| import argparse | |
| import lancedb | |
| import torch | |
| import pyarrow as pa | |
| import pandas as pd | |
| from pathlib import Path | |
| import tqdm | |
| import numpy as np | |
| import logging | |
| from transformers import AutoConfig | |
| from sentence_transformers import SentenceTransformer | |
| logging.basicConfig(level=logging.INFO) | |
| logger = logging.getLogger(__name__) | |
| def main(): | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--emb-model", help="embedding model name on HF hub", type=str) | |
| parser.add_argument("--table", help="table name in DB", type=str) | |
| parser.add_argument("--input-dir", help="input directory with documents to ingest", type=str) | |
| parser.add_argument("--vec-column", help="vector column name in the table", type=str, default="vector") | |
| parser.add_argument("--text-column", help="text column name in the table", type=str, default="text") | |
| parser.add_argument("--db-loc", help="database location", type=str, | |
| default=str(Path().resolve() / "gradio_app" / ".lancedb")) | |
| parser.add_argument("--batch-size", help="batch size for embedding model", type=int, default=32) | |
| parser.add_argument("--num-partitions", help="number of partitions for index", type=int, default=256) | |
| parser.add_argument("--num-sub-vectors", help="number of sub-vectors for index", type=int, default=96) | |
| args = parser.parse_args() | |
| emb_config = AutoConfig.from_pretrained(args.emb_model) | |
| emb_dimension = emb_config.hidden_size | |
| assert emb_dimension % args.num_sub_vectors == 0, \ | |
| "Embedding size must be divisible by the num of sub vectors" | |
| model = SentenceTransformer(args.emb_model) | |
| model.eval() | |
| if torch.backends.mps.is_available(): | |
| device = "mps" | |
| elif torch.cuda.is_available(): | |
| device = "cuda" | |
| else: | |
| device = "cpu" | |
| logger.info(f"using {str(device)} device") | |
| db = lancedb.connect(args.db_loc) | |
| schema = pa.schema( | |
| [ | |
| pa.field(args.vec_column, pa.list_(pa.float32(), emb_dimension)), | |
| pa.field(args.text_column, pa.string()) | |
| ] | |
| ) | |
| tbl = db.create_table(args.table, schema=schema, mode="overwrite") | |
| input_dir = Path(args.input_dir) | |
| files = list(input_dir.rglob("*")) | |
| sentences = [] | |
| for file in files: | |
| with open(file) as f: | |
| sentences.append(f.read()) | |
| for i in tqdm.tqdm(range(0, int(np.ceil(len(sentences) / args.batch_size)))): | |
| try: | |
| batch = [sent for sent in sentences[i * args.batch_size:(i + 1) * args.batch_size] if len(sent) > 0] | |
| encoded = model.encode(batch, normalize_embeddings=True, device=device) | |
| encoded = [list(vec) for vec in encoded] | |
| df = pd.DataFrame({ | |
| args.vec_column: encoded, | |
| args.text_column: batch | |
| }) | |
| tbl.add(df) | |
| except: | |
| logger.info(f"batch {i} was skipped") | |
| ''' | |
| create ivf-pd index https://lancedb.github.io/lancedb/ann_indexes/ | |
| with the size of the transformer docs, index is not really needed | |
| but we'll do it for demonstrational purposes | |
| ''' | |
| tbl.create_index( | |
| num_partitions=args.num_partitions, | |
| num_sub_vectors=args.num_sub_vectors, | |
| vector_column_name=args.vec_column | |
| ) | |
| if __name__ == "__main__": | |
| main() |