# -*- coding: utf-8 -*-
# @Author: privacy
# @Date:   2024-06-11 13:43:14
# @Last Modified by:   privacy
# @Last Modified time: 2024-12-06 17:00:50

import hashlib
from typing import List, Optional

import chromadb
# from chromadb.api.types import Documnets, EmbeddingFunction, Embeddings


# class MyEmbeddingFunction(EmbeddingFunction):
#     def __call__(self, texts: documents) -> Embeddings:
#         embeddings = [model.encode(x) for x in texts]
#         return embeddings

class VectorClient:
    def __init__(self):
        super().__init__()

    @classmethod
    def add(cls, pages_content: List[dict], project_name: Optional[str], file_name: Optional[str]) -> None:
        """
        将文件的每页正文转换为向量数据

        Args:
            pages_content: 每页的正文内容
            file_name: 文件名

        Returns:
            None
        """
        col_name = project_name + '_' + file_name
        name = hashlib.md5(col_name.encode(encoding="utf-8")).hexdigest()

        # 初始化 Chroma 客户端，连接到服务器
        # client = await chromadb.AsyncHttpClient(host='localhost', port=50000)
        client = chromadb.HttpClient(host='localhost', port=50000)

        # 使用集合（Collections）创建集合
        # collection = await client..get_or_create_collection(
        collection = client.get_or_create_collection(
            name=name,
            # embedding_function=emb_fn
        )

        # 向集合添加数据
        for index, item in enumerate(pages_content):
            # await collection.add(
            collection.add(
                documents=[
                    item.get('text')
                ],
                metadatas=[
                    {"page_number": item.get('page_number')}
                ],
                ids=[
                    str(index)
                ],
                # embeddings=[
                #     [1.1, 2.3, 3.2]
                # ]
            )

    @classmethod
    def query(cls, project_name: Optional[str], file_name: Optional[str], query: List[str] = ["资质条件"], contains: Optional[str] = None):
        """
        查询向量数据库

        Args:
            pages_content: 每页的正文内容
            file_name: 文件名

        Returns:
            None
        """
        col_name = project_name + '_' + file_name
        name = hashlib.md5(col_name.encode(encoding="utf-8")).hexdigest()

        # 初始化 Chroma 客户端，连接到服务器
        # client = await chromadb.AsyncHttpClient(host='localhost', port=50000)
        client = chromadb.HttpClient(host='localhost', port=50000)

        # collection = await client.get_collection(
        collection = client.get_collection(
            name=name,
            # embedding_function=emb_fn
        )

        if contains:

            # 查询集合
            # results = await collection.query(
            results = collection.query(
                query_texts=query,
                n_results=5,
                # include=[],
                # where = {"metadata_field": "is_equal_to_this"},
                where_document={"$contains": contains}
            )

        else:
            # results = await collection.query(
            results = collection.query(
                query_texts=query,
                n_results=5
            )

        return results


if __name__ == '__main__':
    import os
    import json
    import asyncio
    from glob import glob
    from pprint import pprint

    base_dir = 'D:\\desktop\\三峡水利\\data\\projects'
    base_dir = 'D:\\desktop\\三峡水利\\data\\0预审查初审详审测试数据'

    proj_name = '三峡左岸及地下电站地坪整治'
    proj_name = '【231100334287】500kV交流GIS GIL固体绝缘沿面失效机理研究及综合诊断装备研制采购程序文件'

    for supplierdir in glob(os.path.join(os.path.join(base_dir, proj_name), '*')):
        for file in glob(os.path.join(supplierdir, '*-content.json')):
            supplier = file.split('\\')[-2]

            # with open(file, 'r', encoding='utf-8') as fp:
            #     content = json.load(fp)

            # # asyncio.run(insert(pages_content = content, project_name=proj_name, file_name=supplier))

            # asyncio.run(
            #     search(
            #         project_name='三峡左岸及地下电站地坪整治',
            #         file_name='湖北建新建设工程有限公司',
            #         query=['财务审计报告'],
            #         contains='2021年'
            #     )
            # )

            # VectorClient.add(
            #     pages_content=content,
            #     project_name=proj_name,
            #     file_name=supplier
            # )

            # r = VectorClient.query(
            #     project_name=proj_name,
            #     file_name=supplier,
            #     # query=['查询设备特性及性能组织'],
            #     query=['证书'],
            #     # contains='设备性能'
            #     contains='二级'
            # )
            # pprint(r)

    # r = VectorClient.query(
    #     project_name=proj_name,
    #     file_name='supplier',
    #     query=['证书'],
    #     contains='营业执照'
    # )
    # pprint(r)