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#!/usr/bin/env python
# -*- coding:utf-8 _*-
"""
@author:quincy qiang
@license: Apache Licence
@file: create_knowledge.py
@time: 2023/04/18
@contact: yanqiangmiffy@gamil.com
@software: PyCharm
@description: - emoji：https://emojixd.com/pocket/science
"""
import os
import pandas as pd
from langchain.schema import Document
from langchain.document_loaders import UnstructuredFileLoader
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from tqdm import tqdm
# 中文Wikipedia数据导入示例：
embedding_model_name = '/root/pretrained_models/text2vec-large-chinese'
docs_path = '/root/GoMall/Knowledge-ChatGLM/cache/financial_research_reports'
embeddings = HuggingFaceEmbeddings(model_name=embedding_model_name)
# Wikipedia数据处理
# docs = []
# with open('docs/zh_wikipedia/zhwiki.sim.utf8', 'r', encoding='utf-8') as f:
# for idx, line in tqdm(enumerate(f.readlines())):
# metadata = {"source": f'doc_id_{idx}'}
# docs.append(Document(page_content=line.strip(), metadata=metadata))
#
# vector_store = FAISS.from_documents(docs, embeddings)
# vector_store.save_local('cache/zh_wikipedia/')
docs = []
with open('cache/zh_wikipedia/wiki.zh-sim-cleaned.txt', 'r', encoding='utf-8') as f:
for idx, line in tqdm(enumerate(f.readlines())):
metadata = {"source": f'doc_id_{idx}'}
docs.append(Document(page_content=line.strip(), metadata=metadata))
vector_store = FAISS.from_documents(docs, embeddings)
vector_store.save_local('cache/zh_wikipedia/')
# 金融研报数据处理
# docs = []
#
# for doc in tqdm(os.listdir(docs_path)):
# if doc.endswith('.txt'):
# # print(doc)
# loader = UnstructuredFileLoader(f'{docs_path}/{doc}', mode="elements")
# doc = loader.load()
# docs.extend(doc)
# vector_store = FAISS.from_documents(docs, embeddings)
# vector_store.save_local('cache/financial_research_reports')
# 英雄联盟
docs = []
lol_df = pd.read_csv('cache/lol/champions.csv')
# lol_df.columns = ['id', '英雄简称', '英雄全称', '出生地', '人物属性', '英雄类别', '英雄故事']
print(lol_df)
for idx, row in lol_df.iterrows():
metadata = {"source": f'doc_id_{idx}'}
text = ' '.join(row.values)
# for col in ['英雄简称', '英雄全称', '出生地', '人物属性', '英雄类别', '英雄故事']:
# text += row[col]
docs.append(Document(page_content=text, metadata=metadata))
vector_store = FAISS.from_documents(docs, embeddings)
vector_store.save_local('cache/lol/')
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