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* Initial commit of AutoGenBench * wording * typo * pre-commit reformulation * Updated README to point to contributor's guide earlier. * Simplified the description of the JSON format. * Added print statements to indicate when run.sh and scenario.py are starting. * Added SocietyOfMind scenario to GAIA. * Pointing autogenbench clone command to the latest branch. * Temporarily disable subsample option. * Updated the GAIA readme to specify how to define a BING API key. * Fixed and re-enabled the subsample option. * Added a draft of a blog post. * Updated authors. * Incorporating Gagan's feedback. * Fixed code formatting. * Updated the help string in the docs. * Light editing of the AutoGenBench blogpost. * Support filtering on model tags. * Added websurfer dependencies to Dockerfile. * Renamed testbed -> autogenbench * Attempting to fix formatting. * Added more gracefull handling of task timeouts (the script is allowed to terminate before Docker is stopped). * Updated the blogpost based on Saleema's and Julia's feedback. * Fixed formatting... again. * Added a main MANIFEST to list available scenarios. * Limit main manifest to directories. * Manifests now use relative paths. * All manifests are now relative. * Updated the contributing guide, and address windows path issues. * Updated the version. Fixed formatting. * Fixed formatting. * De-listing Examples, since it has no clear tabulate criteria. * Updated email in pyproject * typo in blogpost * wording --------- Co-authored-by: Qingyun Wu <qingyun.wu@psu.edu> Co-authored-by: Qingyun Wu <qingyun0327@gmail.com>
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1.2 KiB
JSON
2 lines
1.2 KiB
JSON
{ "id": "nvda_tsla_stocks", "template": "../Templates/ThreeAgents", "substitutions": { "scenario.py": { "__PROMPT__": "Plot and save to disk a chart of NVDA and TESLA stock price YTD.", "__SELECTION_METHOD__": "auto", "__3RD_AGENT_NAME__": "visualization_critic", "__3RD_AGENT_PROMPT__": "A student of Edward Tufte, you are an expert in information design, and will provide helpful critiques of visualizations. As you prepare your critiques, please consider the following dimensions:\n- Are there bugs, logic errors, syntax error or typos in the visualization code? Are there any reasons why the code may fail to run? How should it be fixed?\n- Is the data transformed appropriately for the visualization type? E.g., is the dataset appropriated filtered, aggregated, or grouped if needed? If a date field is used, is the date field first converted to a date object etc?\n- How well does the code meet the specified visualization goals?\n- CONSIDERING BEST PRACTICES, is the visualization type appropriate for the data and intent? Is there a visualization type that would be more effective in conveying insights? \n- Are the aesthetics of the visualization appropriate for the visualization type and the data?" } } }
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