vpp-ai-platform/skills-py/vpp_skills/app.py

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M2: skill contracts, Python skill service, L2 eval harness with baseline - packages/domain: ForecastRequest, BidOptimizationRequest/Result, ReportRequest, SkillReport (+ golden and invalid fixtures, exported to contracts/ and regenerated as pydantic models). - skills-py/vpp_skills: FastAPI service with versioned registry; load/PV/ price forecasts (same-day-type EWM point forecast, conformal residual quantiles — coverage test as acceptance gate); bid-optimization MILP on HiGHS (binary block participation, hard ledger energy bounds, exact Decimal fit of the rounded curve inside the bounds, revenue distribution over quantile paths); report generator whose every figure is a {tool_call_id, path} reference, with a verifier. 48 tests incl. hypothesis property test that bids respect ledger constraints. - packages/services: LedgerService.dayAheadBounds (the P7 cascade band handed to the optimizer); Decimal resolved once for CJS/ESM interop. - packages/evals: L2 metrics (MAPE, nRMSE, coverage, direction accuracy, naive/hindsight revenue baselines), HTTP skill client, rolling-origin harness that pushes each bid through the real ledger, CLI with --check/--write-baseline; committed baseline on the SYNTHETIC dataset (no historical Hubei data yet — baselines measure the harness, not KPI). - CI: evals job boots the skill service and fails on baseline digest drift. - docs/open-questions: A6 (flexibility marginal cost = offer floor); A4/B6 wired as placeholders. README/CLAUDE.md status → M2 done, M3 next. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01UoYoGYzHkFyv3ALenkRPhA
2026-09-02 06:29:08 -04:00
"""HTTP/JSON skill service (docs/11 §3.4, docs/09 §4).
One process, one route per skill, generated pydantic contracts on both request
and response. The TS runtime's registerSkill wrapper is the only intended
caller; it snapshots request/response for lineage. Run locally with
``uvicorn vpp_skills.app:app``.
"""
from __future__ import annotations
from fastapi import FastAPI, HTTPException
from fastapi.responses import JSONResponse
from vpp_contracts.bid_optimization_request import BidOptimizationRequest
from vpp_contracts.bid_optimization_result import BidOptimizationResult
M4: resource agent, envelopes live, review loop, insight cards - packages/domain: AwardNotice, DISPATCH_PLAN proposal payload, ExecutionReport, MeteringRecord, potential-assessment and dispatch-optimization contracts, ReviewFinding (+ typed writebacks), SemanticMemoryEntry, EnvelopeChangeRequest, InsightCard — exported with fixtures on both sides. - skills-py: potential-assessment (certified × rolling fulfilment, evidence days) and dispatch-optimization (per-interval LP on HiGHS, shortfall reported) skills + routes + tests. - packages/services: dispatch rules in the policy pack (over-allocation, award anchor, lineage integrity for allocations); PowerBalanceSimulator; SimulationGateway (permit-only, idempotent, seeded execute → ExecutionReports); envelope deviation-streak suspension + apply(); ReviewService (attribution, reliability EWMA writeback, semantic memory, envelope recommendations as change requests); dispatch assembler; skill client methods. - packages/runtime: resource agent; award-decomposition, review and envelope-review workflows; lifecycle selects simulator/gateway by proposal type; trigger hooks for awards, execution reports, metering; decide() resumes either lifecycle or envelope-review runs; insight cards API. - Tests: docs/07 D-1 16:00 and D+1 end to end; reliability score 0.9 → 0.880 and the next assessment de-rates capacity; envelope suspension on a seeded 3-day streak; WIDEN request applied only by a human. 184 TS + 80 Python. - docs/open-questions: B10 (reliability/potential parameters). README and CLAUDE.md status → M4 done, M5 next. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01UoYoGYzHkFyv3ALenkRPhA
2026-09-02 19:31:12 -04:00
from vpp_contracts.dispatch_optimization_request import DispatchOptimizationRequest
from vpp_contracts.dispatch_optimization_result import DispatchOptimizationResult
from vpp_contracts.potential_assessment_request import PotentialAssessmentRequest
from vpp_contracts.potential_assessment_result import PotentialAssessmentResult
M2: skill contracts, Python skill service, L2 eval harness with baseline - packages/domain: ForecastRequest, BidOptimizationRequest/Result, ReportRequest, SkillReport (+ golden and invalid fixtures, exported to contracts/ and regenerated as pydantic models). - skills-py/vpp_skills: FastAPI service with versioned registry; load/PV/ price forecasts (same-day-type EWM point forecast, conformal residual quantiles — coverage test as acceptance gate); bid-optimization MILP on HiGHS (binary block participation, hard ledger energy bounds, exact Decimal fit of the rounded curve inside the bounds, revenue distribution over quantile paths); report generator whose every figure is a {tool_call_id, path} reference, with a verifier. 48 tests incl. hypothesis property test that bids respect ledger constraints. - packages/services: LedgerService.dayAheadBounds (the P7 cascade band handed to the optimizer); Decimal resolved once for CJS/ESM interop. - packages/evals: L2 metrics (MAPE, nRMSE, coverage, direction accuracy, naive/hindsight revenue baselines), HTTP skill client, rolling-origin harness that pushes each bid through the real ledger, CLI with --check/--write-baseline; committed baseline on the SYNTHETIC dataset (no historical Hubei data yet — baselines measure the harness, not KPI). - CI: evals job boots the skill service and fails on baseline digest drift. - docs/open-questions: A6 (flexibility marginal cost = offer floor); A4/B6 wired as placeholders. README/CLAUDE.md status → M2 done, M3 next. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01UoYoGYzHkFyv3ALenkRPhA
2026-09-02 06:29:08 -04:00
from vpp_contracts.forecast_bundle import ForecastBundle
from vpp_contracts.forecast_request import ForecastRequest
from vpp_contracts.report_request import ReportRequest
from vpp_contracts.skill_report import SkillReport
from . import SKILL_VERSIONS
from .bid_milp import optimize_bid
M4: resource agent, envelopes live, review loop, insight cards - packages/domain: AwardNotice, DISPATCH_PLAN proposal payload, ExecutionReport, MeteringRecord, potential-assessment and dispatch-optimization contracts, ReviewFinding (+ typed writebacks), SemanticMemoryEntry, EnvelopeChangeRequest, InsightCard — exported with fixtures on both sides. - skills-py: potential-assessment (certified × rolling fulfilment, evidence days) and dispatch-optimization (per-interval LP on HiGHS, shortfall reported) skills + routes + tests. - packages/services: dispatch rules in the policy pack (over-allocation, award anchor, lineage integrity for allocations); PowerBalanceSimulator; SimulationGateway (permit-only, idempotent, seeded execute → ExecutionReports); envelope deviation-streak suspension + apply(); ReviewService (attribution, reliability EWMA writeback, semantic memory, envelope recommendations as change requests); dispatch assembler; skill client methods. - packages/runtime: resource agent; award-decomposition, review and envelope-review workflows; lifecycle selects simulator/gateway by proposal type; trigger hooks for awards, execution reports, metering; decide() resumes either lifecycle or envelope-review runs; insight cards API. - Tests: docs/07 D-1 16:00 and D+1 end to end; reliability score 0.9 → 0.880 and the next assessment de-rates capacity; envelope suspension on a seeded 3-day streak; WIDEN request applied only by a human. 184 TS + 80 Python. - docs/open-questions: B10 (reliability/potential parameters). README and CLAUDE.md status → M4 done, M5 next. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01UoYoGYzHkFyv3ALenkRPhA
2026-09-02 19:31:12 -04:00
from .dispatch_opt import optimize_dispatch
from .potential import assess_potential
M2: skill contracts, Python skill service, L2 eval harness with baseline - packages/domain: ForecastRequest, BidOptimizationRequest/Result, ReportRequest, SkillReport (+ golden and invalid fixtures, exported to contracts/ and regenerated as pydantic models). - skills-py/vpp_skills: FastAPI service with versioned registry; load/PV/ price forecasts (same-day-type EWM point forecast, conformal residual quantiles — coverage test as acceptance gate); bid-optimization MILP on HiGHS (binary block participation, hard ledger energy bounds, exact Decimal fit of the rounded curve inside the bounds, revenue distribution over quantile paths); report generator whose every figure is a {tool_call_id, path} reference, with a verifier. 48 tests incl. hypothesis property test that bids respect ledger constraints. - packages/services: LedgerService.dayAheadBounds (the P7 cascade band handed to the optimizer); Decimal resolved once for CJS/ESM interop. - packages/evals: L2 metrics (MAPE, nRMSE, coverage, direction accuracy, naive/hindsight revenue baselines), HTTP skill client, rolling-origin harness that pushes each bid through the real ledger, CLI with --check/--write-baseline; committed baseline on the SYNTHETIC dataset (no historical Hubei data yet — baselines measure the harness, not KPI). - CI: evals job boots the skill service and fails on baseline digest drift. - docs/open-questions: A6 (flexibility marginal cost = offer floor); A4/B6 wired as placeholders. README/CLAUDE.md status → M2 done, M3 next. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01UoYoGYzHkFyv3ALenkRPhA
2026-09-02 06:29:08 -04:00
from .forecast import forecast
from .report import generate_report
app = FastAPI(title="vpp-skills", version="0.1.0")
ROUTES = {
"load-forecast": "/v1/forecast/load",
"pv-forecast": "/v1/forecast/pv",
"price-forecast": "/v1/forecast/price",
"bid-optimization-milp": "/v1/optimize/bid",
"report-generator": "/v1/report",
M4: resource agent, envelopes live, review loop, insight cards - packages/domain: AwardNotice, DISPATCH_PLAN proposal payload, ExecutionReport, MeteringRecord, potential-assessment and dispatch-optimization contracts, ReviewFinding (+ typed writebacks), SemanticMemoryEntry, EnvelopeChangeRequest, InsightCard — exported with fixtures on both sides. - skills-py: potential-assessment (certified × rolling fulfilment, evidence days) and dispatch-optimization (per-interval LP on HiGHS, shortfall reported) skills + routes + tests. - packages/services: dispatch rules in the policy pack (over-allocation, award anchor, lineage integrity for allocations); PowerBalanceSimulator; SimulationGateway (permit-only, idempotent, seeded execute → ExecutionReports); envelope deviation-streak suspension + apply(); ReviewService (attribution, reliability EWMA writeback, semantic memory, envelope recommendations as change requests); dispatch assembler; skill client methods. - packages/runtime: resource agent; award-decomposition, review and envelope-review workflows; lifecycle selects simulator/gateway by proposal type; trigger hooks for awards, execution reports, metering; decide() resumes either lifecycle or envelope-review runs; insight cards API. - Tests: docs/07 D-1 16:00 and D+1 end to end; reliability score 0.9 → 0.880 and the next assessment de-rates capacity; envelope suspension on a seeded 3-day streak; WIDEN request applied only by a human. 184 TS + 80 Python. - docs/open-questions: B10 (reliability/potential parameters). README and CLAUDE.md status → M4 done, M5 next. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01UoYoGYzHkFyv3ALenkRPhA
2026-09-02 19:31:12 -04:00
"potential-assessment": "/v1/assess/potential",
"dispatch-optimization": "/v1/optimize/dispatch",
M2: skill contracts, Python skill service, L2 eval harness with baseline - packages/domain: ForecastRequest, BidOptimizationRequest/Result, ReportRequest, SkillReport (+ golden and invalid fixtures, exported to contracts/ and regenerated as pydantic models). - skills-py/vpp_skills: FastAPI service with versioned registry; load/PV/ price forecasts (same-day-type EWM point forecast, conformal residual quantiles — coverage test as acceptance gate); bid-optimization MILP on HiGHS (binary block participation, hard ledger energy bounds, exact Decimal fit of the rounded curve inside the bounds, revenue distribution over quantile paths); report generator whose every figure is a {tool_call_id, path} reference, with a verifier. 48 tests incl. hypothesis property test that bids respect ledger constraints. - packages/services: LedgerService.dayAheadBounds (the P7 cascade band handed to the optimizer); Decimal resolved once for CJS/ESM interop. - packages/evals: L2 metrics (MAPE, nRMSE, coverage, direction accuracy, naive/hindsight revenue baselines), HTTP skill client, rolling-origin harness that pushes each bid through the real ledger, CLI with --check/--write-baseline; committed baseline on the SYNTHETIC dataset (no historical Hubei data yet — baselines measure the harness, not KPI). - CI: evals job boots the skill service and fails on baseline digest drift. - docs/open-questions: A6 (flexibility marginal cost = offer floor); A4/B6 wired as placeholders. README/CLAUDE.md status → M2 done, M3 next. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01UoYoGYzHkFyv3ALenkRPhA
2026-09-02 06:29:08 -04:00
}
@app.exception_handler(ValueError)
async def _value_error(_, exc: ValueError) -> JSONResponse:
return JSONResponse(status_code=422, content={"detail": str(exc)})
@app.get("/health")
def health() -> dict:
return {"status": "ok"}
@app.get("/v1/skills")
def skills() -> list[dict]:
"""Registry view: what the TS tool registry pins versions against."""
return [{"id": sid, "version": ver, "endpoint": ROUTES[sid]} for sid, ver in SKILL_VERSIONS.items()]
def _forecast_for(kind: str, req: ForecastRequest) -> ForecastBundle:
if req.kind.value != kind:
raise HTTPException(status_code=422, detail=f"this endpoint serves kind={kind}, got {req.kind.value}")
return forecast(req)
@app.post(ROUTES["load-forecast"], response_model=ForecastBundle)
def forecast_load(req: ForecastRequest) -> ForecastBundle:
return _forecast_for("LOAD", req)
@app.post(ROUTES["pv-forecast"], response_model=ForecastBundle)
def forecast_pv(req: ForecastRequest) -> ForecastBundle:
return _forecast_for("PV", req)
@app.post(ROUTES["price-forecast"], response_model=ForecastBundle)
def forecast_price(req: ForecastRequest) -> ForecastBundle:
return _forecast_for("PRICE", req)
@app.post(ROUTES["bid-optimization-milp"], response_model=BidOptimizationResult)
def optimize(req: BidOptimizationRequest) -> BidOptimizationResult:
return optimize_bid(req)
@app.post(ROUTES["report-generator"], response_model=SkillReport)
def report(req: ReportRequest) -> SkillReport:
return generate_report(req)
M4: resource agent, envelopes live, review loop, insight cards - packages/domain: AwardNotice, DISPATCH_PLAN proposal payload, ExecutionReport, MeteringRecord, potential-assessment and dispatch-optimization contracts, ReviewFinding (+ typed writebacks), SemanticMemoryEntry, EnvelopeChangeRequest, InsightCard — exported with fixtures on both sides. - skills-py: potential-assessment (certified × rolling fulfilment, evidence days) and dispatch-optimization (per-interval LP on HiGHS, shortfall reported) skills + routes + tests. - packages/services: dispatch rules in the policy pack (over-allocation, award anchor, lineage integrity for allocations); PowerBalanceSimulator; SimulationGateway (permit-only, idempotent, seeded execute → ExecutionReports); envelope deviation-streak suspension + apply(); ReviewService (attribution, reliability EWMA writeback, semantic memory, envelope recommendations as change requests); dispatch assembler; skill client methods. - packages/runtime: resource agent; award-decomposition, review and envelope-review workflows; lifecycle selects simulator/gateway by proposal type; trigger hooks for awards, execution reports, metering; decide() resumes either lifecycle or envelope-review runs; insight cards API. - Tests: docs/07 D-1 16:00 and D+1 end to end; reliability score 0.9 → 0.880 and the next assessment de-rates capacity; envelope suspension on a seeded 3-day streak; WIDEN request applied only by a human. 184 TS + 80 Python. - docs/open-questions: B10 (reliability/potential parameters). README and CLAUDE.md status → M4 done, M5 next. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01UoYoGYzHkFyv3ALenkRPhA
2026-09-02 19:31:12 -04:00
@app.post(ROUTES["potential-assessment"], response_model=PotentialAssessmentResult)
def potential(req: PotentialAssessmentRequest) -> PotentialAssessmentResult:
return assess_potential(req)
@app.post(ROUTES["dispatch-optimization"], response_model=DispatchOptimizationResult)
def dispatch(req: DispatchOptimizationRequest) -> DispatchOptimizationResult:
return optimize_dispatch(req)