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Check this and I believe this will give you more clarity.
Design your Copilot workflow:
Define input & output: Specify how your Copilot will access the PDF (database query or direct upload) and the desired format of the generated Word document.
Information extraction: Design the logic for extracting relevant information from the PDF. Leverage Copilot's text analysis capabilities and consider using libraries like PyMuPDF or PDFQuery for complex document parsing.
Word document generation: Define how the extracted information will be used to populate the Word template. Utilize Copilot's document generation skills and consider libraries like Docxtemplater for templating.
Training and iteration:
Train your Copilot: Feed your prepared data into Copilot Studio for training. This helps it learn the relationships between your PDFs, extracted information, and Word templates.
Refine and iterate: Evaluate the generated Word documents for accuracy and adjust your Copilot workflow or training data as needed. This iterative process is crucial for improving performance.
Additional Tips:
Start simple: Begin with basic information extraction and document generation tasks before adding complexity.
Leverage Copilot resources: Utilize Copilot Studio's documentation, tutorials, and community support for guidance.
Consider alternatives: Explore pre-built Copilots for information extraction or document generation that might suit your needs with minimal customization.
Remember, creating a custom Copilot requires technical expertise and effort. However, with careful planning, data preparation, and iterative training, you can achieve your desired outcome of automated PDF-to-Word document generation with customized information extraction.
I hope this short overview helps! Feel free to check about Microsoft Copilot in Word or visit O365CloudExperts.com.
