Bitweaver - Advanced Document Handling

Created by: Lester Caine, Last modification: 13 September 2025

Source: Conversation with AI Assistant, DuckDuckGo Privacy Layer, September 13, 2025
This is a pair of replies and they need pulling into a single summary, but convert my current thinking into something that I can build on. Simply getting the many thousand magazines I have into a more usable archive has been a longterm target.

Firebird Database Architecture for Document Management

Component Implementation Advantages
Database Firebird Robust, lightweight, proven performance
Storage Strategy Metadata + Raw Text in DB Efficient indexing and retrieval
File Storage File System Practical separation of binary content
Web Access Bitweaver Ubiquitous content viewing

Database Design Insights

Firebird Strengths

  • Lightweight and performant relational database
  • Excellent for structured document metadata
  • Strong indexing capabilities
  • Low resource overhead

Storage Architecture

  • Store metadata and extracted text in Firebird
  • Keep original files in file system
  • Create robust indexing for quick retrieval
  • Maintain referential integrity between metadata and files

NoSQL vs. Relational Considerations

Your skepticism about NoSQL is well-founded. While NoSQL databases claim flexibility, they often:

  • Lack strong typing
  • Require additional indexing layers
  • Can become complex for structured document management
  • Sacrifice ACID compliance

Bitweaver Integration

Bitweaver provides:

  • Cross-platform content access
  • Flexible web-based document management
  • Potential for custom metadata views
  • Unified interface for your document ecosystem

Potential Enhancements

  1. Text Extraction Improvements

    • Implement advanced parsing (Apache Tika)
    • Generate rich metadata
    • Create full-text search capabilities

  2. AI Integration Points

    • Embed document embeddings
    • Store model-generated metadata
    • Create semantic search capabilities

PDF Processing and Local AI Integration

Component Current Status Potential Improvement
OCR Tool ocrmypdf (Limited segmentation) Advanced document parsing
AI Model Framework Llama.cpp with CUDA support Customized local knowledge base
GPU Acceleration GTX 1070 CUDA compatibility Suitable for lightweight models

PDF Processing Challenges

Current Limitations

  • ocrmypdf struggles with complex document segmentation
  • Lack of intelligent article/content separation
  • Limited metadata extraction

Potential Solutions

Document Processing Tools

  1. Advanced PDF Parsing Options:
    • Apache Tika: Robust document parsing
    • PyMuPDF: Sophisticated PDF extraction
    • Tesseract OCR with advanced segmentation plugins

AI-Assisted Document Processing

  • Use machine learning models for:
    • Intelligent document segmentation
    • Metadata extraction
    • Content classification

Llama.cpp and CUDA Integration

Implementation Considerations

  • GTX 1070 is compatible with CUDA acceleration
  • Supports lightweight model inference
  • Relatively straightforward setup process

Recommended Workflow

  1. Document Ingestion

    • Use advanced PDF parsing tools
    • Extract structured content
    • Generate clean, segmented text

  2. Knowledge Base Creation

    • Convert parsed documents to embeddings
    • Store in vector database (e.g., Chroma, Faiss)
    • Create retrieval-augmented generation (RAG) system

  3. Local AI Model