import sys
import json
from core import embed_room as ap
from config.embed import MODEL, N_THREADS, N_CTX, DEFAULT_PREFIX
from llama_cpp import Llama

def embed_run(args):

    if not MODEL.is_file():
        print(f"Model not found: {MODEL}. Download it first (see README).", file=sys.stderr)
        return

    prefix = args.prefix if args.prefix is not None else DEFAULT_PREFIX

    print("Loading model...")
    llm = Llama(
        model_path = str(MODEL),
        n_ctx      = N_CTX,
        n_threads  = N_THREADS,
        embedding  = True,
        verbose    = False,
    )
    print("Model ready!")

    inputs  = [prefix + t for t in args.texts]

    resp    = llm.create_embedding(inputs)
    vectors = [d["embedding"] for d in resp["data"]]

    out = [
        {"text": text, "dim": len(vec), "embedding": vec}
        for text, vec in zip(args.texts, vectors)
    ]
    print(json.dumps(out, ensure_ascii=False, indent=2))

if __name__ == "__main__":
    embed_run( ap.parser.parse_args() )