import logging
from langchain_community.vectorstores.chroma import Chroma

from src.logging import logging_info

from .BaseDB import BaseDB

# TODO 数据库持久化 和 用户进入的加载。


class ChromaDB(BaseDB):
    def __init__(self, embedding_name: str = None, persist_dir=None) -> None:
        super().__init__(embedding_name, persist_dir)
        # logging_info(self.embedding)

    def init_db(self):
        self.client = Chroma(
            persist_directory=self.persist_dir, embedding_function=self.embedding
        )

    def addStories(self, stories: str, metas: dict = None):
        logging_info(self.text_splitter(stories)[-1])

        split_stories = self.text_splitter(stories)

        self.client.add_texts(
            texts=split_stories, metadatas=[metas] * len(split_stories)
        )

    def searchBySim(
        self, query, n_results=5, metas: dict = None, only_return_document=True
    ):
        result = self.client.similarity_search(query, k=n_results, filter=metas)

        # print(result)

        if only_return_document:
            return [i.page_content for i in result]

        return result

    def deleteStoriesByMeta(self, metas):
        ids = self.searchByMeta(metas=metas)["ids"]
        if ids:
            self.client.delete(ids)
            

    def searchByMeta(self, metas=None, include: list[str] = None) -> dict[str, any]:
        return self.client.get(where=metas, include=include)