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9df97a2 | 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 | #!/usr/bin/env python3
"""Build BERT embeddings and FAISS index from extracted JSONL.
Usage:
PYTHONPATH=backend python backend/scripts/build_bert_faiss.py --input data/extracted_full.jsonl --out models/faiss_index --model sentence-transformers/all-MiniLM-L6-v2 --batch 16
"""
from __future__ import annotations
import argparse
import json
from pathlib import Path
from typing import List
import numpy as np
import joblib
try:
from app.services.cv_extractor import CVExtractionService
except Exception:
CVExtractionService = None
# Try to import sentence-transformers, fallback to transformers
HAS_ST = True
try:
from sentence_transformers import SentenceTransformer
except Exception:
HAS_ST = False
# Try FAISS
HAS_FAISS = True
try:
import faiss
except Exception:
HAS_FAISS = False
from sklearn.neighbors import NearestNeighbors
def read_texts(jsonl_path: Path, limit: int = 0) -> List[dict]:
items = []
with jsonl_path.open("r", encoding="utf-8") as fh:
for i, line in enumerate(fh):
if limit and i >= limit:
break
try:
rec = json.loads(line)
items.append(rec)
except Exception:
continue
return items
def extract_raw_texts(items: List[dict]):
texts = []
files = []
service = CVExtractionService() if CVExtractionService is not None else None
for rec in items:
f = rec.get("file")
raw = rec.get("raw_text")
if raw:
texts.append(raw)
files.append(f)
continue
if not f:
texts.append("")
files.append("")
continue
p = Path(f)
try:
if p.suffix.lower() == ".txt":
texts.append(p.read_text(encoding="utf-8", errors="ignore"))
files.append(f)
else:
if service is not None:
res = service.extract_from_pdf(str(p))
texts.append(res.raw_text or "")
files.append(f)
else:
texts.append("")
files.append(f)
except Exception:
texts.append("")
files.append(f)
return texts, files
def make_embeddings(texts: List[str], model_name: str, batch_size: int = 16):
if HAS_ST:
model = SentenceTransformer(model_name)
embs = model.encode(texts, batch_size=batch_size, show_progress_bar=True, convert_to_numpy=True)
return embs
else:
# Fallback: use HuggingFace transformers with mean pooling
try:
from transformers import AutoTokenizer, AutoModel
import torch
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)
model.eval()
embs = []
for i in range(0, len(texts), batch_size):
batch = texts[i:i+batch_size]
enc = tokenizer(batch, padding=True, truncation=True, return_tensors='pt')
with torch.no_grad():
out = model(**enc)
# mean pooling
last = out.last_hidden_state
mask = enc['attention_mask'].unsqueeze(-1)
summed = (last * mask).sum(1)
counts = mask.sum(1)
emb = (summed / counts).cpu().numpy()
embs.append(emb)
return np.vstack(embs)
except Exception as exc:
raise RuntimeError("No sentence-transformers or transformers available: " + str(exc))
def build_faiss(embs: np.ndarray, out_dir: Path, use_faiss: bool = True):
out_dir.mkdir(parents=True, exist_ok=True)
emb_path = out_dir / 'embeddings.npy'
np.save(str(emb_path), embs)
if use_faiss and HAS_FAISS:
d = embs.shape[1]
index = faiss.IndexFlatIP(d)
# normalize for cosine similarity
faiss.normalize_L2(embs)
index.add(embs)
faiss.write_index(index, str(out_dir / 'faiss.index'))
print(f"Saved FAISS index to {out_dir / 'faiss.index'}")
return {'type': 'faiss', 'index_path': str(out_dir / 'faiss.index'), 'embeddings': str(emb_path)}
else:
nbrs = NearestNeighbors(n_neighbors=10, metric='cosine')
nbrs.fit(embs)
joblib.dump(nbrs, out_dir / 'nn_model.joblib')
print(f"Saved sklearn NearestNeighbors fallback to {out_dir / 'nn_model.joblib'}")
return {'type': 'sklearn', 'model_path': str(out_dir / 'nn_model.joblib'), 'embeddings': str(emb_path)}
def main(argv=None):
parser = argparse.ArgumentParser()
parser.add_argument('--input', required=True, help='extracted JSONL')
parser.add_argument('--out', required=True, help='output directory for index')
parser.add_argument('--model', default='sentence-transformers/all-MiniLM-L6-v2')
parser.add_argument('--batch', type=int, default=16)
parser.add_argument('--limit', type=int, default=0)
args = parser.parse_args(argv)
jsonl = Path(args.input)
out_dir = Path(args.out)
items = read_texts(jsonl, limit=args.limit)
texts, files = extract_raw_texts(items)
print(f"Computing embeddings for {len(texts)} texts (model={args.model})")
embs = make_embeddings(texts, args.model, batch_size=args.batch)
meta = build_faiss(embs, out_dir, use_faiss=True)
mapping = {'files': files, 'meta': meta}
joblib.dump(mapping, out_dir / 'mapping.joblib')
print(f"Saved mapping to {out_dir / 'mapping.joblib'}")
return 0
if __name__ == '__main__':
raise SystemExit(main())
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