- 🎯 喜欢python、transformers、nlp、pytorch
yuanzhoulvpi2017 / documentsearch Goto Github PK
View Code? Open in Web Editor NEW基于sentence transformers和chatglm实现的文档搜索工具
License: Apache License 2.0
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brightxiaohan sam6513 guiyinan ai-awe davidyuan666 iamleon121 2447380040 itsharex xiangya123 bailinsong litanlitudan suifei neverstoplearn yuz998 cqwppbb 5l1v3r1 zsysuper snomycdocumentsearch's Issues
RuntimeError: CUDA error: invalid device ordinal
作者你好，我在colab上运行项目的时候出现了这样的问题，看代码中的cuda指定的是0也没有指定多个GPU。我不知道是什么原因导致的这个问题。
RuntimeError: CUDA error: invalid device ordinal
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be
incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Compile with TORCH_USE_CUDA_DSA to enable device-side assertions.
搜尋文檔格式支援
您好，想請問目前支援 pdf 和 docx 外的格式嗎？
像 pandas.Dataframe(), JSON or text 之類的？
謝謝🙏
想要增加知识库识别的准确率，是需要加大chunk还是减少
有的时候无法正确回答是不是因为一个chunk的信息不够导致的，谢谢
运行 web_ui 报错
大佬帮忙看看，执行命令 streamlit run web_ui.py --server.fileWatcherType none --server.port 8080，项目启动后，提问时报错：
2023-04-27 11:40:16.159 Uncaught app exception
Traceback (most recent call last):
File "/root/anaconda3/envs/ds/lib/python3.9/site-packages/streamlit/runtime/scriptrunner/script_runner.py", line 565, in _run_script
exec(code, module.__dict__)
File "/root/DocumentSearch/web_ui.py", line 36, in <module>
output_str, output_df = kl.search_result(input_str)
File "/root/DocumentSearch/demo.py", line 252, in search_result
search_table_info = pd.concat(
File "/root/anaconda3/envs/ds/lib/python3.9/site-packages/pandas/util/_decorators.py", line 331, in wrapper
return func(*args, **kwargs)
File "/root/anaconda3/envs/ds/lib/python3.9/site-packages/pandas/core/reshape/concat.py", line 368, in concat
op = _Concatenator(
File "/root/anaconda3/envs/ds/lib/python3.9/site-packages/pandas/core/reshape/concat.py", line 425, in __init__
raise ValueError("No objects to concatenate")
ValueError: No objects to concatenate
效果探索
这种 和 直接用BM25检索本地的知识文档，效果有什么区别吗，，感觉这种就是稍微带点生成，
embedding
为什么 我换成 sber的取embedding 效果差很多，
知识库和问题的向量化方式不同
知识库中的文件内容的Embedding是用chinese-roberta-wwm-ext向量化模型做的
而输入问题的Embedding是用THUDM/chatglm-6b对话大模型做的
两者之间计算相似度合理吗?为什么不统一用一个模型做?
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