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