#!/usr/bin/env python """Build the e-hekim vector index. uv run python scripts/ingest.py Pipeline: load the 14 hospital splits -> clean and deduplicate -> select 1,000 articles balanced across sources -> chunk -> embed with the document prompt -> write to ChromaDB -> export the publishable parquet (url, chunk_text, chunk_vector, + metadata). The Hugging Face token is read from ``.env`` and used only to fetch the source dataset. It is never printed. """ from __future__ import annotations import argparse import json import logging import sys import time from pathlib import Path sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src")) import ehekim # noqa: F401 (applies the torch/Triton compatibility fix first) import numpy as np import pandas as pd from ehekim.config import ( CHUNK_MIN_TOKENS, CHUNK_OVERLAP_TOKENS, CHUNK_TARGET_TOKENS, EMBEDDING_MODEL_ID, PROJECT_ROOT, SOURCE_DATASET_ID, TARGET_ARTICLE_COUNT, get_settings, operator_secrets, ) from ehekim.corpus import ( SELECTION_SEED, build_chunk_records, clean_articles, load_raw_articles, select_articles, ) from ehekim.embedding import Embedder from ehekim.vectorstore import VectorStore logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s") logger = logging.getLogger("ingest") DATA_DIR = PROJECT_ROOT / "data" PARQUET_PATH = DATA_DIR / "ehekim_chunks.parquet" MANIFEST_PATH = DATA_DIR / "ingest_manifest.json" def parse_args() -> argparse.Namespace: p = argparse.ArgumentParser(description="e-hekim ingestion") p.add_argument("--articles", type=int, default=TARGET_ARTICLE_COUNT, help="Number of articles to select (default: 1000).") p.add_argument("--batch-size", type=int, default=16, help="Embedding batch size.") p.add_argument("--device", default=None, help="Force a torch device (cuda/cpu).") p.add_argument("--seed", type=int, default=SELECTION_SEED) p.add_argument("--no-parquet", action="store_true", help="Skip the parquet export.") return p.parse_args() def main() -> int: args = parse_args() settings = get_settings() started = time.time() token = operator_secrets().get("HUGGINGFACE_TOKEN") logger.info("Kaynak veri kümesi indiriliyor: %s", SOURCE_DATASET_ID) raw = load_raw_articles(SOURCE_DATASET_ID, token=token) logger.info("Ham makale sayısı: %s", len(raw)) cleaned = clean_articles(raw) logger.info("Temizleme sonrası: %s makale", len(cleaned)) selected = select_articles(cleaned, target=args.articles, seed=args.seed) per_source = selected.groupby("source").size().to_dict() logger.info("Seçilen makale: %s | kaynak dağılımı: %s", len(selected), per_source) logger.info("Embedding modeli yükleniyor: %s", EMBEDDING_MODEL_ID) embedder = Embedder(device=args.device, batch_size=args.batch_size) logger.info("Parçalama başlıyor (hedef=%s, örtüşme=%s token)", CHUNK_TARGET_TOKENS, CHUNK_OVERLAP_TOKENS) t0 = time.time() records = build_chunk_records(selected, embedder.tokenizer) if not records: logger.error("Hiç parça üretilemedi.") return 1 token_counts = np.array([r.token_count for r in records]) logger.info( "%s parça üretildi (%.1fs) | token ort=%.1f medyan=%s min=%s maks=%s", len(records), time.time() - t0, token_counts.mean(), int(np.median(token_counts)), token_counts.min(), token_counts.max(), ) logger.info("Vektörler hesaplanıyor (%s)...", embedder.device) t0 = time.time() vectors = embedder.encode_documents( [r.chunk_text for r in records], titles=[r.title for r in records], show_progress=True, ) logger.info("Embedding tamamlandı: %s vektör, %.1fs", vectors.shape[0], time.time() - t0) if vectors.shape[1] != embedder.dimension: logger.error("Beklenmeyen vektör boyutu: %s", vectors.shape[1]) return 1 logger.info("ChromaDB koleksiyonu yeniden oluşturuluyor: %s", settings.collection_name) store = VectorStore(settings.chroma_dir, settings.collection_name) store.recreate() store.add( ids=[r.chunk_id for r in records], embeddings=vectors, documents=[r.chunk_text for r in records], metadatas=[r.metadata() for r in records], ) indexed = store.count() logger.info("Dizine eklendi: %s parça", indexed) if indexed != len(records): logger.error("Dizin sayısı uyuşmuyor: %s != %s", indexed, len(records)) return 1 DATA_DIR.mkdir(parents=True, exist_ok=True) if not args.no_parquet: # Column order matches the delivery schema: url, chunk_text, chunk_vector # first, optional metadata after. frame = pd.DataFrame( { "url": [r.url for r in records], "chunk_text": [r.chunk_text for r in records], "chunk_vector": [v.astype(np.float32).tolist() for v in vectors], "chunk_id": [r.chunk_id for r in records], "parent_id": [r.parent_id for r in records], "title": [r.title for r in records], "__source": [r.source for r in records], "chunk_index": [r.chunk_index for r in records], "token_count": [r.token_count for r in records], } ) frame.to_parquet(PARQUET_PATH, index=False) size_mb = PARQUET_PATH.stat().st_size / 1e6 logger.info("Parquet yazıldı: %s (%.1f MB)", PARQUET_PATH, size_mb) manifest = { "source_dataset": SOURCE_DATASET_ID, "embedding_model": EMBEDDING_MODEL_ID, "embedding_dim": int(vectors.shape[1]), "selection_seed": args.seed, "raw_articles": int(len(raw)), "cleaned_articles": int(len(cleaned)), "selected_articles": int(len(selected)), "articles_per_source": {k: int(v) for k, v in per_source.items()}, "chunks": len(records), "chunk_target_tokens": CHUNK_TARGET_TOKENS, "chunk_overlap_tokens": CHUNK_OVERLAP_TOKENS, "chunk_min_tokens": CHUNK_MIN_TOKENS, "token_stats": { "mean": float(token_counts.mean()), "median": float(np.median(token_counts)), "p95": float(np.percentile(token_counts, 95)), "min": int(token_counts.min()), "max": int(token_counts.max()), }, "chunks_per_article": round(len(records) / max(1, len(selected)), 2), "collection": settings.collection_name, "elapsed_seconds": round(time.time() - started, 1), } MANIFEST_PATH.write_text(json.dumps(manifest, ensure_ascii=False, indent=2), encoding="utf-8") logger.info("Manifest yazıldı: %s", MANIFEST_PATH) logger.info("Bitti (%.1fs).", time.time() - started) return 0 if __name__ == "__main__": raise SystemExit(main())