Learning from ChatGPT

#4029 Framework to Build User-Specified Knowledge Graphs #4029  Describe a Python/LLM framework using Google Gemini to develop a knowledge graph of any user-specified subject. Each pass should grow the tree by one level. Knowledge should come from both Gemini and trusted sources of the web To develop a knowledge graph that grows level-by-level using Google Gemini, you can implement a Breadth-First Expansion (BFE) framework. This architecture uses Gemini’s Google Search Tool to ensure that every new node is grounded in real-time web data as well as the model’s internal reasoning. Framework Architecture: "Recursive Semantic Expansion" This framework treats the knowledge graph as a tree (or directed graph) where the user provides a "Seed Subject" (Level 0). Each "pass" identifies the sub-topics or related entities of the current leaves and attaches them as the next level (Level n+1 ). Core Components Component Responsibility Orchestrator Manages the queue o...

#4028 Laser Etching Acrylic Sheets #4028  What are the various ways acrylic sheets can be etched by laser to produce useful optical results? Laser etching acrylic is a sophisticated way to manipulate light, transforming a transparent sheet into a functional optical component.  By strategically damaging the surface or interior of the material, you create sites for scattering , refraction , and reflection . The following techniques are the primary ways to achieve useful optical results: 1. Light Guide Panels (LGP) and Dot Matrix Patterns This is the most "industrial" optical application of laser etching. It is used to turn a thin acrylic sheet into a uniform backlight (like those in laptop screens or slim lightboxes). The Mechanism: Light is injected into the edge of the acrylic.  Because of Total Internal Reflection (TIR) , the light bounces between the top and bottom surfaces without escaping. The Etch: You laser-etch a grid of "micro-dots" or lines.   These dots ...

#4027 Python REGEX Generator #4027  Once I have used Docling to convert a PDF file to a text file, including tables, how can I use Python to identify useful regex to extract data? Since you are using Docling , you likely have a relatively clean Markdown or JSON representation of your PDF. Moving from raw text to structured data via Regex (Regular Expressions) can be tedious, but you can use Python to "mine" the text for patterns and then validate them. Here is a workflow to identify and refine Regex patterns for your data extraction. 1. Pattern Discovery with re.finditer Instead of guessing patterns, use Python to scan for common anchors (like "Table 1" or specific NASA SWE codes) and look at the context around them. import re # Load your Docling output with open ( "converted_doc.txt" , "r" ) as f: content = f.read() # Strategy: Find a known anchor and see what the surrounding text looks like # Example: Looking for requirement IDs like...