"""Tunables for the knowledge-extraction pipeline.

Every value here was calibrated on real documents and each one has a reason
recorded in KNOWLEDGE_PIPELINE_CALIBRATION.md. Change them deliberately — most
were arrived at by a measurement, and two of them (`FUZZY_MIN_LEN`,
`CACHE_MIN_TOKENS`) fix bugs that are silent when reintroduced.

Label and cue sets live in `config/*.yaml` so they can be tuned without a code
change: label phrasing is the main recall lever and the filter is very sensitive
to it.
"""

from __future__ import annotations

from functools import lru_cache
from pathlib import Path

import yaml

CONFIG_DIR = Path(__file__).resolve().parent / "config"

# ── Term filter ─────────────────────────────────────────────────────────
# Variant C beat both the English-default and Indonesian-phrasing label sets:
# the other two missed the same class (mining activities and materials).
LABELS_VARIANT = "broad"

# 0.25, not 0.35: measured recall 0.854 @ 0.25 vs 0.658 @ 0.35 on `broad`.
# Precision falls (0.41 vs 0.50) and that is the intended trade — the filter is
# deliberately over-inclusive, clustering and ranking absorb the noise, and a
# term the filter never proposes can never be recovered downstream.
SPAN_SCORE_THRESHOLD = 0.25

# The span NER model truncates past ~384 of its own tokens and *warns rather
# than failing*, so a long chunk silently loses its tail. Indonesian technical
# prose subword-tokenises at roughly 2.5x, so 220-word windows still tripped the
# cap; 130 does not. Chunks are fed as overlapping windows with offsets remapped.
WINDOW_WORDS = 130
WINDOW_OVERLAP = 30
SPAN_TOKEN_CAP = 12

# ── Clustering ──────────────────────────────────────────────────────────
FUZZY_THRESHOLD = 92

# Below this length only exact matching is allowed: "PA" and "UA" score highly
# against each other on token_set_ratio. Over-merging is far worse than
# under-merging — an under-merge costs one extra call and one extra review row,
# a wrong merge destroys a distinct term and the expert never sees it.
FUZZY_MIN_LEN = 5

# ── Evidence ranking ────────────────────────────────────────────────────
EVIDENCE_K = 3
CUE_PROXIMITY_CHARS = 100

EVIDENCE_WEIGHTS: dict[str, float] = {
    "definitional_cue_near": 5.0,
    "term_in_heading": 4.0,
    "in_legend_block": 3.5,
    "formula_present": 2.0,
    "bold_or_italic": 1.5,
    "first_occurrence": 1.0,
    "tabular_penalty": -3.0,
}

# ── Chunking ────────────────────────────────────────────────────────────
MAX_CHUNK_TOKENS = 1500
MAX_HEADING_LEN = 90
BOILERPLATE_MIN_FRAC = 0.6

# ── Extraction ──────────────────────────────────────────────────────────
TEMPERATURE = 0.0

# OpenAI-family prompt caching does not engage AT ALL below this many prompt
# tokens, so a shorter fixed prefix caches nothing and costs ~10x on input. The
# measured hit rate at/above it was 54%.
CACHE_MIN_TOKENS = 1024

# ── Validation ──────────────────────────────────────────────────────────
MAX_ESCALATION_ROUNDS = 2
CONFLICT_OVERLAP_THRESHOLD = 0.4
DUPLICATE_OVERLAP_THRESHOLD = 0.8


@lru_cache(maxsize=4)
def load_yaml(name: str) -> dict:
    with open(CONFIG_DIR / name, encoding="utf-8") as fh:
        return yaml.safe_load(fh)


def labels_for(variant: str = LABELS_VARIANT) -> tuple[list[str], float]:
    """Returns (labels, threshold) for a label variant."""
    cfg = load_yaml("labels.yaml")
    labels = cfg.get(variant) or cfg.get(LABELS_VARIANT) or []
    return list(labels), float(cfg.get("threshold", SPAN_SCORE_THRESHOLD))