from sentence_transformers import SentenceTransformer, util

model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")

documents = [
    {"id": "doc-001", "text": "Set up billing alerts for monthly cloud spending."},
    {"id": "doc-002", "text": "Reset expired password links from the account security page."},
    {"id": "doc-003", "text": "Rotate SSH keys for production deployment hosts."},
    {"id": "doc-004", "text": "Renew TLS certificates before the web server reload."},
    {"id": "doc-005", "text": "Export customer invoices from the finance dashboard."},
]
query = "password reset link expired"

corpus = [item["text"] for item in documents]
corpus_embeddings = model.encode_document(
    corpus,
    convert_to_tensor=True,
    normalize_embeddings=True,
    show_progress_bar=False,
)
query_embedding = model.encode_query(
    query,
    convert_to_tensor=True,
    normalize_embeddings=True,
    show_progress_bar=False,
)

hits = util.semantic_search(query_embedding, corpus_embeddings, top_k=3)[0]

print(f"Corpus embeddings: {tuple(corpus_embeddings.shape)}")
print(f"Query: {query}")
for rank, hit in enumerate(hits, start=1):
    item = documents[hit["corpus_id"]]
    print(f"{rank}. {item['id']} score={hit['score']:.4f} text={item['text']}")

top_item = documents[hits[0]["corpus_id"]]
if top_item["id"] == "doc-002":
    print("Semantic search check: pass")