aquilu
commented
11 months ago 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