Open saineshwar opened 1 month ago
And the issue is ...?
And the issue is ...?
Choose Table as the parsing template, then I've got the above result. Forget about the Chinese characters got by LLMs.
One issue is that, the embedding process is pretty long. Since there are 1000 lines in the csv file, and each line will be sent to embedding model. We'll improve such strategy to enlarge the batch for Table
like data.
Choose Table as the parsing template, then I've got the above result. Forget about the Chinese characters got by LLMs. One issue is that, the embedding process is pretty long. Since there are 1000 lines in the csv file, and each line will be sent to embedding model. We'll improve such strategy to enlarge the batch for
Table
like data.
Which embedding and LLM have you used for testing it.
It does not relate to the LLM/embedding closely. The key issue is you should choose Table
instead of default General
as the file parser template
It does not relate to the LLM/embedding closely. The key issue is you should choose
Table
instead of defaultGeneral
as the file parser template
Know i have chosen table only.
You could try to adjust the prompt. By default, demo adopts deepseek.
You could try to adjust the prompt. By default, demo adopts deepseek.
@yingfeng any example can you share please. I am using llama 3.1 as LLM
Describe your problem
Output
Data in .csv file
Download file to test - customers-1000.csv