from txtai.embeddings import Embeddings


def create_and_index(
    data: list[str], model: str = "sentence-transformers/all-MiniLM-L6-v2"
) -> Embeddings:
    """Create and index embeddings from text."""
    embeddings = Embeddings({
        "path": model,
        "content": True,
        "hybrid": True,
        "scoring": "bm25",
    })
    embeddings.index(data)
    return embeddings


def query_embedding(
    embeddings: Embeddings, query: str, limit: int = 100
) -> list[str]:
    """Search embeddings and return matching texts."""
    results = embeddings.search(query, limit)
    return [r["text"] for r in results]