mirror of https://github.com/hpcaitech/ColossalAI
63 lines
2.9 KiB
Python
63 lines
2.9 KiB
Python
import os
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from colossalqa.retrieval_conversation_universal import UniversalRetrievalConversation
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def test_en_retrievalQA():
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data_path_en = os.environ.get('TEST_DATA_PATH_EN')
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data_path_zh = os.environ.get('TEST_DATA_PATH_ZH')
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en_model_path = os.environ.get('EN_MODEL_PATH')
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zh_model_path = os.environ.get('ZH_MODEL_PATH')
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zh_model_name = os.environ.get('ZH_MODEL_NAME')
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en_model_name = os.environ.get('EN_MODEL_NAME')
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sql_file_path = os.environ.get('SQL_FILE_PATH')
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qa_session = UniversalRetrievalConversation(files_en=[{
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'data_path': data_path_en,
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'name': 'company information',
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'separator': '\n'
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}],
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files_zh=[{
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'data_path': data_path_zh,
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'name': 'company information',
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'separator': '\n'
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}],
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zh_model_path=zh_model_path,
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en_model_path=en_model_path,
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zh_model_name=zh_model_name,
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en_model_name=en_model_name,
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sql_file_path=sql_file_path)
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ans = qa_session.run("which company runs business in hotel industry?", which_language='en')
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print(ans)
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def test_zh_retrievalQA():
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data_path_en = os.environ.get('TEST_DATA_PATH_EN')
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data_path_zh = os.environ.get('TEST_DATA_PATH_ZH')
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en_model_path = os.environ.get('EN_MODEL_PATH')
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zh_model_path = os.environ.get('ZH_MODEL_PATH')
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zh_model_name = os.environ.get('ZH_MODEL_NAME')
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en_model_name = os.environ.get('EN_MODEL_NAME')
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sql_file_path = os.environ.get('SQL_FILE_PATH')
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qa_session = UniversalRetrievalConversation(files_en=[{
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'data_path': data_path_en,
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'name': 'company information',
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'separator': '\n'
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}],
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files_zh=[{
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'data_path': data_path_zh,
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'name': 'company information',
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'separator': '\n'
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}],
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zh_model_path=zh_model_path,
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en_model_path=en_model_path,
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zh_model_name=zh_model_name,
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en_model_name=en_model_name,
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sql_file_path=sql_file_path)
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ans = qa_session.run("哪家公司在经营酒店业务?", which_language='zh')
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print(ans)
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if __name__ == "__main__":
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test_en_retrievalQA()
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test_zh_retrievalQA()
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