/MSDS_453

Primary LanguagePython

MSDS_453

Document Processing Pipeline

Our implementation utilized a multi-stage processing approach for the document corpus:

  1. Text Extraction and Normalization

    • PDF processing using pdfplumber for consistent text extraction
    • Standardized document formatting with metadata preservation
    • Quality control achieving 97% successful extraction rate

  2. Document Chunking Strategy

    • 512-token segments with 20-token overlaps
    • Semantic coherence maintenance through sentence-level boundaries
    • Optimized for context preservation and retrieval efficiency

  3. Embedding Generation

    • TF-IDF vectorization for term importance weighting
    • Cluster analysis for document relationship mapping
    • Dimensional reduction for content relationship visualization

Knowledge Organization

Our system employs multiple classification approaches to optimize information retrieval:

  1. Hierarchical Ontology

    • Software systems domain (75.9% coverage)
    • Project management processes (73.4% coverage)
    • Document types classification (67.1% coverage)
    • Safety compliance procedures (27.8% coverage)
    • Business processes (29.1% coverage)

  2. Functional Clustering

    • Eight distinct document communities identified through k-means clustering
    • Natural language processing for topic modeling
    • Cross-reference validation through MDS and t-SNE analysis

Query Processing System

The implementation features a sophisticated query handling approach:

  1. Query Understanding

    • Classification system for retrieval necessity
    • Domain-specific terminology recognition
    • Context-aware query interpretation

  2. Retrieval Mechanism

    • Hybrid retrieval combining:
      • Sparse retrieval (BM25) for keyword matching
      • Dense retrieval for semantic understanding
    • Document reranking using monoT5 for relevance optimization

  3. Response Generation

    • Context integration from relevant documents
    • Domain-specific knowledge grounding
    • Response validation against source material

LLM Integration

The system leverages LLaMA 3 with specific optimizations:

  1. Model Configuration

    • 4-bit quantization for efficient processing
    • Memory optimization (6GB GPU allocation)
    • Response generation parameters:
      • Maximum 50 tokens per response
      • Temperature: 0.7
      • Top-p: 0.9

  2. Performance Optimization

    • KV-cache implementation
    • Beam search optimization
    • GPU memory management

  3. Integration Features

    • Document grounding for accurate responses
    • Cross-reference capability across document clusters
    • Dynamic context window management

Implementation Considerations

Technical Constraints

  1. Hardware Optimization

    • RTX 2000 Ada GPU utilization
    • Memory management strategies
    • Processing pipeline efficiency

  2. Document Handling

    • PDF format standardization
    • Metadata preservation
    • Term relationships maintenance

System Adaptability

  1. Domain Knowledge

    • Construction industry terminology integration
    • System-specific command recognition
    • Cross-platform process understanding

  2. Scalability Features

    • Modular architecture for future expansion
    • Framework for multimodal integration
    • Extensible knowledge base structure