The Challenge
Tackling an interesting problem: given a user query, search through a PDF document and provide feedback on how well the query aligns with the document's content.
The Solution
The approach is a three-step process: Load & Index, Search & RAG, and Feedback Generation.
Load & Index
First, I need to understand the PDF document. I do this by creating a "semantic index". It's like creating a map of the document, but instead of landmarks, we have vectors.
Search & RAG
Next, I take your query and find the most related parts in the PDF document. This is where RAG (Retrieval Augmentation Generation) comes in. It's like giving the system a cheat sheet before the big test.
Feedback Generation
Finally, I generate feedback for you. This isn't just a simple "yes" or "no". I provide detailed feedback with references from the PDF document. It's like having footnotes for your query.
Dive Deeper
The code is open for you to explore. Feel free to fork it, and see how it fits your use case. I'll break down each section and highlight key points.
If you have any suggestions or improvements, don't hesitate to share. For more technical details, continue reading along this Jupyter notebook.
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