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High-Speed GPU Corpus Indexer for Multilingual RAG.
Optimized for Google Colab (T4/V100/A100 GPU) or any NVIDIA CUDA machine.
Runs batch FP16 PyTorch inference to index 148,500+ passages in ~60-90 seconds.
"""
import json
import logging
import os
import subprocess
import sys
import time
from pathlib import Path
from typing import Dict, List, Any
import numpy as np
import torch
# Ensure project root in sys.path
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
import config
from doc_chunking.metadata import Chunk
from doc_chunking.passage_native import process_corpus_passage_native
from doc_chunking.sentence_window import process_longdocs_sentence_window
from doc_chunking.semantic import process_longdocs_semantic
from vector_search.index_faiss import StrategyVectorIndex
logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s", force=True)
logger = logging.getLogger(__name__)
def ensure_lfs_pulled():
"""Check if any corpus files are Git LFS pointers and pull them if needed."""
for lang in config.LANGUAGES:
corpus_file = config.PROCESSED_DATA_DIR / f"{lang}_corpus.jsonl"
if corpus_file.exists():
with open(corpus_file, "r", encoding="utf-8", errors="ignore") as f:
first_line = f.readline()
if first_line.startswith("version https://git-lfs"):
logger.warning("Detected Git LFS pointer in %s. Pulling real data via 'git lfs pull'...", corpus_file.name)
try:
subprocess.run(["git", "lfs", "install"], check=True)
subprocess.run(["git", "lfs", "pull"], check=True)
logger.info("Git LFS pull completed successfully.")
except Exception as e:
logger.error("Failed to run 'git lfs pull': %s", e)
break
def build_indexes_gpu(batch_size: int = 512, output_dir: Path = None):
ensure_lfs_pulled()
output_dir = output_dir or config.INDEX_DIR
output_dir.mkdir(parents=True, exist_ok=True)
device = "cuda" if torch.cuda.is_available() else "cpu"
device_name = torch.cuda.get_device_name(0) if device == "cuda" else "CPU"
logger.info("Using device: %s (%s)", device, device_name)
from sentence_transformers import SentenceTransformer
logger.info("Loading '%s' in FP16 on %s...", config.EMBEDDING_MODEL_NAME, device)
model = SentenceTransformer(config.EMBEDDING_MODEL_NAME, device=device)
if device == "cuda":
model = model.half() # FP16 for 3x Tensor Core throughput
total_start = time.time()
passage_index_file = output_dir / "passage_native.faiss"
passage_meta_file = output_dir / "passage_native_meta.json"
# Check if passage index was already built and saved (> 100MB on disk)
passage_counts: Dict[str, int] = {}
centroid_sums: Dict[str, np.ndarray] = {}
centroid_counts: Dict[str, int] = {}
grand_total = 0
if passage_index_file.exists() and passage_meta_file.exists() and passage_index_file.stat().st_size > 100_000_000:
logger.info("Existing full 'passage_native' index found on disk (%d MB). Skipping re-embedding passages...",
passage_index_file.stat().st_size // (1024 * 1024))
try:
passage_index = StrategyVectorIndex.load(output_dir, "passage_native")
grand_total = len(passage_index.chunks)
for lang in config.LANGUAGES:
passage_counts[lang] = len(passage_index.lang_to_indices.get(lang.lower(), []))
except Exception as e:
logger.warning("Could not load existing index (%s). Rebuilding...", e)
passage_index = None
else:
passage_index = None
if passage_index is None:
passage_index = StrategyVectorIndex("passage_native")
for lang in config.LANGUAGES:
corpus_file = config.PROCESSED_DATA_DIR / f"{lang}_corpus.jsonl"
if not corpus_file.exists():
logger.warning("Corpus file %s not found; skipping '%s'.", corpus_file, lang)
continue
logger.info(">>> Starting language '%s' from %s...", lang, corpus_file.name)
lang_start = time.time()
batch_records: List[Dict[str, Any]] = []
lang_total = 0
with open(corpus_file, "r", encoding="utf-8") as f:
for line in f:
line = line.strip()
if not line:
continue
try:
record = json.loads(line)
except json.JSONDecodeError:
if line.startswith("version https://git-lfs"):
raise RuntimeError(
f"Corpus file {corpus_file} is a Git LFS pointer! Please run 'git lfs pull' first."
)
continue
batch_records.append(record)
if len(batch_records) >= batch_size:
chunks = process_corpus_passage_native(batch_records)
texts = [f"{config.PASSAGE_PREFIX}{c.embed_text.strip()}" for c in chunks]
with torch.inference_mode():
embeddings = model.encode(
texts,
batch_size=batch_size,
show_progress_bar=False,
normalize_embeddings=True,
convert_to_numpy=True,
)
passage_index.add_chunks(chunks, embeddings)
for chunk, vec in zip(chunks, embeddings):
c_lang = chunk.source_lang.lower()
if c_lang not in centroid_sums:
centroid_sums[c_lang] = np.zeros(passage_index.dim, dtype=np.float64)
centroid_counts[c_lang] = 0
centroid_sums[c_lang] += vec.astype(np.float64, copy=False)
centroid_counts[c_lang] += 1
lang_total += len(chunks)
if lang_total % 5120 == 0 or lang_total == len(chunks):
elapsed = time.time() - lang_start
rate = lang_total / max(0.1, elapsed)
logger.info("Language '%s': %d passages indexed (%.1f passages/sec)...", lang, lang_total, rate)
batch_records.clear()
if batch_records:
chunks = process_corpus_passage_native(batch_records)
texts = [f"{config.PASSAGE_PREFIX}{c.embed_text.strip()}" for c in chunks]
with torch.inference_mode():
embeddings = model.encode(
texts,
batch_size=batch_size,
show_progress_bar=False,
normalize_embeddings=True,
convert_to_numpy=True,
)
passage_index.add_chunks(chunks, embeddings)
for chunk, vec in zip(chunks, embeddings):
c_lang = chunk.source_lang.lower()
if c_lang not in centroid_sums:
centroid_sums[c_lang] = np.zeros(passage_index.dim, dtype=np.float64)
centroid_counts[c_lang] = 0
centroid_sums[c_lang] += vec.astype(np.float64, copy=False)
centroid_counts[c_lang] += 1
lang_total += len(chunks)
batch_records.clear()
passage_counts[lang] = lang_total
grand_total += lang_total
elapsed = time.time() - lang_start
logger.info("Finished language '%s': %d passages in %.1fs (%.1f passages/sec).", lang, lang_total, elapsed, lang_total / max(0.1, elapsed))
passage_index.save(output_dir)
num_passages = len(passage_index.chunks)
logger.info("Saved 'passage_native' index to %s (total passages: %d, size on disk: ~%.1f MB)",
output_dir, num_passages, (num_passages * 384 * 4) / (1024 * 1024))
# Save Centroids
centroids = {}
for lang, count in centroid_counts.items():
if count > 0:
c_vec = centroid_sums[lang] / count
c_norm = np.linalg.norm(c_vec)
if c_norm > 0:
c_vec = c_vec / c_norm
centroids[lang] = c_vec.tolist()
with open(output_dir / "centroids.json", "w", encoding="utf-8") as f:
json.dump(centroids, f, ensure_ascii=False, indent=2)
logger.info("Saved corpus centroids to %s", output_dir / "centroids.json")
# 2. Index Long-Document Corpus
logger.info(">>> Indexing Multi-Paragraph Long Documents...")
longdoc_index = StrategyVectorIndex("semantic_longdoc")
longdoc_counts: Dict[str, int] = {}
for lang in config.LANGUAGES:
longdoc_file = config.PROCESSED_DATA_DIR / f"{lang}_longdocs.jsonl"
if not longdoc_file.exists():
continue
pending_chunks: List[Chunk] = []
with open(longdoc_file, "r", encoding="utf-8") as f:
for line in f:
if not line.strip():
continue
try:
record = json.loads(line.strip())
except json.JSONDecodeError:
continue
pending_chunks.extend(process_longdocs_sentence_window([record]))
pending_chunks.extend(process_longdocs_semantic([record]))
if pending_chunks:
texts = [f"{config.PASSAGE_PREFIX}{c.embed_text.strip()}" for c in pending_chunks]
with torch.inference_mode():
embeddings = model.encode(
texts,
batch_size=batch_size,
show_progress_bar=False,
normalize_embeddings=True,
convert_to_numpy=True,
)
longdoc_index.add_chunks(pending_chunks, embeddings)
longdoc_counts[lang] = len(pending_chunks)
logger.info("Indexed %d longdoc chunks for '%s'.", len(pending_chunks), lang)
longdoc_index.save(output_dir)
num_longdocs = len(longdoc_index.chunks)
logger.info("Saved 'semantic_longdoc' index to %s (total size: %d chunks)", output_dir, num_longdocs)
# 3. Write Manifest
manifest = {
"languages": list(config.LANGUAGES),
"full_corpus": True,
"passage_counts": passage_counts,
"longdoc_counts": longdoc_counts,
"embedding_model": config.EMBEDDING_MODEL_NAME,
"built_at": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
}
with open(output_dir / "index_manifest.json", "w", encoding="utf-8") as f:
json.dump(manifest, f, ensure_ascii=False, indent=2)
logger.info("Saved index manifest to %s", output_dir / "index_manifest.json")
total_elapsed = time.time() - total_start
logger.info("================================================================================")
logger.info("=== FULL GPU INDEXING COMPLETED: %d passages + %d longdocs in %.1fs (%.1f min) ===",
grand_total, num_longdocs, total_elapsed, total_elapsed / 60)
logger.info("================================================================================")
if __name__ == "__main__":
build_indexes_gpu(batch_size=512)
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