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11e1b6d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 | from __future__ import annotations
import logging
import threading
from collections import OrderedDict
from pathlib import Path
from typing import Dict, Iterable, List, Optional, Sequence, Tuple
from index_builder import BM25Index, build_hadith_index, build_quran_index, load_index, save_index
from normalization import char_ngrams, content_words, normalize_for_matching, normalize_lenient, normalize_strict, tokenize
logger = logging.getLogger(__name__)
BASE_DIR = Path(__file__).resolve().parent
DATA_DIR = BASE_DIR / "data"
INDEX_DIR = BASE_DIR / "index"
class CorpusError(RuntimeError):
"""Raised when a corpus or index file is missing or malformed."""
class SourceRetriever:
def __init__(self, index_dir: Path = INDEX_DIR, data_dir: Path = DATA_DIR) -> None:
self.index_dir, self.data_dir = Path(index_dir), Path(data_dir)
self._hadith_lock = threading.Lock()
self._hadith: Optional[dict] = None
self._norm_cache: "OrderedDict[Tuple[int, str], str]" = OrderedDict()
quran = self._load("quran", build_quran_index)
self.quran: List[dict] = quran["records"]
self.q_norm_match: List[str] = quran["norm"]
self.q_word_count: List[int] = quran["word_count"]
self.q_all_index: Dict[str, List[int]] = quran["all_index"]
self.quran_by_surah: Dict[int, Dict[int, int]] = quran["by_surah"]
self.quran_bm25 = BM25Index(quran["postings"], quran["doc_len"])
self.quran_anchors: Dict[int, List[Tuple[int, int]]] = quran["anchors"]
logger.info("Quran index ready: %d ayahs", len(self.quran))
def _load(self, name: str, builder) -> dict:
path = self.index_dir / f"{name}.idx.gz"
if path.is_file():
try:
return load_index(path)
except Exception:
logger.warning("Index %s is unreadable; rebuilding from data/", path)
source = self.data_dir / ("quran.json" if name == "quran" else "hadith.json")
gz = source.with_name(source.name + ".gz")
source = source if source.is_file() else gz
if not source.is_file():
raise CorpusError(f"Neither {path} nor the source corpus {source} was found")
index = builder(source)
try:
save_index(index, path)
except OSError:
logger.info("Could not cache %s (read-only file system); continuing in memory", path)
return index
def warm(self) -> None:
_ = self.hadith_data
@property
def hadith_loaded(self) -> bool:
return self._hadith is not None
@property
def hadith_data(self) -> dict:
if self._hadith is None:
with self._hadith_lock:
if self._hadith is None:
data = self._load("hadith", build_hadith_index)
data["bm25"] = BM25Index(data["postings"], data["doc_len"])
self._hadith = data
logger.info("Hadith index ready: %d records", len(data["records"]))
return self._hadith
@property
def hadith(self) -> List[dict]:
return self.hadith_data["records"]
@property
def quran_vocabulary(self) -> frozenset:
if getattr(self, "_vocab", None) is None:
self._vocab = frozenset(word for text in self.q_norm_match for word in text.split())
return self._vocab
def search_quran_ayahs(self, query: str, top_k: int = 25, extra_bm25: int = 10) -> List[dict]:
query_words = tokenize(normalize_strict(query))
if not query_words:
return []
votes: Dict[int, int] = {}
for word in query_words:
for idx in self.q_all_index.get(word, ()):
votes[idx] = votes.get(idx, 0) + 1
scored: List[Tuple[int, float]] = []
for idx, vote in votes.items():
coverage = vote / len(query_words)
precision = vote / self.q_word_count[idx] if self.q_word_count[idx] else 0.0
f1 = 2 * coverage * precision / (coverage + precision) if coverage + precision > 0 else 0.0
scored.append((idx, f1))
scored.sort(key=lambda item: item[1], reverse=True)
scores = dict(scored)
ranked = [idx for idx, _ in scored[:top_k]]
seen = set(ranked)
bm25_hits = self.quran_bm25.search(content_words(normalize_for_matching(query).split()), extra_bm25)
ranked += [idx for idx, _ in bm25_hits if idx not in seen]
results = []
for idx in ranked:
candidate = dict(self.quran[idx])
candidate.update(type="Quran", retrieval_score=scores.get(idx, 0.0))
results.append(candidate)
return results
def quran_seed_ayahs(self, query_words: Sequence[str], top_k: int = 25) -> List[int]:
return [idx for idx, _ in self.quran_bm25.search(query_words, top_k)]
def hadith_candidates(self, query_words: Sequence[str], top_k: int) -> List[int]:
return [idx for idx, _ in self.hadith_data["bm25"].search(query_words, top_k)]
def hadith_norm(self, idx: int, field: str) -> Optional[str]:
record = self.hadith[idx]
raw = record.get(field)
if not raw:
return None
key = (idx, field)
if key in self._norm_cache:
self._norm_cache.move_to_end(key)
return self._norm_cache[key]
value = normalize_for_matching(raw)
self._norm_cache[key] = value
if len(self._norm_cache) > 4096:
self._norm_cache.popitem(last=False)
return value
def search_hadith(self, query: str, top_k: int = 15, pool: int = 60) -> List[dict]:
words = content_words(normalize_for_matching(query).split())
if not words:
return []
query_grams = char_ngrams(normalize_lenient(query))
results = []
for idx in self.hadith_candidates(words, pool):
record = self.hadith[idx]
text = record["matn"] or record["full"]
doc_grams = char_ngrams(normalize_lenient(text))
cosine = (
len(query_grams & doc_grams) / ((len(query_grams) * len(doc_grams)) ** 0.5)
if query_grams and doc_grams
else 0.0
)
results.append(
{
"type": "Hadith",
"idx": idx,
"hadithID": record["hadithID"],
"book": record["book"],
"title": record["title"],
"text": text,
"has_matn": bool(record["matn"]),
"retrieval_score": cosine,
}
)
results.sort(key=lambda c: c["retrieval_score"], reverse=True)
return results[:top_k]
|