Autogenerate annotations for evals with tarsier+LLM #37
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asim-shrestha
approved these changes
Dec 8, 2023
Comment on lines
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| async def main(): | ||
| openai_client = OpenAI() | ||
| ocr_service = GoogleVisionOCRService() | ||
| tarsier_client = Tarsier(ocr_service) | ||
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| urls = [ | ||
| "https://www.skadden.com/professionals/l/lanstra-allen-l", | ||
| "https://www.sullcrom.com/Lawyers/Erik-D-Lindauer", | ||
| "https://www.winston.com/en/professionals/padmanabhan-krishnan", | ||
| "https://www.mwe.com/people/michael-abercrombie/", | ||
| ] | ||
| schema = get_fetch_schema("attorney").model_fields | ||
| metadata = { | ||
| "category": "legal", | ||
| "subcategory": "attorney_details", | ||
| "fetch_id": "attorney", | ||
| } | ||
| await add_examples_to_json(urls, schema, metadata, tarsier_client, openai_client) | ||
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| # if __name__ == "__main__": | ||
| # asyncio.run(main()) |
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Could also make this a CLI tool
Co-authored-by: Asim Shrestha <50181239+asim-shrestha@users.noreply.github.com>
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Also added schemas for attorney and lawyer job posting data
TODO: determine if and how to integrate these abilities with
bananalyzeCLI