from pathlib import Path

import faiss
from llama_index.core import (
    Document,
    StorageContext,
    VectorStoreIndex,
    load_index_from_storage,
)
from llama_index.core.embeddings import BaseEmbedding
from llama_index.vector_stores.faiss import FaissVectorStore


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

    def _get_text_embedding(self, text: str) -> list[float]:
        return self._vector(text)

    def _get_query_embedding(self, query: str) -> list[float]:
        return self._vector(query)

    async def _aget_query_embedding(self, query: str) -> list[float]:
        return self._vector(query)