SLIM modeling workflow from llmware is recommended:
- Select a specialized model: e.g.
llmware/slim-extractPre-training for financial fields such as amounts, dates, etc. - Configure the parsing template: define the output JSON structure (e.g.
{'amount':'','date':'','vendor':''}) - Batch processing of documents: by
prompter.prompt_mainSimultaneous processing of multiple invoices/statements - Post-processing of results: check logical consistency using built-in validation rules. Practical examples show that the program processes 500 pages of financial statements in only 15 minutes, with an accuracy rate of 98%
This answer comes from the articlellmware: an open source framework for rapidly building enterprise-class RAG applicationsThe
May not be reproduced without permission:AI productivity tools " What are practical solutions to optimize the accuracy and efficiency of financial data extraction?
