from time import monotonic, sleep

from qdrant_client import QdrantClient, models
from sentence_transformers import SentenceTransformer


qdrant_url = "http://localhost:6333"
collection_name = "sentence_transformers_support_docs"
expected_points = 512
query = "How can I reset a forgotten account password?"

corpus = [
    {
        "doc_id": "doc-000",
        "title": "Reset a forgotten password",
        "text": "Reset a forgotten account password from account settings and confirm the recovery email.",
    }
]
corpus.extend(
    {
        "doc_id": f"doc-{point_id:03d}",
        "title": f"Archive retention record {point_id:03d}",
        "text": (
            f"Archive record {point_id:03d} describes retention schedules, "
            "storage labels, and routine document review."
        ),
    }
    for point_id in range(1, expected_points)
)

model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
document_embeddings = model.encode_document(
    [item["text"] for item in corpus],
    normalize_embeddings=True,
    convert_to_numpy=True,
    show_progress_bar=False,
)
vector_size = document_embeddings.shape[1]