from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnableLambda, RunnablePassthrough
from langchain_core.vectorstores import InMemoryVectorStore


class KeywordEmbeddings(Embeddings):
    vocabulary = ("checkout", "billing", "password")

    def _embed(self, text: str) -> list[float]:
        lower_text = text.lower()
        return [1.0 if term in lower_text else 0.0 for term in self.vocabulary]

    def embed_documents(self, texts: list[str]) -> list[list[float]]:
        return [self._embed(text) for text in texts]

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