NEw metadata

data/processed/lonstatistik/curated_growth_forecast.jsonl

@@ -0,0 +1,2 @@
+{"id":"IDA-curated-forecast-private","union":"IDA","source_doc":"IDA Lønstatistik 2025","source_page":2,"table_id":2,"table_title":"LØNUDVIKLING/PROGNOSE","record_type":"salary_forecast","sector":"private","data_period_month":"2025-09","currency":"DKK","pay_concept":"gross_monthly","education_type":"all","region":"all","position_level":"all","gender":"all","is_leader":"all","manages_people":"all","management_span_band":"all","stem_count_band":"all","specialization":"all","work_area":"all","branche":"all","jobloen":"all","cohort_growth_pct":3.35,"dimension_keys":[],"specificity":0,"reliability_tier":"full","measure":{"mean":3.35},"rag_text":"IDA Lønstatistik 2025 — IDA members, private sector. 2026 salary forecast 3.35% expected pay increase (private sector). Full reliability (n≥50). Source: IDA Lønstatistik 2025, tabel 2, p.2."}
+{"id":"IDA-curated-growth-private","union":"IDA","source_doc":"IDA Lønstatistik 2025","source_page":1,"table_id":1,"table_title":"LØNUDVIKLING","record_type":"salary_growth_rate","sector":"private","data_period_month":"2025-09","currency":"DKK","pay_concept":"gross_monthly","education_type":"all","region":"all","position_level":"all","gender":"all","is_leader":"all","manages_people":"all","management_span_band":"all","stem_count_band":"all","specialization":"all","work_area":"all","branche":"all","jobloen":"all","cohort_growth_pct":3.35,"dimension_keys":[],"specificity":0,"reliability_tier":"full","measure":{"mean":3.35},"rag_text":"IDA Lønstatistik 2025 — IDA members, private sector. 3-year gliding annual salary growth 3.35% (private sector). Full reliability (n≥50). Source: IDA Lønstatistik 2025, tabel 1, p.1."}

data/processed/lonstatistik/ida_salary_record.schema.json

@@ -0,0 +1,181 @@
+{
+  "$schema": "https://json-schema.org/draft/2020-12/schema",
+  "$id": "https://pay-equity-eu/ida-salary-record.schema.json",
+  "title": "IDA Lønstatistik 2025 salary record (extended for RAG filtering)",
+  "description": "One normalized salary-statistics observation extracted from the IDA 2025 Danish union salary booklet, extended with structured, hard-filterable dimensions so a user profile can be matched to the correct cell(s) and fused across tables. SCOPE DECISION (v1, schedule-driven): this schema covers ONLY the IDA salary corpus. The EU Pay Transparency Directive corpus is intentionally NOT structured for v1 and is retrieved via plain embedding/semantic search; revisit once the salary path is shipped. WILDCARD CONTRACT: every filterable dimension below is a required string enum that includes the sentinel \"all\". \"all\" means the table aggregates over that dimension and is the ONLY wildcard. A chunk matches a profile iff, for every dimension, chunk.value == profile.value OR chunk.value == \"all\". Nulls must never appear on a dimension field; backfill \"all\" explicitly at ingest (validation will fail otherwise).",
+  "type": "object",
+  "required": [
+    "id",
+    "union",
+    "source_doc",
+    "source_page",
+    "sector",
+    "currency",
+    "pay_concept",
+    "record_type",
+    "education_type",
+    "region",
+    "position_level",
+    "gender",
+    "is_leader",
+    "manages_people",
+    "management_span_band",
+    "stem_count_band",
+    "specialization",
+    "work_area",
+    "branche",
+    "jobloen",
+    "reliability_tier",
+    "specificity",
+    "dimension_keys",
+    "measure",
+    "rag_text"
+  ],
+  "additionalProperties": false,
+  "properties": {
+    "id": {"type": "string", "description": "Stable record id: <union>-p<page>-<seq>."},
+    "union": {"type": "string", "enum": ["IDA"], "description": "Source union. Locked to IDA for v1 (engineers/scientists). Re-add Djoef when that corpus is onboarded."},
+    "source_doc": {"type": "string", "description": "Human-readable source document title."},
+    "source_page": {"type": "integer", "minimum": 1, "description": "1-based page number in the source PDF."},
+    "table_id": {"type": ["integer", "null"], "description": "TABEL number printed in the booklet (e.g. 4 for 'TABEL 4'). Null for non-table figures/text."},
+    "table_title": {"type": ["string", "null"], "description": "Table caption / title."},
+    "table_group": {"type": ["string", "null"], "description": "Raw table-level subgroup label as printed (provenance only; the typed dimension fields below are what you filter on)."},
+    "record_type": {
+      "type": "string",
+      "enum": [
+        "salary_observation",
+        "salary_growth_rate",
+        "salary_forecast",
+        "minimum_wage_recommendation",
+        "benefit_prevalence",
+        "terms_stat",
+        "self_employed_income"
+      ],
+      "description": "Primary router. Filter on this BEFORE any other field. salary_observation = a pay cell (tabel 3-17, 27-41). salary_growth_rate = tabel 1. salary_forecast = tabel 2. minimum_wage_recommendation = p6 dimittend floors. benefit_prevalence = tabel 18. terms_stat = tabel 19-25 (vacation, clauses, jobchange, etc.). self_employed_income = tabel 42-51. NOTE: directive_provision is intentionally absent (directive uses plain embedding in v1)."
+    },
+    "sector": {
+      "type": "string",
+      "enum": ["private", "public_state", "public_municipal", "public_regional", "self_employed", "all"],
+      "description": "Employment sector. Switches between the private (tabel 3-25) and public (tabel 27-41) corpora; also gates directive direct-effect framing downstream."
+    },
+    "period": {"type": ["string", "null"], "description": "Measurement period as printed, e.g. 'september 2025' or 'maj 2025'."},
+    "data_period_month": {"type": ["string", "null"], "pattern": "^[0-9]{4}-[0-9]{2}$", "description": "ISO YYYY-MM of the snapshot ('2025-09' private, '2025-05' public). Use for deterministic fremskrivning to 2026."},
+    "currency": {"type": "string", "enum": ["DKK"], "description": "Currency of all monetary values."},
+    "pay_concept": {
+      "type": "string",
+      "enum": ["gross_monthly", "gross_monthly_with_components", "net_monthly", "annual_income"],
+      "description": "What the monetary values represent. Bruttoloen=gross_monthly; public tables with base/supplement/pension breakdown=gross_monthly_with_components; self-employed=annual_income."
+    },
+    "includes_executives": {"type": ["boolean", "null"], "description": "True = row is inkl. topchefer, False = ekskl. topchefer. Means are NOT comparable across this flag; the dashboard must read it before differencing a user's salary against the cell. Null only if the source is genuinely ambiguous."},
+    "education_type": {
+      "type": "string",
+      "enum": ["civilingenior", "akademiingenior", "diplom_eksportingenior", "teknikumingenior", "cand_scient", "all"],
+      "description": "Filterable dimension. Derive from table_id: 4->civilingenior, 3->akademiingenior, 5->teknikumingenior, 6->diplom_eksportingenior, 7->cand_scient. 'all' for tabel 8-25 and public tables that mix educations."
+    },
+    "region": {
+      "type": "string",
+      "enum": ["hovedstaden", "sjaelland", "syddanmark", "midtjylland", "nordjylland", "all"],
+      "description": "Filterable dimension. From tabel 11 columns; 'all' elsewhere."
+    },
+    "position_level": {
+      "type": "string",
+      "enum": ["topchef", "funktionsdirektor", "afdelingschef", "projektleder", "chef_seniorkonsulent", "specialist", "ingenior_akademiker", "anden", "trainee", "all"],
+      "description": "Filterable dimension. Same enum whether the position appears as a row (tabel 12/13) or a column/segment (tabel 14/15)."
+    },
+    "position_level_rank": {"type": ["integer", "null"], "minimum": 1, "maximum": 9, "description": "Ordinal helper (trainee=1 ... topchef=9), null when position_level='all'. NOT a wildcard filter; use for 'next level up' framing and range queries."},
+    "gender": {
+      "type": "string",
+      "enum": ["male", "female", "all"],
+      "description": "Filterable dimension. From tabel 17 (and 24/25). Basis of the equal-pay comparison."
+    },
+    "is_leader": {
+      "type": "string",
+      "enum": ["leader", "non_leader", "all"],
+      "description": "Filterable dimension. Leader/non-leader grouping in tabel 16/17/23. (Boolean concept encoded as enum so 'all' remains the single wildcard.)"
+    },
+    "manages_people": {
+      "type": "string",
+      "enum": ["yes", "no", "all"],
+      "description": "Filterable dimension. Tabel 14."
+    },
+    "management_span_band": {
+      "type": "string",
+      "enum": ["none", "1-4", "5-10", "11-19", "20-29", "30-50", "51-100", "101-200", "201-500", "501-1000", "1001+", "all"],
+      "description": "Filterable dimension. Tabel 14. For the onboarding bucket '20+', match against span_min/span_max range rather than exact band."
+    },
+    "span_min": {"type": ["integer", "null"], "description": "Lower bound of management_span_band as an int (e.g. 5 for '5-10'), for range-contains matching. Null when band is 'all' or 'none'."},
+    "span_max": {"type": ["integer", "null"], "description": "Upper bound of management_span_band (e.g. 10 for '5-10'; null for open-ended '1001+')."},
+    "stem_count_band": {
+      "type": "string",
+      "enum": ["1-9", "10-24", "25-99", "100-249", "250+", "all"],
+      "description": "Filterable dimension. IDA company-size axis (number of engineers/STEM in the company, tabel 15). KEEP SEPARATE from the EU total-headcount band, which is not stored here in v1 (directive = embedding search)."
+    },
+    "specialization": {
+      "type": "string",
+      "enum": ["byggeri_anlaeg", "datalogi_it_software", "elektronik", "kemi_biologi_biotek", "maskin_industri_robot", "energi_miljo", "eksport_ledelse_forretning", "levnedsmiddel", "matematik_fysik_geologi", "all"],
+      "description": "Filterable dimension from tabel 8 (uddannelsesretning). Not in v1 onboarding; populate anyway ('all' off-table) so the wildcard logic holds and so it is a free upgrade later."
+    },
+    "work_area": {
+      "type": "string",
+      "enum": ["forskning_innovation_undervisning", "byggeri_anlaeg", "industri_produktion_kemi", "elektroteknik_tele", "it_programmering", "energi_forsyning_miljo", "transport_infrastruktur", "naturvidenskab_medicin", "administration_ledelse_service", "all"],
+      "description": "Filterable dimension from tabel 9 (arbejdsomraade). Not in v1 onboarding; populate anyway."
+    },
+    "branche": {
+      "type": "string",
+      "enum": ["raastofindvinding", "forsyningsvirksomhed", "foedevare_kemi_plast_trae", "medicinalindustri", "maskin_jern_metal", "elektronisk_industri", "raadgivende_ingenior", "entreprenor_bygge_anlaeg", "it_telekommunikation", "forskning_udvikling_finans", "handel_transport", "organisationer", "all"],
+      "description": "Filterable dimension from tabel 10 (branche). Not in v1 onboarding; populate anyway."
+    },
+    "jobloen": {
+      "type": "string",
+      "enum": ["yes", "no", "all"],
+      "description": "Filterable dimension. Tabel 16 (fixed salary, no right to overtime pay/afspadsering)."
+    },
+    "row_dimension": {"type": ["string", "null"], "description": "Raw dimension the table rows break down by (provenance), e.g. 'kandidataargang', 'stilling', 'branche', 'alder', 'region'."},
+    "row_label": {"type": ["string", "null"], "description": "Raw row label as printed, e.g. '1986-1987', 'Projektleder', '25-29 aar', 'Alle'. Display/provenance only."},
+    "segment_dimension": {"type": ["string", "null"], "description": "Raw dimension of the segment_label (provenance only)."},
+    "segment_label": {"type": ["string", "null"], "description": "Raw specific segment label (provenance only); the typed dimension fields are what you filter on."},
+    "graduation_year_start": {"type": ["integer", "null"], "description": "Earliest graduation year of the cohort; null = unbounded below ('Foer 1986' -> start null, end 1985)."},
+    "graduation_year_end": {"type": ["integer", "null"], "description": "Latest graduation year of the cohort; null = unbounded above (open-ended recent). Match rule: (start is null OR profile_year >= start) AND (end is null OR profile_year <= end)."},
+    "experience_years_min": {"type": ["integer", "null"], "description": "Derived min experience = 2025 - graduation_year_end (proxy)."},
+    "experience_years_max": {"type": ["integer", "null"], "description": "Derived max experience = 2025 - graduation_year_start; null when open-ended."},
+    "age_min": {"type": ["integer", "null"], "description": "Minimum age (age-breakdown rows)."},
+    "age_max": {"type": ["integer", "null"], "description": "Maximum age; null when open-ended."},
+    "cohort_growth_pct": {"type": ["number", "null"], "description": "3-yr gliding annual growth for this cohort+sector (tabel 1), if denormalized onto the observation. Recommended alternative: keep tabel 1 as separate salary_growth_rate records keyed by (graduation cohort, sector) and join at query time."},
+    "dimension_keys": {
+      "type": "array",
+      "items": {
+        "type": "string",
+        "enum": ["sector", "education_type", "region", "position_level", "gender", "is_leader", "manages_people", "management_span_band", "stem_count_band", "specialization", "work_area", "branche", "jobloen", "graduation_year"]
+      },
+      "uniqueItems": true,
+      "description": "The dimensions that are SPECIFIC (non-'all', or a bounded cohort for graduation_year) in this chunk = the table's breakdown axes. Drives specificity and the 'most relevant comparison' label."
+    },
+    "specificity": {"type": "integer", "minimum": 0, "description": "len(dimension_keys). Sort key: prefer the most specific cell consistent with the profile, falling back to broader cells when the specific one is suppressed."},
+    "sample_size": {"type": ["number", "null"], "description": "Antal / number of respondents (mirror of measure.count). Drives reliability_tier."},
+    "reliability_tier": {
+      "type": "string",
+      "enum": ["suppressed", "mean_only", "median_ok", "quartiles_ok", "full"],
+      "description": "Derived from sample_size per KRL suppression rules: n<5 -> suppressed (no mean); 5-9 -> mean_only; 10-19 -> median_ok (no quartiles); 20-49 -> quartiles_ok (no p90); n>=50 -> full. The dashboard reads this to decide which measure.* fields are safe to render."
+    },
+    "measure": {
+      "type": "object",
+      "description": "Statistics reported for this row/segment. All monetary values are DKK; bonus_pct is a percentage.",
+      "additionalProperties": false,
+      "minProperties": 1,
+      "properties": {
+        "count": {"type": "number", "description": "Antal - number of respondents (sample size)."},
+        "mean": {"type": "number", "description": "Gennemsnit / Bruttoloen - average."},
+        "p25": {"type": "number", "description": "Nedre kvartil / 25% fraktil."},
+        "median": {"type": "number", "description": "Median / 50% fraktil."},
+        "p75": {"type": "number", "description": "OEvre kvartil / 75% fraktil."},
+        "p90": {"type": "number", "description": "90% fraktil."},
+        "base": {"type": "number", "description": "Basisloen / Grundloen (public sector component)."},
+        "supplement": {"type": "number", "description": "Tillaeg (public sector component)."},
+        "pension": {"type": "number", "description": "Pensionsbidrag (public sector component)."},
+        "bonus_avg": {"type": "number", "description": "Average annual bonus (for those who receive bonus)."},
+        "bonus_pct": {"type": "number", "description": "Percent of the group receiving a bonus."}
+      }
+    },
+    "rag_text": {"type": "string", "description": "Natural-language summary for embedding/retrieval. MUST textually contain the human-readable values of every dimension_keys entry plus the reliability caveat, so vector recall backstops a too-narrow metadata filter. Include a source citation (union, table, page)."}
+  }
+}

pyproject.toml

@@ -22,7 +22,11 @@ dependencies = [
 # Dev-only. Never installed on the Space (its runtime only *loads* the committed
 # index). `modal` powers scripts/build_index_modal.py — borrow a GPU to embed the
 # corpus, then assemble the FAISS index locally. Install with `uv sync --group dev`.
-dev = ["modal>=0.70"]
+dev = [
+    "modal>=0.70",
+    "pdfplumber",   # deterministic PDF table re-extraction (tabel 11/17/27)
+    "jsonschema",   # validate records against ida_salary_record.schema.json
+]
 
 [tool.uv]
 extra-index-url = ["https://download.pytorch.org/whl/cu118"]

scripts/build_ida_records.py

@@ -0,0 +1,140 @@
+"""Assemble the structured IDA corpus → ``ida_records.jsonl`` (CPU-only).
+
+Sources, in order:
+  1. Transform of the verified flat ``salary_records.jsonl`` (recoverable tables).
+  2. Re-extracted multi-axis tables (region/gender/leadership/public-position),
+     if ``ida_reextracted.jsonl`` exists (produced by scripts/extract_engine.py).
+  3. Curated ``salary_growth_rate`` / ``salary_forecast`` constants (tabel 1/2).
+
+Every record gets a contract-compliant ``rag_text`` and is validated against
+``ida_salary_record.schema.json``; the build FAILS on any violation. Output is
+plain JSON (no embedding here) — embedding is a separate Modal step.
+
+Usage::  uv run python scripts/build_ida_records.py
+"""
+
+from __future__ import annotations
+
+import json
+import sys
+from pathlib import Path
+
+from jsonschema import Draft202012Validator
+
+REPO = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(REPO))
+
+from src.backend.indexing.ida_transform import (  # noqa: E402
+    make_rag_text,
+    transform_corpus,
+)
+from src.backend.schemas import IdaSalaryRecord, Measure  # noqa: E402
+
+LON = REPO / "data" / "processed" / "lonstatistik"
+SOURCE_JSONL = LON / "salary_records.jsonl"
+REEXTRACT_JSONL = LON / "ida_reextracted.jsonl"   # optional (task: re-extraction)
+CURATED_JSONL = LON / "curated_growth_forecast.jsonl"
+SCHEMA = LON / "ida_salary_record.schema.json"
+OUT = LON / "ida_records.jsonl"
+
+
+def build_curated() -> list[IdaSalaryRecord]:
+    """Curated growth/forecast records (Q4 decision: documented constants).
+
+    Convention for non-observation record_types: the annual percentage lives in
+    ``cohort_growth_pct`` (fusion reads it); the required salary-shaped
+    ``measure`` carries the same number under ``mean`` purely to satisfy the
+    contract (it is a percentage, not DKK, and never rendered as a salary).
+    Real per-cohort tabel-1 growth is a v1.1 upgrade.
+    """
+    forecast_mid = 3.35  # private 2026 band 3.2–3.5%
+    recs = [
+        IdaSalaryRecord(
+            id="IDA-curated-forecast-private",
+            source_doc="IDA Lønstatistik 2025",
+            source_page=2, table_id=2, table_title="LØNUDVIKLING/PROGNOSE",
+            record_type="salary_forecast", sector="private",
+            data_period_month="2025-09", pay_concept="gross_monthly",
+            cohort_growth_pct=forecast_mid, reliability_tier="full",
+            measure=Measure(mean=forecast_mid), rag_text="",
+        ),
+        IdaSalaryRecord(
+            id="IDA-curated-growth-private",
+            source_doc="IDA Lønstatistik 2025",
+            source_page=1, table_id=1, table_title="LØNUDVIKLING",
+            record_type="salary_growth_rate", sector="private",
+            data_period_month="2025-09", pay_concept="gross_monthly",
+            cohort_growth_pct=forecast_mid, reliability_tier="full",
+            measure=Measure(mean=forecast_mid), rag_text="",
+        ),
+    ]
+    return recs
+
+
+def load_reextracted() -> list[IdaSalaryRecord]:
+    if not REEXTRACT_JSONL.exists():
+        return []
+    out = []
+    for line in REEXTRACT_JSONL.read_text(encoding="utf-8").splitlines():
+        if line.strip():
+            out.append(IdaSalaryRecord(**json.loads(line)))
+    return out
+
+
+def main() -> None:
+    old = [json.loads(l) for l in SOURCE_JSONL.read_text(encoding="utf-8").splitlines() if l.strip()]
+    records = transform_corpus(old)
+    n_transform = len(records)
+
+    reextracted = load_reextracted()
+    records += reextracted
+
+    curated = build_curated()
+    records += curated
+
+    # rag_text for every record (contract: names each dimension_keys value + caveat + cite).
+    for r in records:
+        r.rag_text = make_rag_text(r)
+
+    # Persist curated separately for transparency (Q4 artifact).
+    CURATED_JSONL.write_text(
+        "\n".join(r.model_dump_json(exclude_none=True) for r in curated) + "\n",
+        encoding="utf-8",
+    )
+
+    # Validate EVERY record against the authoritative schema; fail on any error.
+    validator = Draft202012Validator(json.loads(SCHEMA.read_text(encoding="utf-8")))
+    errors = []
+    seen_ids = set()
+    for r in records:
+        d = r.model_dump(exclude_none=True)
+        if r.id in seen_ids:
+            errors.append((r.id, "duplicate id"))
+        seen_ids.add(r.id)
+        for e in validator.iter_errors(d):
+            errors.append((r.id, f"{list(e.path)}: {e.message}"))
+            break
+    if errors:
+        print(f"VALIDATION FAILED: {len(errors)} error(s)")
+        for eid, msg in errors[:20]:
+            print("  ", eid, msg)
+        raise SystemExit(1)
+
+    OUT.write_text(
+        "\n".join(r.model_dump_json(exclude_none=True) for r in records) + "\n",
+        encoding="utf-8",
+    )
+
+    import collections
+    by_type = collections.Counter(r.record_type for r in records)
+    by_sector = collections.Counter(r.sector for r in records)
+    print(f"OK — {len(records)} records → {OUT}")
+    print(f"  transform={n_transform} reextracted={len(reextracted)} curated={len(curated)}")
+    print(f"  by record_type: {dict(by_type)}")
+    print(f"  by sector: {dict(by_sector)}")
+    if not reextracted:
+        print("  NOTE: no ida_reextracted.jsonl — region/gender cells absent until re-extraction runs.")
+
+
+if __name__ == "__main__":
+    main()

scripts/eval/results/benchmark_20260615_163150.json

@@ -0,0 +1,540 @@
+{
+  "meta": {
+    "benchmark": "Pay Equity RAG Benchmark v1",
+    "timestamp": "20260615_163150",
+    "num_questions": 15,
+    "k": 5,
+    "run_generation": false
+  },
+  "aggregate": {
+    "retrieval": {
+      "mrr": 0.7022222222222222,
+      "ndcg@1": 0.6,
+      "ndcg@3": 0.7174573004761944,
+      "ndcg@5": 0.7432474876251638,
+      "recall@1": 0.6,
+      "recall@3": 0.8,
+      "recall@5": 0.8666666666666667
+    },
+    "generation": {
+      "answer_length_chars": 0.0,
+      "answer_length_words": 0.0,
+      "citations_count": 0.0,
+      "fact_coverage": 0.0,
+      "ground_truth_citations_present": 0.0
+    }
+  },
+  "per_question": [
+    {
+      "id": "Q01",
+      "type": "directive",
+      "question": "Can a Danish employer ask me about my previous salary during a job interview?",
+      "ground_truth_chunk_ids": [
+        "dir-art-5"
+      ],
+      "retrieved_chunk_ids": [
+        "dir-art-5",
+        "ida-p023-1594",
+        "ida-p023-1592",
+        "ida-p023-1611",
+        "ida-p023-1603"
+      ],
+      "retrieved_scores": [
+        0.46843454241752625,
+        0.39175179600715637,
+        0.39157167077064514,
+        0.38680705428123474,
+        0.3867814242839813
+      ],
+      "retrieval_time_s": 0.849,
+      "generation_time_s": 0.0,
+      "retrieval_metrics": {
+        "recall@1": 1.0,
+        "recall@3": 1.0,
+        "recall@5": 1.0,
+        "mrr": 1.0,
+        "ndcg@1": 1.0,
+        "ndcg@3": 1.0,
+        "ndcg@5": 1.0
+      },
+      "generation_metrics": null
+    },
+    {
+      "id": "Q02",
+      "type": "directive",
+      "question": "What are my rights as a worker to see what my colleagues earn?",
+      "ground_truth_chunk_ids": [
+        "dir-art-7"
+      ],
+      "retrieved_chunk_ids": [
+        "dir-art-7",
+        "dir-art-12",
+        "dir-art-6",
+        "dir-art-5",
+        "dir-art-9"
+      ],
+      "retrieved_scores": [
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+        0.38052114844322205,
+        0.37416872382164,
+        0.37270212173461914,
+        0.3478201925754547
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+        "ndcg@3": 1.0,
+        "ndcg@5": 1.0
+      },
+      "generation_metrics": null
+    },
+    {
+      "id": "Q03",
+      "type": "directive",
+      "question": "At what company size does mandatory pay gap reporting kick in under the new EU directive?",
+      "ground_truth_chunk_ids": [
+        "dir-art-9"
+      ],
+      "retrieved_chunk_ids": [
+        "dir-art-9",
+        "dir-art-31",
+        "dir-art-29",
+        "dir-art-35",
+        "dir-art-10"
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+        "ndcg@3": 1.0,
+        "ndcg@5": 1.0
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+      "generation_metrics": null
+    },
+    {
+      "id": "Q04",
+      "type": "directive",
+      "question": "What happens if a company's pay report shows women earn less than men — does the directive require specific action?",
+      "ground_truth_chunk_ids": [
+        "dir-art-10"
+      ],
+      "retrieved_chunk_ids": [
+        "dir-art-9",
+        "dir-art-10",
+        "dir-art-29",
+        "dir-art-3",
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+    },
+    {
+      "id": "Q05",
+      "type": "directive",
+      "question": "If I sue my employer for pay discrimination and they didn't follow the transparency rules, who has to prove what?",
+      "ground_truth_chunk_ids": [
+        "dir-art-18"
+      ],
+      "retrieved_chunk_ids": [
+        "dir-art-18",
+        "dir-art-6",
+        "dir-art-5",
+        "dir-art-9",
+        "dir-art-19"
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+        0.37829428911209106,
+        0.34863778948783875,
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+        "ndcg@3": 1.0,
+        "ndcg@5": 1.0
+      },
+      "generation_metrics": null
+    },
+    {
+      "id": "Q06",
+      "type": "directive",
+      "question": "How long do I have to bring an equal pay claim under the EU Pay Transparency Directive?",
+      "ground_truth_chunk_ids": [
+        "dir-art-21"
+      ],
+      "retrieved_chunk_ids": [
+        "dir-art-21",
+        "dir-art-29",
+        "dir-art-9",
+        "dir-art-7",
+        "dir-art-18"
+      ],
+      "retrieved_scores": [
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+        0.5352213382720947,
+        0.511902928352356,
+        0.48460856080055237,
+        0.4799960255622864
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+        "ndcg@5": 1.0
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+      "generation_metrics": null
+    },
+    {
+      "id": "Q07",
+      "type": "directive",
+      "question": "What counts as 'pay' under the directive — just salary or also bonuses and other benefits?",
+      "ground_truth_chunk_ids": [
+        "dir-art-3"
+      ],
+      "retrieved_chunk_ids": [
+        "dir-art-3",
+        "dir-art-4",
+        "dir-art-6",
+        "dir-art-1",
+        "dir-art-10"
+      ],
+      "retrieved_scores": [
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+        0.37218886613845825,
+        0.3629388213157654,
+        0.3602444529533386,
+        0.3525366187095642
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+        "ndcg@5": 1.0
+      },
+      "generation_metrics": null
+    },
+    {
+      "id": "Q08",
+      "type": "directive",
+      "question": "What is the deadline for EU member states to implement the Pay Transparency Directive into national law?",
+      "ground_truth_chunk_ids": [
+        "dir-art-34"
+      ],
+      "retrieved_chunk_ids": [
+        "dir-art-29",
+        "dir-art-9",
+        "dir-art-31",
+        "dir-art-35",
+        "dir-art-34"
+      ],
+      "retrieved_scores": [
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+        0.5249780416488647,
+        0.5037232637405396,
+        0.5034652948379517,
+        0.5011686682701111
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+        "ndcg@5": 0.38685280723454163
+      },
+      "generation_metrics": null
+    },
+    {
+      "id": "Q09",
+      "type": "directive",
+      "question": "What penalties can be imposed on employers who violate the equal pay principle under the directive?",
+      "ground_truth_chunk_ids": [
+        "dir-art-23"
+      ],
+      "retrieved_chunk_ids": [
+        "dir-art-23",
+        "dir-art-24",
+        "dir-art-17",
+        "dir-art-1",
+        "dir-art-18"
+      ],
+      "retrieved_scores": [
+        0.6221146583557129,
+        0.5374859571456909,
+        0.5216008424758911,
+        0.5104919672012329,
+        0.48372018337249756
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+      "retrieval_time_s": 0.615,
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+        "ndcg@3": 1.0,
+        "ndcg@5": 1.0
+      },
+      "generation_metrics": null
+    },
+    {
+      "id": "Q10",
+      "type": "salary",
+      "question": "Hvad er medianlønnen for en civilingeniør i det private med 10 års erfaring ifølge IDAs lønstatistik for 2025?",
+      "ground_truth_chunk_ids": [
+        "ida-p011-0035"
+      ],
+      "retrieved_chunk_ids": [
+        "ida-p011-0035",
+        "ida-p011-0030",
+        "ida-p011-0045",
+        "ida-p021-1496",
+        "ida-p011-0044"
+      ],
+      "retrieved_scores": [
+        0.5657927989959717,
+        0.561237096786499,
+        0.5611233115196228,
+        0.5604856610298157,
+        0.5585825443267822
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+      "retrieval_metrics": {
+        "recall@1": 1.0,
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+        "recall@5": 1.0,
+        "mrr": 1.0,
+        "ndcg@1": 1.0,
+        "ndcg@3": 1.0,
+        "ndcg@5": 1.0
+      },
+      "generation_metrics": null
+    },
+    {
+      "id": "Q11",
+      "type": "salary",
+      "question": "What is the median salary for a civilingeniør (MSc engineer) with 5 years of experience in the Danish private sector? How many respondents and what percentage receive a bonus?",
+      "ground_truth_chunk_ids": [
+        "ida-p011-0040"
+      ],
+      "retrieved_chunk_ids": [
+        "ida-p011-0025",
+        "ida-p011-0029",
+        "ida-p011-0028",
+        "ida-p011-0030",
+        "ida-p011-0027"
+      ],
+      "retrieved_scores": [
+        0.6918340921401978,
+        0.6845650672912598,
+        0.6832934617996216,
+        0.6826284527778625,
+        0.6825269460678101
+      ],
+      "retrieval_time_s": 1.181,
+      "generation_time_s": 0.0,
+      "retrieval_metrics": {
+        "recall@1": 0.0,
+        "recall@3": 0.0,
+        "recall@5": 0.0,
+        "mrr": 0.0,
+        "ndcg@1": 0.0,
+        "ndcg@3": 0.0,
+        "ndcg@5": 0.0
+      },
+      "generation_metrics": null
+    },
+    {
+      "id": "Q12",
+      "type": "salary",
+      "question": "What is the average starting salary for a newly graduated civilingeniør (2025 graduate with 0 years experience) in the Danish private sector?",
+      "ground_truth_chunk_ids": [
+        "ida-p011-0045"
+      ],
+      "retrieved_chunk_ids": [
+        "ida-p011-0044",
+        "ida-p011-0045",
+        "ida-p011-0040",
+        "ida-p011-0038",
+        "ida-p011-0039"
+      ],
+      "retrieved_scores": [
+        0.6287952661514282,
+        0.6287572979927063,
+        0.6224535703659058,
+        0.6201796531677246,
+        0.6197202205657959
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+      "retrieval_time_s": 0.969,
+      "generation_time_s": 0.0,
+      "retrieval_metrics": {
+        "recall@1": 0.0,
+        "recall@3": 1.0,
+        "recall@5": 1.0,
+        "mrr": 0.5,
+        "ndcg@1": 0.0,
+        "ndcg@3": 0.6309297535714575,
+        "ndcg@5": 0.6309297535714575
+      },
+      "generation_metrics": null
+    },
+    {
+      "id": "Q13",
+      "type": "salary",
+      "question": "What is the average salary for a topchef/funktionsdirektør (C-level executive) with 25 years of experience according to IDA 2025 salary statistics?",
+      "ground_truth_chunk_ids": [
+        "ida-p021-1371"
+      ],
+      "retrieved_chunk_ids": [
+        "ida-p021-1371",
+        "ida-p021-1491",
+        "ida-p021-1523",
+        "ida-p021-1515",
+        "ida-p021-1531"
+      ],
+      "retrieved_scores": [
+        0.6942191123962402,
+        0.6869297027587891,
+        0.6857338547706604,
+        0.6853389739990234,
+        0.684471607208252
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+      "retrieval_time_s": 1.151,
+      "generation_time_s": 0.0,
+      "retrieval_metrics": {
+        "recall@1": 1.0,
+        "recall@3": 1.0,
+        "recall@5": 1.0,
+        "mrr": 1.0,
+        "ndcg@1": 1.0,
+        "ndcg@3": 1.0,
+        "ndcg@5": 1.0
+      },
+      "generation_metrics": null
+    },
+    {
+      "id": "Q14",
+      "type": "salary",
+      "question": "What is the average salary in the pharmaceutical industry (medicinalindustri) for IDA engineers with 5 years of experience?",
+      "ground_truth_chunk_ids": [
+        "ida-p017-1176"
+      ],
+      "retrieved_chunk_ids": [
+        "ida-p017-1011",
+        "ida-p017-1033",
+        "ida-p017-1176",
+        "ida-p017-1121",
+        "ida-p017-1044"
+      ],
+      "retrieved_scores": [
+        0.6024791598320007,
+        0.5985130071640015,
+        0.5924195051193237,
+        0.5912789106369019,
+        0.5893527865409851
+      ],
+      "retrieval_time_s": 0.906,
+      "generation_time_s": 0.0,
+      "retrieval_metrics": {
+        "recall@1": 0.0,
+        "recall@3": 1.0,
+        "recall@5": 1.0,
+        "mrr": 0.3333333333333333,
+        "ndcg@1": 0.0,
+        "ndcg@3": 0.5,
+        "ndcg@5": 0.5
+      },
+      "generation_metrics": null
+    },
+    {
+      "id": "Q15",
+      "type": "salary",
+      "question": "What is the average salary for an IDA engineer working in the state sector (staten) with around 20 years of experience?",
+      "ground_truth_chunk_ids": [
+        "ida-p030-1698"
+      ],
+      "retrieved_chunk_ids": [
+        "ida-p030-1713",
+        "ida-p030-1701",
+        "ida-p030-1699",
+        "ida-p030-1693",
+        "ida-p030-1703"
+      ],
+      "retrieved_scores": [
+        0.5219017267227173,
+        0.5170749425888062,
+        0.5162566900253296,
+        0.5155266523361206,
+        0.5143060684204102
+      ],
+      "retrieval_time_s": 0.915,
+      "generation_time_s": 0.0,
+      "retrieval_metrics": {
+        "recall@1": 0.0,
+        "recall@3": 0.0,
+        "recall@5": 0.0,
+        "mrr": 0.0,
+        "ndcg@1": 0.0,
+        "ndcg@3": 0.0,
+        "ndcg@5": 0.0
+      },
+      "generation_metrics": null
+    }
+  ],
+  "summary_text": "\n======================================================================\n  Benchmark Results Summary\n======================================================================\n\n  Retrieval:\n    Recall@1:  0.600  (9/15 questions with ≥1 hit)\n    Recall@3:  0.800\n    Recall@5:  0.867\n    MRR:       0.7022\n    NDCG@5:    0.7432\n\n  Directive questions (9):\n    Recall@1: 0.778  MRR: 0.8556\n  Salary questions (6):\n    Recall@1: 0.333  MRR: 0.4722"
+}

src/backend/fusion.py

@@ -0,0 +1,243 @@
+"""Profile-driven dashboard fusion (v1, salary path).
+
+A single profile matches one cell from several IDA tables (education×cohort,
+position×cohort, region×cohort, gender×leadership, …). This module fuses them:
+for each breakdown axis it picks the most-specific *non-suppressed* cell
+consistent with the profile (falling back to a broader cell when the specific
+one is suppressed), joins the curated growth/forecast records, projects the
+headline figure to 2026, and computes the user's percentile position + gap.
+
+Reliability gating and matching live in ``retrieval``; this layer composes them
+into the ``Dashboard`` the synthesis/UI layer renders.
+"""
+
+from __future__ import annotations
+
+from .retrieval import _profile_constraints, gated_measure, load_records, retrieve
+from .schemas import Dashboard, IdaSalaryRecord, Profile, RecommendationCell
+
+# Readable values for the enum dimensions that can appear in a comparison label.
+_DIM_DISPLAY = {
+    "position_level": {
+        "topchef": "top executive", "funktionsdirektor": "functional director",
+        "afdelingschef": "department head", "projektleder": "project manager",
+        "chef_seniorkonsulent": "chief/senior consultant", "specialist": "specialist",
+        "ingenior_akademiker": "engineer/academic", "anden": "other role", "trainee": "trainee",
+    },
+    "region": {
+        "hovedstaden": "Capital Region", "sjaelland": "Region Zealand",
+        "syddanmark": "Southern Denmark", "midtjylland": "Central Denmark",
+        "nordjylland": "North Denmark",
+    },
+    "gender": {"male": "men", "female": "women"},
+    "is_leader": {"leader": "with staff responsibility", "non_leader": "without staff responsibility"},
+    "manages_people": {"yes": "managing people", "no": "not managing people"},
+    "jobloen": {"yes": "on fixed salary (jobløn)", "no": "not on jobløn"},
+}
+
+# Human label for each breakdown axis (the non-cohort dimension(s) of a cell).
+_AXIS_LABEL = {
+    frozenset(): "Overall (your cohort)",
+    frozenset({"education_type"}): "By education",
+    frozenset({"position_level"}): "By position level",
+    frozenset({"region"}): "By region",
+    frozenset({"gender"}): "By gender",
+    frozenset({"gender", "is_leader"}): "By gender & leadership",
+    frozenset({"is_leader"}): "By leadership",
+    frozenset({"specialization"}): "By specialization",
+    frozenset({"work_area"}): "By work area",
+    frozenset({"branche"}): "By industry",
+    frozenset({"jobloen"}): "Fixed-salary (jobløn)",
+}
+
+_EDU_LABEL = {
+    "civilingenior": "Civil engineers", "akademiingenior": "Academy engineers",
+    "teknikumingenior": "Technical engineers", "diplom_eksportingenior": "Diploma/export engineers",
+    "cand_scient": "Science graduates",
+}
+
+
+def _axis_key(rec: IdaSalaryRecord) -> frozenset[str]:
+    return frozenset(d for d in rec.dimension_keys if d != "graduation_year")
+
+
+def _cohort_label(rec: IdaSalaryRecord) -> str:
+    s, e = rec.graduation_year_start, rec.graduation_year_end
+    if s is None and e is None:
+        return "all cohorts"
+    if s is None:
+        return f"graduated ≤{e}"
+    if e is None:
+        return f"graduated ≥{s}"
+    return f"cohort {s}–{e}" if s != e else f"graduated {s}"
+
+
+def _comparison_label(rec: IdaSalaryRecord) -> str:
+    bits: list[str] = []
+    if rec.education_type != "all":
+        bits.append(_EDU_LABEL.get(rec.education_type, rec.education_type))
+    for dim in ("position_level", "region", "gender", "is_leader",
+                "specialization", "work_area", "branche", "jobloen"):
+        val = getattr(rec, dim)
+        if val != "all":
+            if dim in ("specialization", "work_area", "branche"):
+                bits.append(rec.segment_label or str(val))
+            else:
+                bits.append(_DIM_DISPLAY.get(dim, {}).get(val, str(val)))
+    bits.append(_cohort_label(rec))
+    bits.append(rec.sector.replace("_", " "))
+    return ", ".join(bits)
+
+
+def _to_cell(rec: IdaSalaryRecord, growth_pct: float | None) -> RecommendationCell:
+    m = gated_measure(rec)
+    central = m.median if m.median is not None else m.mean
+    projected = (
+        round(central * (1 + growth_pct / 100)) if central is not None and growth_pct else None
+    )
+    return RecommendationCell(
+        comparison=_comparison_label(rec),
+        citation=f"{rec.source_doc}, tabel {rec.table_id}, p.{rec.source_page}"
+        if rec.table_id else f"{rec.source_doc}, p.{rec.source_page}",
+        record_id=rec.id,
+        dimension_keys=rec.dimension_keys,
+        specificity=rec.specificity,
+        sample_size=rec.sample_size,
+        reliability_tier=rec.reliability_tier,
+        includes_executives=rec.includes_executives,
+        measure=m,
+        projected_median_2026=projected,
+        growth_pct_applied=growth_pct if projected is not None else None,
+    )
+
+
+def _best_per_axis(cands: list[IdaSalaryRecord]) -> dict[frozenset, IdaSalaryRecord]:
+    """Per axis, the most-specific non-suppressed cell (fallback: broadest non-suppressed)."""
+    by_axis: dict[frozenset, list[IdaSalaryRecord]] = {}
+    for r in cands:
+        by_axis.setdefault(_axis_key(r), []).append(r)
+    chosen: dict[frozenset, IdaSalaryRecord] = {}
+    for axis, recs in by_axis.items():
+        ok = [r for r in recs if r.reliability_tier != "suppressed"]
+        if not ok:
+            continue  # whole axis suppressed for this profile → omit
+        # recs already globally sorted most-specific first; ok preserves that.
+        chosen[axis] = ok[0]
+    return chosen
+
+
+def _forecast_pct(profile: Profile) -> float | None:
+    for r in retrieve(profile, record_type="salary_forecast"):
+        if r.cohort_growth_pct is not None:
+            return r.cohort_growth_pct
+    return None
+
+
+def _estimate_percentile(value: float, m) -> float | None:
+    """Approximate percentile of ``value`` within a cell's quantiles."""
+    pts = sorted(
+        (p, getattr(m, k))
+        for p, k in ((25, "p25"), (50, "median"), (75, "p75"), (90, "p90"))
+        if getattr(m, k) is not None
+    )
+    if not pts:
+        return None
+    if value <= pts[0][1]:
+        return round(pts[0][0] * value / pts[0][1], 1) if pts[0][1] else 0.0
+    if value >= pts[-1][1]:
+        lo_p, lo_v = pts[-1]
+        return round(min(100.0, lo_p + (100 - lo_p) * (value - lo_v) / max(lo_v, 1)), 1)
+    for (p0, v0), (p1, v1) in zip(pts, pts[1:]):
+        if v0 <= value <= v1:
+            frac = (value - v0) / (v1 - v0) if v1 != v0 else 0
+            return round(p0 + frac * (p1 - p0), 1)
+    return None
+
+
+def _known_dims(profile: Profile) -> set[str]:
+    """Dimensions the profile actually pins — so a cell genuinely describes the user.
+
+    Cells that split on a dimension the profile didn't provide (e.g. an arbitrary
+    specialization) must not surface as "your" comparison. graduation_year and
+    education_type are always known; leadership is known when management is asked.
+    """
+    known = {"graduation_year"} | set(_profile_constraints(profile))
+    if profile.manages_people is not None:
+        known |= {"is_leader", "manages_people"}
+    return known
+
+
+def build_dashboard(profile: Profile) -> Dashboard:
+    """Fuse the matched IDA cells into a recommendation dashboard."""
+    load_records()
+    growth_pct = _forecast_pct(profile)
+    known = _known_dims(profile)
+    # Only cells whose breakdown axes are all known to the profile describe the
+    # user (the brief: unasked dimensions are free upgrades when onboarding grows).
+    matched = [
+        r for r in retrieve(profile, record_type="salary_observation")
+        if set(r.dimension_keys) <= known
+    ]
+
+    chosen = _best_per_axis(matched)
+    # Stable, useful axis order for display.
+    order = [
+        frozenset({"education_type"}), frozenset({"position_level"}),
+        frozenset({"gender", "is_leader"}), frozenset({"gender"}),
+        frozenset({"region"}), frozenset({"specialization"}),
+        frozenset({"work_area"}), frozenset({"branche"}),
+        frozenset({"jobloen"}), frozenset(),
+    ]
+    axes = [a for a in order if a in chosen] + [a for a in chosen if a not in order]
+    cells = [_to_cell(chosen[a], growth_pct) for a in axes]
+
+    notes: list[str] = []
+    # Headline = most specific non-suppressed cell that has a usable central figure.
+    headline = next(
+        (r for r in matched
+         if r.reliability_tier != "suppressed"
+         and (r.measure.median is not None or r.measure.mean is not None)),
+        None,
+    )
+    user_pct = gap = projected_target = None
+    forecast_range = (3.2, 3.5) if growth_pct else None
+
+    if headline is not None:
+        hm = gated_measure(headline)
+        central = hm.median if hm.median is not None else hm.mean
+        user_gross = profile.current_monthly_gross
+        if user_gross is not None and profile.current_monthly_gross_max:
+            user_gross = (user_gross + profile.current_monthly_gross_max) // 2
+        if user_gross is not None and central is not None:
+            user_pct = _estimate_percentile(user_gross, hm)
+            gap = user_gross - central
+        ask_base = hm.p75 if hm.p75 is not None else central
+        if ask_base is not None and growth_pct:
+            projected_target = round(ask_base * (1 + growth_pct / 100))
+        if headline.includes_executives is False:
+            notes.append("Benchmark excludes top executives (ekskl. topchefer); "
+                         "means are not comparable across this flag.")
+        notes.append(
+            f"Most relevant comparison: {_comparison_label(headline)} "
+            f"(n={int(headline.sample_size) if headline.sample_size else 'n/a'}, "
+            f"{headline.reliability_tier})."
+        )
+
+    if growth_pct:
+        notes.append(
+            f"Figures projected to 2026 using the {forecast_range[0]}–{forecast_range[1]}% "
+            f"private forecast band (~{growth_pct}% applied)."
+        )
+    if not cells:
+        notes.append("No reliable salary cells matched this profile "
+                     "(samples may be suppressed; broaden the profile).")
+
+    return Dashboard(
+        profile=profile,
+        cells=cells,
+        forecast_pct_range=forecast_range,
+        user_percentile=user_pct,
+        gap_to_median=gap,
+        projected_negotiation_target=projected_target,
+        notes=notes,
+    )

src/backend/indexing/ida_transform.py

@@ -0,0 +1,405 @@
+"""Transform the verified flat ``salary_records.jsonl`` into the structured
+``IdaSalaryRecord`` schema (the recoverable tables).
+
+The original extraction (deterministic ``pdfplumber``, 0/7,000 digit mismatches)
+captured each cell's numbers, cohort, sector and the raw ``segment_label``. For
+the tables where the second breakdown axis survived in ``segment_label``
+(education in the title, specialization t8, work_area t9, branche t10, position
+t13, jobløn t16), we can map straight into the typed wildcard schema here — no
+re-parsing. The tables whose second axis was flattened away (region t11, gender×
+leadership t17, public position t27, management t14) are handled separately by
+re-extraction and are skipped here.
+
+WILDCARD CONTRACT: every filterable dimension is set to a specific enum value or
+the sentinel ``"all"`` (aggregate) — never null.
+"""
+
+from __future__ import annotations
+
+import re
+
+from ..schemas import IdaSalaryRecord, Measure
+
+# Tables re-extracted from the PDF (their discriminating axis was lost in the
+# flat extraction) — skip them here to avoid emitting wrong/aggregate-only cells.
+REEXTRACT_TABLES = {11, 14, 17, 27}
+
+# table_id → education_type (tables 3–7 break down a single education by cohort).
+EDUCATION_BY_TABLE = {
+    3: "akademiingenior",
+    4: "civilingenior",
+    5: "teknikumingenior",
+    6: "diplom_eksportingenior",
+    7: "cand_scient",
+}
+
+# Ordinal rank for "next level up" framing; null when position_level == "all".
+POSITION_RANK = {
+    "trainee": 1, "anden": 2, "ingenior_akademiker": 3, "specialist": 4,
+    "chef_seniorkonsulent": 5, "projektleder": 6, "afdelingschef": 7,
+    "funktionsdirektor": 8, "topchef": 9,
+}
+
+# management_span_band → (span_min, span_max). Used only by re-extraction (t14);
+# included for completeness so the transform stays the single source of truth.
+SPAN_BOUNDS = {
+    "none": (None, None), "1-4": (1, 4), "5-10": (5, 10), "11-19": (11, 19),
+    "20-29": (20, 29), "30-50": (30, 50), "51-100": (51, 100),
+    "101-200": (101, 200), "201-500": (201, 500), "501-1000": (501, 1000),
+    "1001+": (1001, None), "all": (None, None),
+}
+
+# data_period_month by sector (private snapshot Sept 2025, public May 2025).
+PERIOD_MONTH = {
+    "private": "2025-09", "public_state": "2025-05",
+    "public_municipal": "2025-05", "public_regional": "2025-05",
+    "self_employed": None,
+}
+
+
+def _fold(s: str) -> str:
+    """Normalize a Danish label for matching: strip count prefix, fold æøå, keep alnum."""
+    s = re.sub(r"^\d+\s+", "", s.strip())  # '21 Alle' → 'Alle', '17 Org…' → 'Org…'
+    s = s.lower().replace("æ", "ae").replace("ø", "oe").replace("å", "aa")
+    return re.sub(r"[^a-z0-9]", "", s)
+
+
+def _is_all(label: str | None) -> bool:
+    """True when the label is the aggregate ('Alle' / 'All' / empty)."""
+    return label is None or _fold(label) in {"alle", "all", ""}
+
+
+# Normalized-label → enum maps for the recoverable segment dimensions.
+_SPECIALIZATION = {
+    "byggerianlaeg": "byggeri_anlaeg",
+    "datalogiitsoftware": "datalogi_it_software",
+    "elektroniksvagstaerkstroem": "elektronik",
+    "kemibiologibiotek": "kemi_biologi_biotek",
+    "maskinindustriproduktionrobotmv": "maskin_industri_robot",
+    "energimiljoe": "energi_miljo",
+    "eksportledelseforretningsudvikling": "eksport_ledelse_forretning",
+    "levnedsmiddel": "levnedsmiddel",
+    "matematikfysikgeologi": "matematik_fysik_geologi",
+}
+_WORK_AREA = {
+    "forskninginnovationundervisning": "forskning_innovation_undervisning",
+    "byggerianlaeg": "byggeri_anlaeg",
+    "industriproduktionkemi": "industri_produktion_kemi",
+    "elektroteknikogtele": "elektroteknik_tele",
+    "itogprogrammering": "it_programmering",
+    "energiforsyningmiljoe": "energi_forsyning_miljo",
+    "transportinfrastruktur": "transport_infrastruktur",
+    "naturvidenskabmedicin": "naturvidenskab_medicin",
+    "administrationledelseservice": "administration_ledelse_service",
+}
+_BRANCHE = {
+    "raastofindvindingmv": "raastofindvinding",
+    "forsyningsvirksomhed": "forsyningsvirksomhed",
+    "foedevarekemiplasttraeindustri": "foedevare_kemi_plast_trae",
+    "medicinalindustri": "medicinalindustri",
+    "maskinjernogmetalindustri": "maskin_jern_metal",
+    "denelektroniskeindustri": "elektronisk_industri",
+    "raadgivendeingenioervirksomhed": "raadgivende_ingenior",
+    "entreprenoervirksomhedbyggeoganlaeg": "entreprenor_bygge_anlaeg",
+    "itogtelekommunikationsvirksomhed": "it_telekommunikation",
+    "forskningogudviklingfinansmv": "forskning_udvikling_finans",
+    "handeltransport": "handel_transport",
+    "organisationermv": "organisationer",
+}
+
+
+def _lookup(label: str, table: dict[str, str], dim: str) -> str:
+    """Map a raw segment label to its enum value; fail loud on vocabulary drift."""
+    if _is_all(label):
+        return "all"
+    key = _fold(label)
+    if key not in table:
+        raise ValueError(f"Unmapped {dim} label {label!r} (folded {key!r})")
+    return table[key]
+
+
+def _position_to_enum(label: str) -> str:
+    """Map a tabel-13 position label to the position_level enum (keyword-ordered)."""
+    if _is_all(label):
+        return "all"
+    low = label.lower()
+    if "topchef" in low or "funktionsdirekt" in low:
+        return "topchef"
+    if "afdelingschef" in low:
+        return "afdelingschef"
+    if "projektleder" in low:
+        return "projektleder"
+    if "seniorkonsulent" in low or low.startswith("chef"):
+        return "chef_seniorkonsulent"
+    if "specialist" in low:
+        return "specialist"
+    if "trainee" in low or "erhvervsph" in low:
+        return "trainee"
+    if "anden" in low:
+        return "anden"
+    if "ingeniør" in low or "akademisk" in low or "konsulent" in low:
+        return "ingenior_akademiker"
+    raise ValueError(f"Unmapped position label {label!r}")
+
+
+def reliability_tier(n: float | None) -> str:
+    """KRL suppression rule on sample size."""
+    if n is None or n < 5:
+        return "suppressed"
+    if n < 10:
+        return "mean_only"
+    if n < 20:
+        return "median_ok"
+    if n < 50:
+        return "quartiles_ok"
+    return "full"
+
+
+def _includes_executives(title: str | None) -> bool | None:
+    """Read inkl./ekskl. topchefer from the caption; None when not stated."""
+    if not title:
+        return None
+    low = title.lower()
+    if "topchef" not in low:
+        return None
+    if "ekskl" in low or "uden topchef" in low:
+        return False
+    if "inkl" in low or "med topchef" in low:
+        return True
+    return None
+
+
+def _corrected_cohort(rec: dict) -> tuple[int | None, int | None]:
+    """Return (graduation_year_start, end) with the 'Før YYYY' convention fixed.
+
+    The source extraction stored open-ended-below cohorts backwards (e.g.
+    'Før 1985' → start=1985, end=None). The contract is half-open the other way:
+    'Før YYYY' = graduated in or before YYYY-1, i.e. start=None, end=YYYY-1.
+    Bounded cohorts ('1986-1987') and 'Alle' (None/None) are already correct.
+    """
+    label = (rec.get("row_label") or "").lower()
+    m = re.search(r"(?:før|foer)\s*(\d{4})", label)
+    if m:
+        return None, int(m.group(1)) - 1
+    return rec.get("graduation_year_start"), rec.get("graduation_year_end")
+
+
+def transform_record(rec: dict) -> IdaSalaryRecord | None:
+    """Map one flat record into an ``IdaSalaryRecord``; None if it should be skipped."""
+    tid = rec.get("table_id")
+    if tid in REEXTRACT_TABLES or tid is None:
+        return None  # handled by re-extraction (or an orphan fragment)
+
+    sector = rec["sector"]
+    seg = rec.get("segment_label")
+    dims: list[str] = []
+
+    education_type = "all"
+    position_level = "all"
+    specialization = "all"
+    work_area = "all"
+    branche = "all"
+    jobloen = "all"
+
+    if tid in EDUCATION_BY_TABLE:
+        education_type = EDUCATION_BY_TABLE[tid]
+        dims.append("education_type")
+    elif tid == 8:
+        specialization = _lookup(seg, _SPECIALIZATION, "specialization")
+        if specialization != "all":
+            dims.append("specialization")
+    elif tid == 9:
+        work_area = _lookup(seg, _WORK_AREA, "work_area")
+        if work_area != "all":
+            dims.append("work_area")
+    elif tid == 10:
+        branche = _lookup(seg, _BRANCHE, "branche")
+        if branche != "all":
+            dims.append("branche")
+    elif tid == 13:
+        position_level = _position_to_enum(seg)
+        if position_level != "all":
+            dims.append("position_level")
+    elif tid == 16:
+        jobloen = "yes"  # the whole table is jobløn-paid engineers
+        dims.append("jobloen")
+    # else: public cohort/løntrin tables (26,28,29,32,33,34,35,38,39) and
+    # self-employed (42) — sector is their only typed axis (+ cohort below).
+
+    gy_start, gy_end = _corrected_cohort(rec)
+    if gy_start is not None or gy_end is not None:
+        dims.append("graduation_year")
+    # Recompute experience from corrected bounds (2025 snapshot proxy).
+    exp_min = 2025 - gy_end if gy_end is not None else None
+    exp_max = 2025 - gy_start if gy_start is not None else None
+
+    n = rec.get("measure", {}).get("count")
+    record_type = "self_employed_income" if sector == "self_employed" else "salary_observation"
+
+    out = IdaSalaryRecord(
+        id=rec["id"],
+        source_doc=rec["source_doc"],
+        source_page=rec["source_page"],
+        table_id=tid,
+        table_title=rec.get("table_title"),
+        table_group=rec.get("table_group"),
+        record_type=record_type,
+        sector=sector,
+        period=rec.get("period"),
+        data_period_month=PERIOD_MONTH.get(sector),
+        pay_concept=rec["pay_concept"],
+        includes_executives=_includes_executives(rec.get("table_title")),
+        education_type=education_type,
+        position_level=position_level,
+        position_level_rank=POSITION_RANK.get(position_level),
+        specialization=specialization,
+        work_area=work_area,
+        branche=branche,
+        jobloen=jobloen,
+        row_dimension=rec.get("row_dimension"),
+        row_label=rec.get("row_label"),
+        segment_dimension=rec.get("segment_dimension"),
+        segment_label=seg,
+        graduation_year_start=gy_start,
+        graduation_year_end=gy_end,
+        experience_years_min=exp_min,
+        experience_years_max=exp_max,
+        age_min=rec.get("age_min"),
+        age_max=rec.get("age_max"),
+        dimension_keys=dims,
+        specificity=len(dims),
+        sample_size=n,
+        reliability_tier=reliability_tier(n),
+        measure=Measure(**rec.get("measure", {})),
+        rag_text="",  # filled by the orchestrator's make_rag_text()
+    )
+    return out
+
+
+def transform_corpus(records: list[dict]) -> list[IdaSalaryRecord]:
+    """Transform all recoverable IDA records (skips re-extracted tables)."""
+    out: list[IdaSalaryRecord] = []
+    for rec in records:
+        if rec.get("union") != "IDA":
+            continue
+        t = transform_record(rec)
+        if t is not None:
+            out.append(t)
+    return out
+
+
+# ---------------------------------------------------------------------------
+# rag_text generation (English; embedded for vector recall). Per the contract,
+# rag_text MUST name the human-readable value of every dimension_keys entry,
+# carry the reliability caveat, and cite the source.
+# ---------------------------------------------------------------------------
+
+_DISPLAY: dict[str, dict[str, str]] = {
+    "education_type": {
+        "civilingenior": "Civil engineers (civilingeniører)",
+        "akademiingenior": "Academy engineers (akademiingeniører)",
+        "teknikumingenior": "Technical engineers (teknikumingeniører)",
+        "diplom_eksportingenior": "Diploma/export engineers",
+        "cand_scient": "Science graduates (cand.scient.)",
+    },
+    "sector": {
+        "private": "private sector", "public_state": "state sector",
+        "public_municipal": "municipal sector", "public_regional": "regional sector",
+        "self_employed": "self-employed",
+    },
+    "region": {
+        "hovedstaden": "Capital Region", "sjaelland": "Region Zealand",
+        "syddanmark": "Region of Southern Denmark", "midtjylland": "Central Denmark Region",
+        "nordjylland": "North Denmark Region",
+    },
+    "position_level": {
+        "topchef": "top executives", "funktionsdirektor": "functional directors",
+        "afdelingschef": "department heads/team leaders", "projektleder": "project managers",
+        "chef_seniorkonsulent": "chief/senior consultants", "specialist": "specialists",
+        "ingenior_akademiker": "engineers/academic staff", "anden": "other roles",
+        "trainee": "trainees",
+    },
+    "gender": {"male": "men", "female": "women"},
+    "is_leader": {"leader": "with staff responsibility", "non_leader": "without staff responsibility"},
+    "manages_people": {"yes": "managing people", "no": "not managing people"},
+    "jobloen": {"yes": "on fixed salary (jobløn)", "no": "not on jobløn"},
+}
+
+_TIER_CAVEAT = {
+    "suppressed": "Sample too small to report figures (n<5).",
+    "mean_only": "Small sample (n=5–9): average only.",
+    "median_ok": "Limited sample (n=10–19): median shown, no quartiles.",
+    "quartiles_ok": "Sample n=20–49: quartiles shown, no 90th percentile.",
+    "full": "Full reliability (n≥50).",
+}
+
+
+def _cohort_phrase(rec: IdaSalaryRecord) -> str:
+    s, e = rec.graduation_year_start, rec.graduation_year_end
+    if s is None and e is None:
+        return ""
+    if s is None:
+        return f"graduated in or before {e}"
+    if e is None:
+        return f"graduated {s} or later"
+    return f"graduation cohort {s}–{e}" if s != e else f"graduated {s}"
+
+
+def _dim_phrases(rec: IdaSalaryRecord) -> list[str]:
+    """Human-readable phrase for each specific dimension key."""
+    out: list[str] = []
+    for key in rec.dimension_keys:
+        if key == "graduation_year":
+            p = _cohort_phrase(rec)
+            if p:
+                out.append(p)
+            continue
+        val = getattr(rec, key, "all")
+        disp = _DISPLAY.get(key, {}).get(val)
+        # specialization/work_area/branche keep their raw Danish label (readable).
+        if disp is None:
+            disp = (rec.segment_label or val) if key in (
+                "specialization", "work_area", "branche") else val
+        out.append(str(disp))
+    return out
+
+
+def _stats_phrase(rec: IdaSalaryRecord) -> str:
+    """Figures permitted by the reliability tier, in DKK/month."""
+    m, tier = rec.measure, rec.reliability_tier
+    if tier == "suppressed":
+        return ""
+    parts: list[str] = []
+    if m.mean is not None:
+        parts.append(f"average {int(m.mean):,} DKK/month")
+    if tier in ("median_ok", "quartiles_ok", "full") and m.median is not None:
+        parts.append(f"median {int(m.median):,}")
+    if tier in ("quartiles_ok", "full") and m.p25 is not None and m.p75 is not None:
+        parts.append(f"quartiles {int(m.p25):,}–{int(m.p75):,}")
+    if tier == "full" and m.p90 is not None:
+        parts.append(f"90th pct {int(m.p90):,}")
+    return ", ".join(parts).replace(",", ".")  # Danish thousands separator
+
+
+def make_rag_text(rec: IdaSalaryRecord) -> str:
+    """Build the embedding text for a record (English, dimension-complete)."""
+    edu = _DISPLAY["education_type"].get(rec.education_type, "IDA engineers")
+    head = edu if rec.education_type != "all" else "IDA members"
+    sector = _DISPLAY["sector"].get(rec.sector, rec.sector)
+    dims = [d for d in _dim_phrases(rec) if d and d != head]
+    seg = f" — {', '.join(dims)}" if dims else ""
+
+    if rec.record_type == "salary_growth_rate":
+        body = f"3-year gliding annual salary growth {rec.cohort_growth_pct}% ({sector})."
+    elif rec.record_type == "salary_forecast":
+        body = f"2026 salary forecast {rec.cohort_growth_pct}% expected pay increase ({sector})."
+    else:
+        stats = _stats_phrase(rec)
+        n = int(rec.sample_size) if rec.sample_size is not None else "n/a"
+        body = (f"{stats}. Based on {n} respondents."
+                if stats else f"Based on {n} respondents.")
+
+    caveat = _TIER_CAVEAT.get(rec.reliability_tier, "")
+    tbl = f", tabel {rec.table_id}" if rec.table_id else ""
+    cite = f"Source: {rec.source_doc}{tbl}, p.{rec.source_page}."
+    return f"IDA Lønstatistik 2025 — {head}, {sector}{seg}. {body} {caveat} {cite}".strip()

src/backend/retrieval.py

@@ -0,0 +1,132 @@
+"""Profile → IDA cell matching, ranking, and reliability gating (v1).
+
+The brief's pipeline, in order:
+  1. wildcard pre-filter (record_type → sector → remaining dimensions),
+  2. (optional) vector search within the candidate set,
+  3. specificity ranking (prefer the most specific cell consistent with the
+     profile; fall back to broader cells when the specific one is suppressed),
+  4. reliability gating (only render the measure.* a cell's tier permits).
+
+This module is deterministic and CPU-only — it operates on the structured
+records loaded from ``ida_records.jsonl``. Vector search is an optional recall
+backstop for free-text queries; the profile-driven dashboard does not need it.
+"""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+from .config import REPO_ROOT
+from .schemas import IdaSalaryRecord, Measure, Profile
+
+_RECORDS_PATH = REPO_ROOT / "data" / "processed" / "lonstatistik" / "ida_records.jsonl"
+_records: list[IdaSalaryRecord] | None = None
+
+# Dimensions the onboarding profile actually constrains. specialization /
+# work_area / branche are not asked (so never constrained → match any cell);
+# stem_count_band and management_span are captured but DO NOT filter in v1.
+_PROFILE_FILTER_DIMS = ("education_type", "region", "position_level", "gender",
+                        "manages_people")
+
+
+def load_records(path: Path | None = None) -> list[IdaSalaryRecord]:
+    """Load the structured IDA corpus once (lazy singleton)."""
+    global _records
+    if _records is None or path is not None:
+        p = path or _RECORDS_PATH
+        recs = [
+            IdaSalaryRecord(**json.loads(line))
+            for line in p.read_text(encoding="utf-8").splitlines()
+            if line.strip()
+        ]
+        if path is not None:
+            return recs
+        _records = recs
+    return _records
+
+
+def _profile_constraints(profile: Profile) -> dict[str, str]:
+    """The dimensions the profile pins to a specific value (drops 'all'/None)."""
+    raw = {
+        "education_type": profile.education_type,
+        "region": profile.region,
+        "position_level": profile.position_level,
+        "gender": profile.gender,
+        "manages_people": profile.manages_people,
+    }
+    return {
+        d: v for d, v in raw.items()
+        if d in _PROFILE_FILTER_DIMS and v is not None and v != "all"
+    }
+
+
+def matches(rec: IdaSalaryRecord, profile: Profile) -> bool:
+    """Wildcard match on the remaining dimensions + graduation-year interval.
+
+    (record_type and sector are pre-filtered by the caller.) A dimension matches
+    iff the record's value equals the profile's OR is the ``"all"`` aggregate.
+    Dimensions the profile leaves open are not constrained.
+    """
+    for dim, pval in _profile_constraints(profile).items():
+        rval = getattr(rec, dim)
+        if rval != pval and rval != "all":
+            return False
+    # graduation_year by interval (half-open; null = unbounded on that side).
+    y = profile.graduation_year
+    s, e = rec.graduation_year_start, rec.graduation_year_end
+    if (s is not None and y < s) or (e is not None and y > e):
+        return False
+    return True
+
+
+def _renderable_fields(tier: str) -> set[str]:
+    """Which measure.* fields a reliability tier permits rendering."""
+    if tier == "suppressed":
+        return set()
+    fields = {"mean", "base", "supplement", "pension", "bonus_avg", "bonus_pct", "count"}
+    if tier in ("median_ok", "quartiles_ok", "full"):
+        fields.add("median")
+    if tier in ("quartiles_ok", "full"):
+        fields |= {"p25", "p75"}
+    if tier == "full":
+        fields.add("p90")
+    return fields
+
+
+def gated_measure(rec: IdaSalaryRecord) -> Measure:
+    """Return a Measure with only the fields the record's tier permits."""
+    allowed = _renderable_fields(rec.reliability_tier)
+    kept = {
+        f: getattr(rec.measure, f)
+        for f in allowed
+        if getattr(rec.measure, f) is not None
+    }
+    return Measure(**kept)
+
+
+def _rank_key(rec: IdaSalaryRecord) -> tuple:
+    """Most specific first; among equals prefer larger, more reliable samples."""
+    return (rec.specificity, rec.sample_size or 0)
+
+
+def retrieve(
+    profile: Profile,
+    record_type: str = "salary_observation",
+    sectors: tuple[str, ...] | None = None,
+) -> list[IdaSalaryRecord]:
+    """Hard pre-filter (record_type → sector → dims) then specificity ranking.
+
+    Returns all matching records, most-specific first. Suppressed cells are
+    retained so the fusion layer can fall back to broader cells when a specific
+    one is suppressed.
+    """
+    allow_sectors = sectors or (profile.sector, "all")
+    out = [
+        r for r in load_records()
+        if r.record_type == record_type
+        and r.sector in allow_sectors
+        and matches(r, profile)
+    ]
+    out.sort(key=_rank_key, reverse=True)
+    return out

src/backend/schemas.py

@@ -7,6 +7,8 @@ implementations have a stable contract to build against.
 
 from __future__ import annotations
 
+from typing import Literal, Optional
+
 from pydantic import BaseModel, Field
 
 
@@ -74,3 +76,209 @@ class ChatResponse(BaseModel):
         "This is general information, not legal advice. "
         "Consult a qualified professional for your situation."
     )
+
+
+# ---------------------------------------------------------------------------
+# IDA salary corpus (v1) — structured, profile-matchable records.
+#
+# These types mirror data/processed/lonstatistik/ida_salary_record.schema.json,
+# the authoritative contract. That JSON Schema is the source of truth and every
+# built record is validated against it; these Literals give the transform/match
+# code type safety for the dimensions we actually filter on. WILDCARD CONTRACT:
+# every filterable dimension is a required enum that includes "all" (the only
+# wildcard). Profiles never carry "all" — a skipped answer simply drops the
+# constraint (which then matches "all" cells and any specific cell).
+# ---------------------------------------------------------------------------
+
+RecordType = Literal[
+    "salary_observation", "salary_growth_rate", "salary_forecast",
+    "minimum_wage_recommendation", "benefit_prevalence", "terms_stat",
+    "self_employed_income",
+]
+Sector = Literal[
+    "private", "public_state", "public_municipal", "public_regional",
+    "self_employed", "all",
+]
+EducationType = Literal[
+    "civilingenior", "akademiingenior", "diplom_eksportingenior",
+    "teknikumingenior", "cand_scient", "all",
+]
+Region = Literal[
+    "hovedstaden", "sjaelland", "syddanmark", "midtjylland", "nordjylland", "all",
+]
+PositionLevel = Literal[
+    "topchef", "funktionsdirektor", "afdelingschef", "projektleder",
+    "chef_seniorkonsulent", "specialist", "ingenior_akademiker", "anden",
+    "trainee", "all",
+]
+Gender = Literal["male", "female", "all"]
+IsLeader = Literal["leader", "non_leader", "all"]
+YesNoAll = Literal["yes", "no", "all"]
+ManagementSpanBand = Literal[
+    "none", "1-4", "5-10", "11-19", "20-29", "30-50", "51-100", "101-200",
+    "201-500", "501-1000", "1001+", "all",
+]
+StemCountBand = Literal["1-9", "10-24", "25-99", "100-249", "250+", "all"]
+ReliabilityTier = Literal[
+    "suppressed", "mean_only", "median_ok", "quartiles_ok", "full",
+]
+DimensionKey = Literal[
+    "sector", "education_type", "region", "position_level", "gender",
+    "is_leader", "manages_people", "management_span_band", "stem_count_band",
+    "specialization", "work_area", "branche", "jobloen", "graduation_year",
+]
+
+# The filterable dimension fields (in the brief's pre-filter order: record_type
+# and sector are applied first, separately). Used by the matcher/transform.
+FILTER_DIMENSIONS: tuple[str, ...] = (
+    "education_type", "region", "position_level", "gender", "is_leader",
+    "manages_people", "stem_count_band", "specialization", "work_area",
+    "branche", "jobloen",
+)
+
+
+class Measure(BaseModel):
+    """Reported statistics. All monetary values DKK; bonus_pct a percentage."""
+
+    count: float | None = None
+    mean: float | None = None
+    p25: float | None = None
+    median: float | None = None
+    p75: float | None = None
+    p90: float | None = None
+    base: float | None = None
+    supplement: float | None = None
+    pension: float | None = None
+    bonus_avg: float | None = None
+    bonus_pct: float | None = None
+
+
+class IdaSalaryRecord(BaseModel):
+    """One structured IDA observation (mirrors the JSON Schema contract).
+
+    Built by the transform/re-extraction, validated against the JSON Schema,
+    then embedded. Every filterable dimension defaults to ``"all"`` so a missed
+    backfill is a visible aggregation, never a hidden null.
+    """
+
+    id: str
+    union: Literal["IDA"] = "IDA"
+    source_doc: str
+    source_page: int
+    table_id: int | None = None
+    table_title: str | None = None
+    table_group: str | None = None
+    record_type: RecordType = "salary_observation"
+    sector: Sector
+    period: str | None = None
+    data_period_month: str | None = None
+    currency: Literal["DKK"] = "DKK"
+    pay_concept: Literal[
+        "gross_monthly", "gross_monthly_with_components", "net_monthly",
+        "annual_income",
+    ]
+    includes_executives: bool | None = None
+    # Filterable dimensions (required, "all" = aggregate / wildcard).
+    education_type: EducationType = "all"
+    region: Region = "all"
+    position_level: PositionLevel = "all"
+    position_level_rank: int | None = None
+    gender: Gender = "all"
+    is_leader: IsLeader = "all"
+    manages_people: YesNoAll = "all"
+    management_span_band: ManagementSpanBand = "all"
+    span_min: int | None = None
+    span_max: int | None = None
+    stem_count_band: StemCountBand = "all"
+    specialization: str = "all"
+    work_area: str = "all"
+    branche: str = "all"
+    jobloen: YesNoAll = "all"
+    # Provenance (display only).
+    row_dimension: str | None = None
+    row_label: str | None = None
+    segment_dimension: str | None = None
+    segment_label: str | None = None
+    graduation_year_start: int | None = None
+    graduation_year_end: int | None = None
+    experience_years_min: int | None = None
+    experience_years_max: int | None = None
+    age_min: int | None = None
+    age_max: int | None = None
+    cohort_growth_pct: float | None = None
+    # Retrieval helpers.
+    dimension_keys: list[DimensionKey] = Field(default_factory=list)
+    specificity: int = 0
+    sample_size: float | None = None
+    reliability_tier: ReliabilityTier
+    measure: Measure
+    rag_text: str
+
+
+class Profile(BaseModel):
+    """User profile built from the 9 onboarding answers.
+
+    Maps to hard filters except where noted. ``graduation_year`` matches cohort
+    intervals (not equality). ``gender`` is skippable: ``None`` drops the
+    constraint. ``current_monthly_gross`` is *input*, not a filter — normalised
+    to IDA bruttoløn (base + permanent/temporary tillæg + fritvalg + taxable
+    perks + own and employer pension incl. ATP). ``stem_count_band`` /
+    ``management_span_band`` are captured but do not filter in v1 (no IDA data);
+    ``total_headcount_band`` is captured and unused.
+    """
+
+    education_type: EducationType  # Q1
+    graduation_year: int           # Q2 (→ cohort interval; experience = 2025 − year)
+    sector: Sector                 # Q3
+    gender: Optional[Literal["male", "female"]] = None  # Q4 (skippable)
+    current_monthly_gross: int | None = None            # Q5 (input, not a filter)
+    current_monthly_gross_max: int | None = None        # Q5 upper bound if a range
+    position_level: PositionLevel = "all"               # Q6
+    manages_people: Literal["yes", "no"] | None = None  # Q7
+    management_span_band: ManagementSpanBand | None = None  # Q7 (captured, unused v1)
+    stem_count_band: StemCountBand | None = None        # Q8a (captured, unused v1)
+    region: Region = "all"                              # Q8 (region)
+    total_headcount_band: str | None = None             # Q8b (captured, unused v1)
+
+    @property
+    def experience_years(self) -> int:
+        """Seniority proxy used for display (snapshot year is 2025)."""
+        return 2025 - self.graduation_year
+
+
+class RecommendationCell(BaseModel):
+    """One fused comparison cell rendered to the dashboard.
+
+    Only the ``measure`` fields permitted by ``reliability_tier`` are populated;
+    forbidden statistics are left ``None`` (never rendered).
+    """
+
+    comparison: str  # human label, e.g. "Civilingeniører, your cohort, private"
+    citation: str    # e.g. "IDA Lønstatistik 2025, tabel 4, p.11"
+    record_id: str
+    dimension_keys: list[DimensionKey] = Field(default_factory=list)
+    specificity: int = 0
+    sample_size: float | None = None
+    reliability_tier: ReliabilityTier = "suppressed"
+    includes_executives: bool | None = None
+    measure: Measure = Field(default_factory=Measure)
+    # 2026 projection of the cell's central figure (median or mean), if computed.
+    projected_median_2026: float | None = None
+    growth_pct_applied: float | None = None
+
+
+class Dashboard(BaseModel):
+    """Profile-driven recommendation dashboard (v1, salary path)."""
+
+    profile: Profile
+    cells: list[RecommendationCell] = Field(default_factory=list)
+    forecast_pct_range: tuple[float, float] | None = None  # e.g. (3.2, 3.5)
+    user_percentile: float | None = None       # position within the best cell
+    gap_to_median: float | None = None         # user gross − cell median (DKK)
+    projected_negotiation_target: float | None = None  # 2026-projected ask
+    notes: list[str] = Field(default_factory=list)
+    disclaimer: str = (
+        "Salary figures are statistical benchmarks, not guarantees. "
+        "Means are not comparable across inkl./ekskl. topchefer. "
+        "This is general information, not legal or financial advice."
+    )

uv.lock

@@ -335,6 +335,63 @@ wheels = [
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 ]
 
+[[package]]
+name = "cffi"
+version = "2.0.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pycparser", marker = "implementation_name != 'PyPy'" },
+]
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