_package, so no orchestrator change was needed. Add an integration test
proving the new objective end-to-end on the real calculator: a band-D property
(~57.4) with a goal of band D — already met — yields a Plan with NO measures and
zero cost (the old max-gain objective would have recommended wall+floor+vent,
improving within the band it is already in). Clarified that the existing
multi-measure test now exercises the max-gain fallback (goal C unreachable from
D, tops out ~61). Narrowed Optional sap_points/estimated_cost through locals to
keep pyright strict-clean.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
ModellingOrchestrator builds the ventilation dependency per Property
(suppressed when already mechanically ventilated) and passes it to
optimise_package, so a selected wall measure forces MEV into the package before
the re-score. Ventilation joins the role-3 cascade in best-practice order
(walls -> roof -> ventilation -> floor) and persists as a Plan Measure carrying
its real negative marginal and its cost. Added the mechanical_ventilation
contingency rate (0.26, per legacy Costs.CONTINGENCIES). Integration test now
seeds the ventilation Product and asserts the forced measure persists with
<=0 SAP and 2x900 cost; the full-pipeline test seeds the Product too (the
dependency is built for every not-yet-ventilated dwelling). On 000490 the real
calculator scores MEV at -1.275 SAP.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Slice 3b — closes#1160. ModellingOrchestrator._plan_for now runs the
full ADR-0016 flow instead of a single cavity measure:
generate wall + roof + floor Recommendations → score each Option
independently (role 1) into grouped ScoredOptions → optimise_package
(grouped knapsack within budget + whole-package re-score + greedy
repair toward the Scenario's SAP target) → attribute the selected set
via the best-practice marginal cascade (role 3) → persist the Plan
with its Plan Measures.
The repair target comes from the goal: INCREASING_EPC → the goal_value
band floor via Epc.sap_lower_bound(); other goals carry no SAP target
yet (later slice). Best-practice order walls → roof → floor.
Integration test: an uninsulated cavity wall + suspended floor (000490)
driven directly through the Modelling stage off a repo-seeded EPC
(the calculator fixture has no lodged recorded-performance fields, so
Baseline can't run it) persists a Plan with two attributed, priced Plan
Measures. The existing first-run test keeps full-pipeline coverage and
now exercises real modelling (its sample EPC's uninsulated solid floor
yields a floor measure). Replaces the single-measure cavity integration
test (subsumed). 138 pass; pyright strict clean.
Multi-phase remains descoped (ADR-0005); single-phase optimiser.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Slice 4b — closes the #1157 tracer. ModellingOrchestrator.run(property_ids,
scenario_ids, portfolio_id) now does real work in one Unit of Work,
committed once (ADR-0011/0012/0016/0017):
read Property (effective EPC) + Scenario via repos → recommend_cavity_wall
→ select its Option → PackageScorer.score (role-2 package total) +
marginal_impacts (role-3 attribution) → build Plan/PlanMeasure →
uow.plan.save → commit.
- AraFirstRunPipeline / ModellingStage thread portfolio_id from the trigger
body (one source of truth); handler builds the real orchestrator
(unit_of_work + Sap10Calculator), dropping the Scenario/Materials stubs.
- ScenarioRepository.get_many promoted to @abstractmethod now the bare-stub
instantiations are gone.
- New ara_first_run-style integration test: a property with an uninsulated
cavity wall yields a persisted Plan + one cavity_wall_insulation Plan
Measure (priced from the Product, figures present, linked by plan_id).
Numeric SAP correctness is pinned separately in test_elmhurst_cascade_pins.
- Existing pipeline integration test updated: seeds scenario 7 and runs the
real Modelling stage (its already-insulated sample wall yields an empty
package — no crash).
121 pass across repositories/modelling/orchestration/app; pyright strict clean.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The PropertyBaselineOrchestrator now reads the current Fuel Rates snapshot
once per batch, builds a BillDerivation, and prices each scored property's
SapResult -> EnergyBreakdown into a Bill carried on PropertyBaselinePerformance
(None only on the stub no-calculator path). The Bill is flattened onto nullable
bill_* flat columns (per-section kwh+cost, standing charges, SEG credit, total)
on the postgres table, with bill_total_annual_bill_gbp as the not-null
discriminator on read-back. Section absent from the bill stays None, not 0.
Updated all four orchestrator construction sites to inject the FuelRatesRepository
port (handler + three test sites), and the FE migration doc to reflect the
prefixed columns and that they are now populated.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Slice 5a: the promotion. Replaces StubRebaseliner in production and collapses the
shadow runner into the rebaseliner (ADR-0013 amendment).
- CalculatorRebaseliner runs Sap10Calculator on every Property:
* sap_version < 10.2 -> Effective Performance IS the calculator output
(band via Epc.from_sap_score, CO2 kg->t, PEUI rounded), reason "pre_sap10".
* sap_version >= 10.2 -> Effective = lodged (API figures on-target), and the
calculator only logs divergence (SAP>0.5, PEUI/CO2 1%) as a validation signal.
* a calculator raise propagates -> batch aborts (ADR-0012); fix the cert at once.
- Rebaseliner.rebaseline gains property_id (for the divergence log).
- LoggingCalculatorShadow / the calculator_shadow seam removed from the
orchestrator; its divergence-comparison logic now lives in the rebaseliner.
- StubRebaseliner kept (signature updated) for orchestrator/repo unit tests.
The SapResult->EnergyBreakdown adapter + BillDerivation wiring (to populate the
bill block) follow once the appliances/cooking SapResult fields land.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Wire Sap10Calculator into PropertyBaselineOrchestrator as a non-load-bearing
shadow runner. For each property it scores the Effective EPC beside the
load-bearing Lodged/Effective write, catches any strict-raise -> log.error
(never aborts the batch), and on success log.warning's divergence from Lodged:
SAP |continuous - lodged| > 0.5; PEUI/CO2 > 1% relative (CO2 after kg->tonnes).
Every line is tagged with sap_version so SAP-10.2 signal separates from
older-spec drift (ADR-0010 Validation Cohort).
Per ADR-0013, Calculated SAP10 Performance is not a persisted third value-set:
effective = calculated in every baselining scenario, so the calculator IS the
mechanism that produces Effective Performance (the Rebaseliner). It runs in
shadow only while being hardened; when overrides/estimation land it is promoted
to drive Effective and the failure posture flips to abort (ADR-0012, calculator
now load-bearing). No table change.
- ADR-0013 + CONTEXT (Calculated SAP10 Performance / Effective Performance /
Rebaselining) record the decision.
- CalculatorShadow port + LoggingCalculatorShadow + Calculator protocol.
- FakeCalculatorShadow for orchestrator unit tests.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Aligns the composition with its entry point (the `ara_first_run` lambda +
`AraFirstRunTriggerBody`): clearer what the file does.
- orchestration/first_run_pipeline.py → ara_first_run_pipeline.py
- FirstRunPipeline → AraFirstRunPipeline; FirstRunCommand → AraFirstRunCommand
- test files renamed to match
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Final slice of ADR-0012: collapse the per-property read round-trips a batch
made (Baseline hydrated ~8 queries x 30 properties one at a time) into a
handful of per-table IN queries.
- EpcPostgresRepository: extracted a shared `_compose(rows)` from `get` (the
windows + floor-dim fetches are now passed in, not fetched inline), so both
`get` and the new `get_for_properties(property_ids)` build EpcPropertyData
from pre-fetched rows. `get_for_properties` fetches each child table once
(`WHERE epc_property_id IN ...`), groups in memory, and composes — load-whole
per ADR-0002.
- PropertyRepository.get_many(property_ids) -> Properties: one query for the
property rows + one bulk EPC hydration, composed in input order.
- BaselineOrchestrator / IngestionOrchestrator read the batch via get_many
instead of N x get.
- Ports + fakes gain the bulk methods.
The #1129 round-trip fidelity test stays green (the compose extraction is
behaviour-preserving). New tests: bulk hydration correctness + round-trips are
constant w.r.t. batch size (one-per-table, proven by query count). 123 pass;
pyright strict clean; AAA.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Replaces the handler's whole-pipeline Session (one transaction across all
three stages, connection pinned during Ingestion's external IO) with a
Unit-of-Work per stage (ADR-0012, added here). Each stage runs its batch in
one unit and commits once; any property raising aborts the batch and the
subtask fails noisily.
- BaselineOrchestrator(unit_of_work, rebaseliner): one unit for the batch,
commit once. Raise on a pre-SAP10 property leaves the unit uncommitted.
- IngestionOrchestrator(unit_of_work, epc_fetcher, geospatial_repo,
solar_fetcher): fetch/write split — phase 1 fetches the whole batch (EPC /
coords / solar) with NO unit open; phase 2 writes in one unit and commits.
The connection is never held during external IO. Geospatial S3 repo stays
injected (reference data, not transactional).
- Handler: module-scoped engine (pool reused across warm invocations) + a UoW
factory; whole-pipeline `with Session` gone. `build_first_run_pipeline`
composes on the factory. Source clients still behind the raising seam.
- ADR-0012 records the decision (per-stage boundary, all-or-nothing batch,
idempotent re-run, fetch/write split, module-scoped engine). Modelling stub
left untouched (no-op, no DB) per the ADR.
Tests: orchestrators on a shared FakeUnitOfWork (assert persisted batch +
exactly-once commit + no-commit-on-raise). New real-DB E2E integration test:
real PostgresUnitOfWork, Ingestion writes the EPC → Baseline reads it back
through the repo → re-run replaces, not duplicates (1 EPC row, 1 baseline row
after two runs). 121 pass in tests/; pyright strict clean; AAA.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Completes the First Run spine. Replaces the #1130 stub FirstRunPipeline
with the real three-stage composition and wires it into the handler.
- `FirstRunPipeline.run(command)` sequences Ingestion → Baseline →
Modelling, threading **only** `property_ids` between stages (and
`scenario_ids` into Modelling, off the command — never a prior stage's
output). Stages are injected behind thin `IngestionStage` /
`BaselineStage` / `ModellingStage` Protocols (the EpcFetcher/SolarFetcher
idiom), so the handler owns wiring and tests substitute fakes (ADR-0011).
- `ModellingOrchestrator` stub + `ScenarioRepository` / `MaterialsRepository`
seam ports — `run(property_ids, scenario_ids)` reads through repos, does
no scoring yet. Method shapes deferred to the Modelling per-service grills
(Scenario / Scenario Phase / Snapshot / Optimised Package / Plans are rich
— not pre-empted here).
- Handler delegates to the real pipeline via `build_first_run_pipeline`
(Postgres-backed repos off the session). The Ingestion source clients
(EPC API / Google Solar / geospatial S3) are isolated behind one
`_source_clients_from_env` seam that raises until the deploy/Terraform
config settles — out of scope for this slice. Subtask complete/failed +
CloudWatch URL still come from `@subtask_handler`.
Integration test (the criterion's centrepiece): wires REAL Ingestion +
REAL Baseline + stub Modelling through a shared fake EPC repo, with a
repo-backed PropertyRepo composing the Property from that slice. Proves
Baseline reads the very EPC Ingestion persisted — the through-repos
hand-off, no in-memory coupling. Plus a composition test pinning stage
order + only-property_ids threading.
TDD, one test → one impl. pyright strict clean; AAA layout. 116 pass in
the tests/ tree, no regressions.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Stage 2 of First Run. Establishes each Property's Baseline Performance
from persisted source data and writes it back — reads only from repos,
never a Fetcher or HTTP (ADR-0003), so it is byte-identical whether
Ingestion ran milliseconds ago or last week.
Domain (`domain/baseline/`):
- `Performance` VO — the four rated quantities: SAP / EPC Band / CO2 /
Primary Energy Intensity. `lodged_performance(epc)` reads them off the
EPC's recorded fields (PEUI = `energy_consumption_current`).
- `BaselinePerformance` (ADR-0004) — the paired `lodged` + `effective`
Performance + `rebaseline_reason`, plus the no-derivation part of the
energy block (`space_heating_kwh` / `water_heating_kwh`, off the RHI,
deterministic per ADR-0006). Both halves always populated.
- `Rebaseliner` port + `StubRebaseliner`: the re-score-on-override seam
(ADR-0011). SAP10 certs pass through (effective == lodged, reason
"none"); a pre-SAP10 cert raises `RebaselineNotImplemented` rather
than fabricating a plausible-but-wrong "none" — ML rebaselining is not
wired yet. Mirrors the repo's strict-raise culture.
Persistence: new `BaselineRepository` port + `BaselinePostgresRepository`
+ flat-column `baseline_performance` SQLModel (one row per Property). Per
ADR-0004's amendment this is a standalone table, NOT columns on the
retiring `property_details_epc`. Production migration is FE-owned
(Drizzle) — docs/migrations/baseline-performance-table.md.
Docs (grill-with-docs): corrected CONTEXT.md Lodged/Effective Performance
to Primary Energy Intensity (the term collided with its own _Avoid_ entry
under "heat demand") + fixed stale RHI field names; amended ADR-0004
Consequences for the standalone-table decision.
Fuel split + bills (rest of EPC Energy Derivation) deferred to a
follow-up — they need a Fuel Rates source (Ofgem-cap ETL) that does not
exist yet.
TDD, one test -> one impl: 7 tests (lodged read, rebaseliner pass-through
+ raise, orchestrator establish-and-persist + pre-SAP10 raise, Postgres
round-trip + absent). pyright strict clean; AAA layout.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Stage-2 entry point for the First Run use case. Adds the
`ara_first_run` Lambda package mirroring the `postcode_splitter`
template, its typed trigger contract, and a stub `FirstRunPipeline`.
- `AraFirstRunTriggerBody`: thin command of five fields — `task_id`,
`sub_task_id` (UUID, lifecycle), `portfolio_id`, `property_ids`,
`scenario_ids` (int business IDs). No `model_config` override, so
Pydantic's default `extra="ignore"` lets the FastAPI backend add
fields without breaking deployed lambdas. UPRNs / Scenario defs are
deliberately off the event — read from source-of-truth tables.
- Thin `handler.py`: validate-and-delegate only, via a named
`dispatch_first_run` seam (testable without the Lambda runtime).
Subtask status (in-progress/complete/failed) + CloudWatch log URL
come for free from the existing `@subtask_handler()` decorator.
- `FirstRunPipeline` (orchestration/) stub: `run(command)` receives the
validated command. Declares a structural `FirstRunCommand` Protocol
(the three business fields) that `AraFirstRunTriggerBody` satisfies,
so orchestration needs no application-layer import — rhymes with the
`EpcFetcher`/`SolarFetcher` Protocols on IngestionOrchestrator
(ADR-0011). Full Ingestion→Baseline→Modelling composition lands in
#1136.
- Dockerfile / requirements.txt / local_handler/ mirror postcode_splitter.
TDD: 7 new tests (trigger-body validation incl. forward-compat +
id-types, pipeline seam, handler delegation). pyright strict clean.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Stage 1 of the pipeline: per property, read its UPRN from the property row,
fetch its EPC, resolve coordinates from the Geospatial reference repo, thread
those into the Solar fetcher, and persist EPC + solar via repos. Fetchers never
call each other — the orchestrator threads the coordinate (ADR-0011). Coordinates
are reference data (deterministic from UPRN), resolved transiently to drive the
solar fetch rather than persisted per-property.
Depends on thin EpcFetcher/SolarFetcher Protocols (EpcClientService and
GoogleSolarApiClient satisfy them structurally). Unit-tested against fakes — no
DB, gov API, or network: persists EPC, threads coords into solar, skips
UPRN-less properties and skips solar when coordinates are absent. pyright clean.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Aligns the composition with its entry point (the `ara_first_run` lambda +
`AraFirstRunTriggerBody`): clearer what the file does.
- orchestration/first_run_pipeline.py → ara_first_run_pipeline.py
- FirstRunPipeline → AraFirstRunPipeline; FirstRunCommand → AraFirstRunCommand
- test files renamed to match
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Final slice of ADR-0012: collapse the per-property read round-trips a batch
made (Baseline hydrated ~8 queries x 30 properties one at a time) into a
handful of per-table IN queries.
- EpcPostgresRepository: extracted a shared `_compose(rows)` from `get` (the
windows + floor-dim fetches are now passed in, not fetched inline), so both
`get` and the new `get_for_properties(property_ids)` build EpcPropertyData
from pre-fetched rows. `get_for_properties` fetches each child table once
(`WHERE epc_property_id IN ...`), groups in memory, and composes — load-whole
per ADR-0002.
- PropertyRepository.get_many(property_ids) -> Properties: one query for the
property rows + one bulk EPC hydration, composed in input order.
- BaselineOrchestrator / IngestionOrchestrator read the batch via get_many
instead of N x get.
- Ports + fakes gain the bulk methods.
The #1129 round-trip fidelity test stays green (the compose extraction is
behaviour-preserving). New tests: bulk hydration correctness + round-trips are
constant w.r.t. batch size (one-per-table, proven by query count). 123 pass;
pyright strict clean; AAA.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Replaces the handler's whole-pipeline Session (one transaction across all
three stages, connection pinned during Ingestion's external IO) with a
Unit-of-Work per stage (ADR-0012, added here). Each stage runs its batch in
one unit and commits once; any property raising aborts the batch and the
subtask fails noisily.
- BaselineOrchestrator(unit_of_work, rebaseliner): one unit for the batch,
commit once. Raise on a pre-SAP10 property leaves the unit uncommitted.
- IngestionOrchestrator(unit_of_work, epc_fetcher, geospatial_repo,
solar_fetcher): fetch/write split — phase 1 fetches the whole batch (EPC /
coords / solar) with NO unit open; phase 2 writes in one unit and commits.
The connection is never held during external IO. Geospatial S3 repo stays
injected (reference data, not transactional).
- Handler: module-scoped engine (pool reused across warm invocations) + a UoW
factory; whole-pipeline `with Session` gone. `build_first_run_pipeline`
composes on the factory. Source clients still behind the raising seam.
- ADR-0012 records the decision (per-stage boundary, all-or-nothing batch,
idempotent re-run, fetch/write split, module-scoped engine). Modelling stub
left untouched (no-op, no DB) per the ADR.
Tests: orchestrators on a shared FakeUnitOfWork (assert persisted batch +
exactly-once commit + no-commit-on-raise). New real-DB E2E integration test:
real PostgresUnitOfWork, Ingestion writes the EPC → Baseline reads it back
through the repo → re-run replaces, not duplicates (1 EPC row, 1 baseline row
after two runs). 121 pass in tests/; pyright strict clean; AAA.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Completes the First Run spine. Replaces the #1130 stub FirstRunPipeline
with the real three-stage composition and wires it into the handler.
- `FirstRunPipeline.run(command)` sequences Ingestion → Baseline →
Modelling, threading **only** `property_ids` between stages (and
`scenario_ids` into Modelling, off the command — never a prior stage's
output). Stages are injected behind thin `IngestionStage` /
`BaselineStage` / `ModellingStage` Protocols (the EpcFetcher/SolarFetcher
idiom), so the handler owns wiring and tests substitute fakes (ADR-0011).
- `ModellingOrchestrator` stub + `ScenarioRepository` / `MaterialsRepository`
seam ports — `run(property_ids, scenario_ids)` reads through repos, does
no scoring yet. Method shapes deferred to the Modelling per-service grills
(Scenario / Scenario Phase / Snapshot / Optimised Package / Plans are rich
— not pre-empted here).
- Handler delegates to the real pipeline via `build_first_run_pipeline`
(Postgres-backed repos off the session). The Ingestion source clients
(EPC API / Google Solar / geospatial S3) are isolated behind one
`_source_clients_from_env` seam that raises until the deploy/Terraform
config settles — out of scope for this slice. Subtask complete/failed +
CloudWatch URL still come from `@subtask_handler`.
Integration test (the criterion's centrepiece): wires REAL Ingestion +
REAL Baseline + stub Modelling through a shared fake EPC repo, with a
repo-backed PropertyRepo composing the Property from that slice. Proves
Baseline reads the very EPC Ingestion persisted — the through-repos
hand-off, no in-memory coupling. Plus a composition test pinning stage
order + only-property_ids threading.
TDD, one test → one impl. pyright strict clean; AAA layout. 116 pass in
the tests/ tree, no regressions.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Stage 2 of First Run. Establishes each Property's Baseline Performance
from persisted source data and writes it back — reads only from repos,
never a Fetcher or HTTP (ADR-0003), so it is byte-identical whether
Ingestion ran milliseconds ago or last week.
Domain (`domain/baseline/`):
- `Performance` VO — the four rated quantities: SAP / EPC Band / CO2 /
Primary Energy Intensity. `lodged_performance(epc)` reads them off the
EPC's recorded fields (PEUI = `energy_consumption_current`).
- `BaselinePerformance` (ADR-0004) — the paired `lodged` + `effective`
Performance + `rebaseline_reason`, plus the no-derivation part of the
energy block (`space_heating_kwh` / `water_heating_kwh`, off the RHI,
deterministic per ADR-0006). Both halves always populated.
- `Rebaseliner` port + `StubRebaseliner`: the re-score-on-override seam
(ADR-0011). SAP10 certs pass through (effective == lodged, reason
"none"); a pre-SAP10 cert raises `RebaselineNotImplemented` rather
than fabricating a plausible-but-wrong "none" — ML rebaselining is not
wired yet. Mirrors the repo's strict-raise culture.
Persistence: new `BaselineRepository` port + `BaselinePostgresRepository`
+ flat-column `baseline_performance` SQLModel (one row per Property). Per
ADR-0004's amendment this is a standalone table, NOT columns on the
retiring `property_details_epc`. Production migration is FE-owned
(Drizzle) — docs/migrations/baseline-performance-table.md.
Docs (grill-with-docs): corrected CONTEXT.md Lodged/Effective Performance
to Primary Energy Intensity (the term collided with its own _Avoid_ entry
under "heat demand") + fixed stale RHI field names; amended ADR-0004
Consequences for the standalone-table decision.
Fuel split + bills (rest of EPC Energy Derivation) deferred to a
follow-up — they need a Fuel Rates source (Ofgem-cap ETL) that does not
exist yet.
TDD, one test -> one impl: 7 tests (lodged read, rebaseliner pass-through
+ raise, orchestrator establish-and-persist + pre-SAP10 raise, Postgres
round-trip + absent). pyright strict clean; AAA layout.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Stage-2 entry point for the First Run use case. Adds the
`ara_first_run` Lambda package mirroring the `postcode_splitter`
template, its typed trigger contract, and a stub `FirstRunPipeline`.
- `AraFirstRunTriggerBody`: thin command of five fields — `task_id`,
`sub_task_id` (UUID, lifecycle), `portfolio_id`, `property_ids`,
`scenario_ids` (int business IDs). No `model_config` override, so
Pydantic's default `extra="ignore"` lets the FastAPI backend add
fields without breaking deployed lambdas. UPRNs / Scenario defs are
deliberately off the event — read from source-of-truth tables.
- Thin `handler.py`: validate-and-delegate only, via a named
`dispatch_first_run` seam (testable without the Lambda runtime).
Subtask status (in-progress/complete/failed) + CloudWatch log URL
come for free from the existing `@subtask_handler()` decorator.
- `FirstRunPipeline` (orchestration/) stub: `run(command)` receives the
validated command. Declares a structural `FirstRunCommand` Protocol
(the three business fields) that `AraFirstRunTriggerBody` satisfies,
so orchestration needs no application-layer import — rhymes with the
`EpcFetcher`/`SolarFetcher` Protocols on IngestionOrchestrator
(ADR-0011). Full Ingestion→Baseline→Modelling composition lands in
#1136.
- Dockerfile / requirements.txt / local_handler/ mirror postcode_splitter.
TDD: 7 new tests (trigger-body validation incl. forward-compat +
id-types, pipeline seam, handler delegation). pyright strict clean.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Stage 1 of the pipeline: per property, read its UPRN from the property row,
fetch its EPC, resolve coordinates from the Geospatial reference repo, thread
those into the Solar fetcher, and persist EPC + solar via repos. Fetchers never
call each other — the orchestrator threads the coordinate (ADR-0011). Coordinates
are reference data (deterministic from UPRN), resolved transiently to drive the
solar fetch rather than persisted per-property.
Depends on thin EpcFetcher/SolarFetcher Protocols (EpcClientService and
GoogleSolarApiClient satisfy them structurally). Unit-tested against fakes — no
DB, gov API, or network: persists EPC, threads coords into solar, skips
UPRN-less properties and skips solar when coordinates are absent. pyright clean.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
