📄 AS-IS SYSTEM WORKFLOW DOCUMENTATION

This document captures the current architecture of the long-document classification and information extraction system, as extracted from code_summary.txt. It includes:

An overview of the as-is pipeline
A detailed text-based component diagram
A Component–Responsibility–Collaborator (CRC) table
Notes for managing risk-aware incremental upgrades
A discussion on handling extremely long documents with modern architectures

🧹 OVERVIEW OF CURRENT WORKFLOW

The system processes legal and compliance documents (often PDFs) to classify them, extract relevant named entities, and detect the presence of digital or visual signatures. It is built in modular components using both traditional ML and deep learning, relying on caching, multiprocessing, and format-specific processing pipelines.

🔗 TEXT-BASED WORKFLOW DIAGRAM

                           ┌────────────────────────────┐
                           │    Input PDF Files     │
                           └─────────────────────────┘
                                        │
                                [1] PDF LOADING
                                        │
                ┌───────────────────────────────────────┐
                │                                                │
     ┌────────▼───────────┐                         ┌───────────────────────┐
     │ OCR Pipeline       │                         │ Digital Signature Check │
     │ (tesserocr +       │                         │ (PDF bytes + regex)     │
     │  pdf2texts)        │                         └─────────────────────┘
     └──────────────────────┘                                      │
                │                                                ▼
       ┌─────▼────────────┐                         ┌───────────────────────┐
       │ Chunking        │                         │ Visual Signature Detector │
       │ (semchunk)      │                         │ (signitractor ONNX)      │
       └────────────┘                         └─────────────────────┘
              │                                                  ▼
       ┌────▼───────────────┐                         ┌────────────────────┐
       │ Embedding Module │                         │  Signature Classifier  │
       │ (RoBERTa, TF-IDF)│                         │  (signitector LGBM)    │
       └────────────────────┘                         └─────────────────┘
              │                                                                
       ┌────▼────────────────────┐                                            
       │ Chunk Classification     │                                            
       │ (RoBERTa + LSTM fusion)  │                                            
       └────────────────────┘                                            
              │                                                                
       ┌────▼────────────┐                                                   
       │ Entity Extraction │                                                   
       │ (TokenClassifier) │                                                   
       └─────────────┘                                                   
                │                                                              
           ┌───▼───┐                                                         
           │  Cache  │                                                         
           │ + Persist│                                                        
           └───────┘                                                         

📋 COMPONENT–RESPONSIBILITY–COLLABORATOR (CRC) TABLE

Component	Responsibilities	Collaborators
PDF Loader	Load PDF files, manage byte-level access	Signature Checker
OCR Pipeline	Convert scanned or image-based PDFs into text using Tesseract	Chunker, Visual Signature Detector
Digital Sig. Check	Check for cryptographic digital signatures in PDF structure	PDF Loader
Visual Sig. Detect	Detect presence of visual signature bounding boxes using ONNX model	OCR, Signature Classifier
Signature Classifier	Classify detected visual regions as true/false signatures	Visual Detector
Chunker	Split full text into semantically coherent chunks (e.g. ~512 tokens)	OCR, Embedding Module
Embedding Module	Generate chunk-level representations using RoBERTa, TF-IDF	Chunker, Classifier
Chunk Classifier	Classify document using embeddings + LSTM fusion	Embedding, Cache
Entity Extractor	Named entity recognition using RoBERTa-style token classification	Chunker
Cache Manager	Cache all intermediate outputs on disk for efficiency and fault tolerance	All major components

📌 RISK-MANAGED UPGRADE GUIDANCE (by Component)

Upgrade Target	Risk Level	Recommendation
PDF Loader + OCR	Medium	Replace with unified MLLM-based model (e.g., Donut, MMDocReader)
Signature Detection	High	Maintain for now, refactor to MLLM only after testing
Chunking + Embedding	Medium	Replace with long-context model inference over entire doc
Chunk Classifier	Low	Easy to refactor into FusionNet-style or MoE classifier
Entity Extractor	Low	Migrate to InstructNER or few-shot NER
Caching Framework	Low	Can retain as-is; decouples stability from model upgrades

♻️ HANDLING EXTREMELY LONG DOCUMENTS (MODERN SOLUTIONS)

To address long (potentially infinite) document lengths that exceed standard transformer context windows, modern 2025-era models offer viable alternatives:

✅ Long-Context Transformers

Use foundation models like:

Claude 3 Opus, Command R+, GPT-4.5, Mistral-7B-Long, or Phi-3-Long
Native context lengths from 32K to 256K+ tokens

Benefits:

Eliminate manual chunking
Preserve cross-section dependencies in classification
Allow direct classification, extraction, or summarization across the full document

✅ Recurrent Memory-Augmented Models

If full sequence input isn’t feasible:

Use RetNet, RWKV, or Memorizing Transformers with chunkwise recurrence

✅ Compression-Aware Planning

Hybrid techniques:

Use semantic hashing or symbolic sketching to reduce context
Augment with retrieval-based augmentation over the entire doc corpus

✅ Agentic Refactor with Memory

The existing agentic orchestrator can manage:

Long-document streaming (via a memory-augmented plan)
Caching intermediate judgments
Chunk-based voting with justification replay

These strategies allow you to shift from token-limited classification to full-document, multi-pass comprehension and labeling.

✅ Summary

This modular architecture is highly swappable, meaning a gradual migration to newer models can be achieved without full system disruption. The first candidates for upgrade include:

Long-context embedding + classifier to replace chunk+LSTM
Replacing OCR + chunker with layout-aware transformer
Swapping entity extraction with instruction-tuned few-shot LLM-based NER

By combining modern long-context architectures with your agentic wrapping approach, the system can evolve into a robust, streaming-friendly platform for industrial-scale document intelligence.