from llama_index.core.embeddings import BaseEmbedding


class KeywordEmbedding(BaseEmbedding):
    def _vector(self, text: str) -> list[float]:
        terms = [
            "billing",
            "escalation",
            "inventory",
            "restart",
            "refund",
            "dashboard",
            "ticket",
            "runbook",
        ]
        lowered = text.lower()
        return [1.0 if term in lowered else 0.0 for term in terms]