wehub-resource-sync
35 lines
1.9 KiB
Python
35 lines
1.9 KiB
Python
# Copyright 2023 The Qwen team, Alibaba Group. All rights reserved.
|
||
#
|
||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||
# you may not use this file except in compliance with the License.
|
||
# You may obtain a copy of the License at
|
||
#
|
||
# http://www.apache.org/licenses/LICENSE-2.0
|
||
#
|
||
# Unless required by applicable law or agreed to in writing, software
|
||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||
# See the License for the specific language governing permissions and
|
||
# limitations under the License.
|
||
|
||
from qwen_agent.tools import VectorSearch
|
||
|
||
|
||
def test_vector_search():
|
||
tool = VectorSearch()
|
||
doc = ('主要序列转导模型基于复杂的循环或卷积神经网络，包括编码器和解码器。性能最好的模型还通过注意力机制连接编码器和解码器。'
|
||
'我们提出了一种新的简单网络架构——Transformer，它完全基于注意力机制，完全不需要递归和卷积。对两个机器翻译任务的实验表明，'
|
||
'这些模型在质量上非常出色，同时具有更高的并行性，并且需要的训练时间显着减少。'
|
||
'我们的模型在 WMT 2014 英语到德语翻译任务中取得了 28.4 BLEU，比现有的最佳结果（包括集成）提高了 2 BLEU 以上。'
|
||
'在 WMT 2014 英法翻译任务中，我们的模型在 8 个 GPU 上训练 3.5 天后，建立了新的单模型最先进 BLEU 分数 41.0，'
|
||
'这只是最佳模型训练成本的一小部分文献中的模型。')
|
||
res = tool.call({'query': '这个模型要训练多久？'}, docs=[doc], max_ref_token=100)
|
||
print(res)
|
||
|
||
res = tool.call({'query': '这个模型要训练多久？'}, docs=[doc.split('。')], max_ref_token=100)
|
||
print(res)
|
||
|
||
|
||
if __name__ == '__main__':
|
||
test_vector_search()
|