File size: 1,405 Bytes
f459a9c | 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 | from __future__ import annotations
import argparse
import json
from pathlib import Path
from sentence_transformers import SentenceTransformer
MODEL_TITLE = "LumynaX Embed E5 Mistral 7B"
def _build_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser(description=f"Generate dense embeddings with {MODEL_TITLE}.")
parser.add_argument("texts", nargs="*", help="Text inputs to embed.")
parser.add_argument("--prompt-name", default="web_search_query", help="SentenceTransformer prompt preset.")
parser.add_argument("--max-seq-length", type=int, default=4096)
return parser
def main() -> None:
args = _build_parser().parse_args()
texts = args.texts or ["LumynaX packages local models for retrieval."]
model_dir = Path(__file__).resolve().parent / "merged_model"
model = SentenceTransformer(str(model_dir))
model.max_seq_length = args.max_seq_length
embeddings = model.encode(
texts,
prompt_name=args.prompt_name or None,
)
print(
json.dumps(
{
"model_title": MODEL_TITLE,
"count": len(texts),
"embedding_dim": len(embeddings[0]),
"embeddings": embeddings.tolist(),
},
ensure_ascii=False,
indent=2,
)
)
if __name__ == "__main__":
main()
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