#!/usr/bin/env python3
"""Compact recalled prose into small, entity-centred context cards."""
from __future__ import annotations

import re
from collections import defaultdict

SPLIT = re.compile(r"(?<=[。！？；])|\s*\|\s*")
META = re.compile(r"^(When|Involving|来源|时间)\s*[:：]\s*", re.I)


def _key(text: str) -> str:
    text = META.sub("", text)
    return re.sub(r"[\s，。；、,:：|（）()“”\"'\-]+", "", text).lower()


def compact(names: list[str], texts: list[str], *, per_entity: int = 3,
            total: int = 12) -> str:
    """Deduplicate Hindsight recall before it reaches any model prompt.

    Repeated memories often restate the same relationship with progressively more
    prose. Keep one normalized fact per subject and cap every entity so a single
    well-known person cannot consume the whole context window.
    """
    buckets: dict[str, list[str]] = defaultdict(list)
    seen: set[str] = set()
    known = sorted(set(names), key=len, reverse=True)
    for raw in texts:
        for fact in SPLIT.split(re.sub(r"\s+", " ", raw).strip()):
            fact = fact.strip(" -|")
            if len(fact) < 8:
                continue
            k = _key(fact)
            if not k or any(k == old or k in old or old in k for old in seen):
                continue
            seen.add(k)
            owner = next((n for n in known if n in fact), "相关背景")
            if len(buckets[owner]) < per_entity:
                buckets[owner].append(fact[:220])

    lines = []
    for owner, facts in buckets.items():
        lines.append(f"{owner}：")
        lines.extend(f"- {f}" for f in facts)
        if sum(1 for x in lines if x.startswith("- ")) >= total:
            break
    return "\n".join(lines)