# ─────────────────────────────────────────────────────────────────────────────
# Lexora
#
#   make setup     install both toolchains
#   make corpus    download the official law PDFs (digest-pinned)
#   make index     parse -> chunk -> embed -> Qdrant + BM25
#   make dev       run the API and the web app
#   make check     every quality gate, exactly as CI runs them
#   make eval      evaluate retrieval with and without reranking
#
# Ports are deliberately non-default (API 7862, web 3020): 7860 and 3000 are
# commonly already taken, and silently binding a neighbouring project's port is a
# confusing way to fail.
# ─────────────────────────────────────────────────────────────────────────────

SHELL := /bin/bash
PY    := apps/api/.venv/bin/python
PIP   := uv pip install --python apps/api/.venv/bin/python
# Must match scripts/dev.sh, which owns the port. They disagreed: `make dev`
# started the API on 7862 while `make api` started it on 7861, so the web app
# pointed at whichever had been run last.
API_PORT ?= 7862
WEB_PORT ?= 3020
export PYTHONPATH := apps/api

.DEFAULT_GOAL := help
.PHONY: help setup setup-api setup-web corpus index reindex warm dev api web stop status \
        demo verify-hosted eval-workspace check ci ci-cold lint typecheck test fmt web-check \
        eval eval-judge \
        calibrate chunking questions docker-build docker-run clean

help:
	@grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) \
	  | awk 'BEGIN {FS = ":.*?## "}; {printf "  \033[1m%-14s\033[0m %s\n", $$1, $$2}'

# ── setup ────────────────────────────────────────────────────────────────────
setup: setup-api setup-web ## install both toolchains

setup-api: ## create the Python 3.12 venv and install the API
	uv venv --python 3.12 apps/api/.venv
	$(PIP) -e "apps/api[dev]"

setup-web: ## install the web dependencies
	cd apps/web && npm ci || (cd apps/web && npm install)

# ── corpus and index (Pipeline A) ────────────────────────────────────────────
corpus: ## download the official law PDFs and verify their digests
	$(PY) corpus/download.py

index: ## parse, chunk, embed and build both indexes
	$(PY) -m app.rag.index

reindex: corpus index ## re-download and rebuild from scratch

warm: ## pre-download the ONNX models (do this before an offline demo)
	$(PY) -c "from app.core.embedding import embed_query; from app.rag.rerank import get_cross_encoder; embed_query('warm'); get_cross_encoder(); print('models cached')"

questions: ## rebuild eval/questions.jsonl and verify every label
	$(PY) eval/build_questions.py

# ── run ──────────────────────────────────────────────────────────────────────
dev: ## run the API and the web app together
	./scripts/dev.sh start

api: ## run the API only
	$(PY) -m uvicorn app.main:app --host 127.0.0.1 --port $(API_PORT) --reload

web: ## run the web app only
	cd apps/web && npm run dev

stop: ## stop both (leaves other projects alone)
	./scripts/dev.sh stop

status: ## show what is running
	./scripts/dev.sh status

demo: ## pre-flight every demo state against the running API (run this before presenting)
	$(PY) scripts/demo_check.py

eval-workspace: ## measure retrieval over an UPLOADED document (the workspace, not the corpus)
	$(PY) eval/workspace_run.py

verify-hosted: ## pre-flight the DEPLOYED stack, and warm it (run this before presenting remotely)
	$(PY) scripts/verify_hosted.py

# ── quality gates ────────────────────────────────────────────────────────────
check: lint typecheck test web-check ## every gate, fast (uses the existing venv)
	@echo ""
	@echo "  all gates passed"

# Run this before pushing. `make check` uses the venv you already have, which is why
# three separate CI failures were invisible locally: a dependency constraint that never
# re-resolved, an import order that depended on the working directory, and a model cache
# that was warm here and cold there. This target resolves dependencies from scratch in a
# throwaway venv, exactly as a fresh runner does.
ci: ## reproduce CI locally in a clean-room venv (slow, but catches what `check` cannot)
	@echo "── clean-room dependency resolution ─────────────────────────────"
	@rm -rf /tmp/lexora-ci-venv
	@uv venv --python 3.12 /tmp/lexora-ci-venv >/dev/null
	@uv pip install --python /tmp/lexora-ci-venv/bin/python -e "apps/api[dev]" >/dev/null
	@echo "  dependencies resolve from scratch"
	@echo "── gates ────────────────────────────────────────────────────────"
	@/tmp/lexora-ci-venv/bin/ruff check .
	@/tmp/lexora-ci-venv/bin/ruff format --check .
	@/tmp/lexora-ci-venv/bin/mypy
	@/tmp/lexora-ci-venv/bin/python -m pytest -q
	@echo "── retrieval quality gate (same thresholds as CI) ───────────────"
	@/tmp/lexora-ci-venv/bin/python eval/build_questions.py >/dev/null
	@/tmp/lexora-ci-venv/bin/python eval/ragas_run.py >/dev/null
	@/tmp/lexora-ci-venv/bin/python scripts/quality_gate.py
	@cd apps/web && npx tsc --noEmit && npx eslint . --max-warnings 0 && npm run build >/dev/null
	@echo ""
	@echo "  clean-room CI passed — safe to push"

# The one divergence `ci` cannot simulate cheaply: a cold model cache. Running this
# deletes ~1.6 GB and re-downloads, so it is separate and deliberate.
ci-cold: ## like `ci`, but with an empty ONNX model cache
	rm -rf var/models
	$(MAKE) ci

lint: ## ruff, zero warnings
	apps/api/.venv/bin/ruff check .
	apps/api/.venv/bin/ruff format --check .

fmt: ## apply ruff formatting
	apps/api/.venv/bin/ruff format .
	apps/api/.venv/bin/ruff check --fix .

typecheck: ## mypy --strict
	apps/api/.venv/bin/mypy

test: ## pytest, warnings are errors
	$(PY) -m pytest

web-check: ## tsc, eslint and a production build
	cd apps/web && npx tsc --noEmit && npx eslint . --max-warnings 0 && npm run build

# ── evaluation ───────────────────────────────────────────────────────────────
eval: ## retrieval metrics, with and without reranking
	$(PY) eval/ragas_run.py

eval-judge: ## add Claude-judged faithfulness and answer relevance
	$(PY) eval/ragas_run.py --judge

calibrate: ## recompute the refusal floor from the labelled set
	$(PY) eval/ragas_run.py --calibrate

chunking: ## re-index at 300/600/1000 tokens and compare hit-rate
	$(PY) eval/chunking_experiment.py

# ── container ────────────────────────────────────────────────────────────────
docker-build: ## build the API image (models baked in)
	docker build -t lexora-api:latest .

docker-run: ## run the image on the Hugging Face Spaces port
	docker run --rm -p 7860:7860 --env-file .env lexora-api:latest

clean: ## remove build artefacts, keep the corpus and the portable index
	rm -rf var/qdrant .pytest_cache .mypy_cache .ruff_cache apps/web/.next
	find . -name __pycache__ -type d -prune -exec rm -rf {} +