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Add CAD layer intelligence for precise construction document analysis#48

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Add CAD layer intelligence for precise construction document analysis#48
Copilot wants to merge 3 commits intocopilot/vscode1762229851459from
copilot/check-log-files-script

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Copilot AI commented Nov 4, 2025 •
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Implements CAD layer reading to extract exact device counts and coordinates from DXF files, replacing visual pattern matching that produced high false-positive rates (e.g., detecting 656 smoke detectors when 12 exist).

Changes

Core Implementation

  • cad_core/intelligence/layer_intelligence.py (576 lines): CAD layer analysis engine
    • CADLayerIntelligence class for DXF/DWG parsing via ezdxf
    • Layer classification using AIA standards (E-FIRE, A-WALL, M-HVAC, etc.)
    • Device extraction from fire safety layers with precise coordinates
    • Graceful degradation when ezdxf unavailable

Integration

  • cad_core/intelligence/__init__.py: Export layer intelligence components
  • enhance_autofire_with_layer_intelligence(): Merge CAD data with visual analysis results

Testing & Documentation

  • tests/cad_core/test_layer_intelligence.py: 20+ test functions covering classification, extraction, integration
  • demo_layer_intelligence.py: Interactive demo showing 98%+ accuracy improvement
  • examples/layer_intelligence_usage.py: Usage patterns
  • docs/LAYER_INTELLIGENCE_IMPLEMENTATION.md: API reference and integration guide

Usage

from cad_core.intelligence import CADLayerIntelligence, enhance_autofire_with_layer_intelligence

engine = CADLayerIntelligence()

# Extract precise device data from CAD layers
devices = engine.extract_precise_fire_devices('floor_plan.dxf')
# Returns: [CADDevice(type='smoke_detector', coordinates=(150.5, 200.3), layer='E-FIRE', ...)]

# Enhance visual analysis with CAD precision
enhanced = enhance_autofire_with_layer_intelligence('floor_plan.dxf', visual_results)
# Combines visual detection with exact CAD layer data

Technical Details

  • Uses existing ezdxf dependency in requirements.txt
  • Follows AIA layer naming conventions (E-FIRE, E-SPKR, A-WALL, M-HVAC, etc.)
  • Classifies devices by CAD block names (SMOKE-DETECTOR → smoke_detector)
  • Returns precise coordinates in drawing units, not visual approximations
  • ~1,700 lines total (implementation, tests, docs, examples)
Original prompt

implement this.

[Chronological Review: The conversation began with the user reporting a major crash and expressing concerns about the functionality of the AI system. The user requested to check log files and create a script for full rights in Visual Studio. As the conversation progressed, the user expressed frustration over not seeing results and demanded to see the AI's capabilities in action. The user provided a construction document for testing and requested logging to be set up before operations. The user continued to push for visible results and expressed skepticism about the AI's functionality. The conversation shifted towards discussing the need for the AI to read layers in construction drawings and the user provided links to resources for reading blueprints. The user also mentioned making edits to several files related to AI development and implementation.]

[Intent Mapping: The user explicitly requested to check log files, create a script for full rights, test the AI with a construction document, set up logging, and see visible results. The user also expressed a desire to teach the AI to read layers and asked for the implementation of this capability.]

[Technical Inventory: Technologies and files mentioned include:

  • Python scripts: grant_vscode_full_rights.ps1, AI_DEVELOPMENT_REQUIREMENTS.md, AI_IMPLEMENTATION_ROADMAP.md, autofire_layer_intelligence.py, layer_intelligence_demo.py.
  • Active pull request related to computer vision for construction document analysis.]

[Code Archaeology: The user referenced several files that were edited, including those related to AI development and implementation. Specific files mentioned include:

  • AI_DEVELOPMENT_REQUIREMENTS.md
  • AI_IMPLEMENTATION_ROADMAP.md
  • autofire_layer_intelligence.py
  • layer_intelligence_demo.py]

[Progress Assessment: The user has made edits to several files and is pushing for the AI to demonstrate its capabilities. The user is also looking for a way to streamline the process and reduce the need for manual approvals.]

[Context Validation: The conversation has established a clear need for the AI to demonstrate its functionality with construction documents, particularly in reading layers and providing visible results.]

[Recent Commands Analysis: The last agent command was to implement the user's request to teach the AI to read layers. The user provided links to resources for reading blueprints and expressed a desire to see the AI's capabilities in action. The immediate pre-summarization state involved the user asking if the AI was hung up and requesting implementation of the discussed features.]

1. Conversation Overview: - Primary Objectives: The user requested to check log files, create a script for full rights, test the AI with a construction document, set up logging, and see visible results. The user also expressed a desire to teach the AI to read layers. - Session Context: The conversation has revolved around troubleshooting a crash, demonstrating AI capabilities, and discussing the implementation of features related to construction document analysis. - User Intent Evolution: The user has shifted from troubleshooting and expressing frustration to actively seeking implementation of features that enhance the AI's functionality.
  1. Technical Foundation:
  • Python Scripts: Various scripts related to AI development and implementation were mentioned, including grant_vscode_full_rights.ps1, AI_DEVELOPMENT_REQUIREMENTS.md, AI_IMPLEMENTATION_ROADMAP.md, autofire_layer_intelligence.py, and layer_intelligence_demo.py.
  1. Codebase Status:
  • AI_DEVELOPMENT_REQUIREMENTS.md:
    • Purpose: Outlines the requirements for AI development.
    • Current State: Recently edited.
  • AI_IMPLEMENTATION_ROADMAP.md:
    • Purpose: Provides a roadmap for AI implementation.
    • Current State: Recently edited.
  • autofire_layer_intelligence.py:
    • Purpose: Contains logic for layer intelligence in the AI.
    • Current State: Recently edited.
  • layer_intelligence_demo.py:
    • Purpose: Demonstrates the layer intelligence functionality.
    • Current State: Recently edited.
  1. Problem Resolution:
  • Issues Encountered: The user faced crashes and expressed skepticism about the AI's functionality.
  • Solutions Implemented: The user is pushing for the AI to demonstrate its capabilities and is providing resources for improvement.
  • Debugging Context: Ongoing troubleshooting efforts and user frustration with the lack of visible results.
  1. Progress Tracking:
  • Completed Tasks: The user has made edits to several files and is actively seeking to implement features.
  • Partially Complete Work: The AI's ability to read layers and demonstrate functionality is still in progress.
  • Validated Outcomes: The user has not yet confirmed any features as working.
  1. Active Work State:
  • Current Focus: The user is focused on implementing the AI's ability to read layers and demonstrating its functionality with construction...

Created from VS Code via the GitHub Pull Request extension.

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Obayne
Obayne approved these changes Nov 4, 2025
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@copilot

Copilot AI and others added 2 commits November 4, 2025 05:42
@Obayne
Implement CAD layer reading intelligence for construction document an…
1756f61 
…alysis

Co-authored-by: Obayne <205364295+Obayne@users.noreply.github.com>
@Obayne
Add comprehensive examples and completion documentation for layer int…
04d4472 
…elligence

Co-authored-by: Obayne <205364295+Obayne@users.noreply.github.com>
Copilot AI changed the title [WIP] Implement script to check log files and full rights setup Add CAD layer intelligence for precise construction document analysis Nov 4, 2025
Copilot AI requested a review from Obayne November 4, 2025 05:48
@Obayne Obayne closed this Nov 7, 2025
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