mixtape2025-1

UniK3D project (https://github.com/lpiccinelli-eth/UniK3D) converted to ONNX. Also upgraded the Gradio interface to generate 3D meshes. Enjoy!

Thanks! I liked the implementation so much that I have made a python version here https://github.com/Topping1/probability-spreadsheet

First, apologies because I used ChatGPT to draft this text. First time poster here.

I’ve developed a lightweight Python-based solution that lets you turn your personal PDF library into a searchable knowledge base—no complex setup, no bloated dependencies.

What It Does: 1. Extracts text from PDFs and saves it as JSON files, chunked by pages. 2. Optionally generates text embeddings (using `fastembed`) for semantic search. 3. Provides a user-friendly GUI for searching, navigating results, and previewing matching content.

The search interface supports: - BM25 Algorithm: Great for keyword-based search. - Simple Text Search: For exact phrase matching. - Embedding-Based Search (optional): If you want semantic search capabilities, embeddings are supported but not required.

Why This Project? I wanted something simple, portable, and fast for managing my own PDF archives—something that doesn’t require setting up a database or maintaining a server. The focus is on the search interface simplicity and usefulness.

With this tool: - Your knowledge base is stored as portable JSON and EMB files. You can easily move it across devices. - The GUI allows for intuitive searches and even opens matching pages in your default PDF viewer (Okular by default, but configurable).

Key Features: - Minimal Dependencies: Works out of the box with BM25 and a small set of Python libraries. - Optional Semantic Search: Add `fastembed` for enhanced search capabilities. - Customizable Workflow: JSON-only mode for simplicity or JSON+EMB for full functionality. - Cross-Platform Support: As long as Python and your chosen PDF viewer are installed.

Use Cases: - Researchers managing collections of academic papers. - Anyone needing quick access to information buried in PDFs.

How to Get Started: 1. Install Python and the required libraries. 2. Process your PDFs into JSON files with a single script. 3. (Optional) Generate embeddings for richer search results. 4. Use the search interface to start finding what you need!

Check out the code and detailed instructions here: https://github.com/Topping1/BM25-PDF-Search

I hope this is useful for the community.