🏗️ ClaudeHopper - AI-Powered Construction Document Assistant

ClaudeHopper is a specialized Model Context Protocol (MCP) server that enables Claude and other LLMs to interact directly with construction documents, drawings, and specifications through advanced RAG (Retrieval-Augmented Generation) and hybrid search. Ask questions about your construction drawings, locate specific details, and analyze technical specifications with ease.

✨ Features

• 🔍 Vector-based search for construction document retrieval optimized for CAD drawings, plans, and specs

• 🖼️ Visual search to find similar drawings based on textual descriptions

• 🏢 Specialized metadata extraction for construction industry document formats

• 📊 Efficient token usage through intelligent document chunking and categorization

• 🔒 Security through local document storage and processing

• 📈 Support for various drawing types and construction disciplines (Structural, Civil, Architectural, etc.)

🚀 Quick Start

Prerequisites

• Node.js 18+

• Ollama for local AI models

  • Required models: nomic-embed-text, phi4, clip

• Claude Desktop App

• For image extraction: Poppler Utils (pdfimages command)

One-Click Setup

1. Download ClaudeHopper

2. Run the setup script:

This will:

• Create the necessary directory structure

• Install required AI models

• Process your construction documents

• Configure the Claude Desktop App to use ClaudeHopper

Adding Documents

Place your construction documents in these folders:

• Drawings: ~/Desktop/PDFdrawings-MCP/InputDocs/Drawings/

• Specifications: ~/Desktop/PDFdrawings-MCP/InputDocs/TextDocs/

After adding documents, run:

🏗️ Using ClaudeHopper with Claude

Try these example questions in the Claude Desktop App:

🛠️ Technical Architecture

ClaudeHopper uses a multi-stage pipeline for processing construction documents:

1. Document Analysis: PDF documents are analyzed for structure and content type

2. Metadata Extraction: AI-assisted extraction of project information, drawing types, disciplines

3. Content Chunking: Intelligent splitting of documents to maintain context

4. Image Extraction: Identification and extraction of drawing images from PDFs

5. Vector Embedding: Creation of semantic representations for text and images

6. Database Storage: Local LanceDB storage for vector search capabilities

👀 Testing the Image Search

To test the image search functionality, you can use the provided test script:

This will:

• Build the application

• Check for required dependencies (like pdfimages)

• Seed the database with images from your drawings directory

• Run a series of test queries against the image search

You can also run individual test commands:

📝 Available Search Tools

ClaudeHopper provides several specialized search capabilities:

• catalog_search: Find documents by project, discipline, drawing type, etc.

• chunks_search: Locate specific content within documents

• all_chunks_search: Search across the entire document collection

• image_search: Find drawings based on visual similarity to textual descriptions

Examples of using the image search feature can be found in the image_search_examples.md file.

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.