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Action Plan for Integrating PDF Processing in BlindChat
1. Text Extraction from PDF
Use a client-side library to extract text from PDF files. This ensures that the PDF content never leaves the user's device.
JavaScript-based libraries like pdf.js are recommended for this task.
2. Vectorization and Embedding Creation
Once the text has been extracted, use a natural language processing model to convert the text into embeddings.
For this, one can use TensorFlow.js and pretrained models available on the platform, or consider using specific libraries that offer embeddings in the browser.
It's crucial to consider model efficiency and size to ensure a good user experience.
3. Integration with BlindChat
Add an option in BlindChat for users to upload PDF files.
Process the PDF and convert the extracted text into embeddings.
Use these embeddings based on BlindChat functionalities. For example, for searching relevant information, comparing embeddings, etc.
4. Privacy and Security Considerations
Ensure that all processing is done on the client-side to maintain privacy.
Do not permanently store embeddings or text in the browser to ensure there are no security risks.
Add a button on the Chat UI to load PDFs on the client side for future RAG integration
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