wiseflow/test

Test Script Documentation

Web Content Fetching and Parsing

fetching_for_sample.py

• Before using, edit the sites and save_dir variables in the script to specify target websites and save directory

Extract Focus Content or Related Links from Parsed Web Content [Multiple Models Can Be Tested Simultaneously]

get_info_test.py

• Before using, edit the sample_dir variable in the script to specify the directory of parsed web content (generally the directory saved by fetching_for_sample.py, which must contain parsed web content including at least text.txt file and link_dict.json file)

• Create focus point descriptions for test tasks, refer to reports/wiseflow_report_20241223_bigbrother666/task0/focus_point.json

  Note: Sample files for different focus points need to be placed in different folders, refer to the structure in reports/wiseflow_report_20241223_bigbrother666

• Before using, edit the models and vl_model variables in the script to specify models to test

To modify get_info prompts, edit prompts.py. For providers not using OpenAI SDK data format, edit openai_wrapper.py

Result Submission and Sharing

Wiseflow is an open source project aiming to create an "information crawling tool for everyone" through collective contributions!

At this stage, submitting test results is equivalent to submitting project code - you'll be accepted as a contributor and may even be invited to participate in commercial projects!

Test results should be submitted to the reports directory. Create a subdirectory for each test named {test_content}_{test_date}_{tester}, for example:

mkdir -p reports/wiseflow_report_20241223_bigbrother666

Please submit all test samples and the original output results of the program run, and create a README.md file in the directory to record the test content, test date, tester, test models, conclusions, statistical tables, etc.

Finally, edit the reports/README.md file, add the directory name of the test result to the index, so that others can view it.