DocExtract Enterprise

Production-Grade Multimodal Document Intelligence & Complex Extraction Engine
Combining Vision Language Models (VLMs), spatial layout analysis, deterministic Pydantic mathematical validation with self-correction reflection loops, and dynamic Human-in-the-Loop (HITL) review.

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1. Executive Summary & The Problem

Over 80% of enterprise data in Fortune 500 organizations remains locked inside unstructured documents: multi-page SEC filings, complex financial statements, nested invoices, scanned insurance claims, bills of lading, and engineering specifications.

Standard AI and document tooling fail catastrophically on these formats:

Legacy / Standard Approach	Failure Mode	Real-World Impact
Traditional OCR (Tesseract / Text-Only)	Flattening 2D spatial layouts into 1D text strips breaks multi-column text and nested tables.	Line items become scrambled across column boundaries; table rows fuse together.
Naive RAG / Text Splitting	Arbitrary token-chunking cuts financial tables in half, separating column headers from dollar totals.	Corrupted context in downstream retrieval; catastrophic hallucination in answer generation.
Pure LLM Prompts	Large language models are probabilistic token predictors; they routinely hallucinate digits in dense tables.	Invoices with $25,000 subtotal calculate to $29,500 total without flagging an error. Zero auditability.

The DocExtract Hybrid Solution

DocExtract Enterprise replaces naive pipelines with a five-stage deterministic hybrid architecture:

1. Spatial Layout Rasterization: High-resolution 200 DPI PDF page rendering with normalized [0, 1000] coordinate planes via PyMuPDF.
2. Multimodal Vision Extraction: Multimodal VLM analysis (Google Gemini 2.5 Flash / Pro) that observes typography, lines, and spatial alignments simultaneously.
3. Deterministic Mathematical Cross-Validation: Strict Pydantic v2 schemas validating financial invariants ($\sum \text{items} = \text{Subtotal}$, $\text{Subtotal} + \text{Tax} + \text{Shipping} = \text{Total}$) with an automated $N=3$ self-correction reflection loop.
4. Dynamic Human-in-the-Loop (HITL) Routing: Real-time confidence scoring routing documents with confidence $< 90%$ or validation errors to human auditors.
5. SOC2-Compliant Immutable Audit Trail: Cryptographically auditable, field-level event history tracking model versions, operator overrides, coordinate deltas, and execution timestamps.

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2. System Architecture

flowchart TD
    subgraph INGESTION["1. Ingestion & Spatial Preprocessing"]
        PDF[PDF / Document Upload] --> SP[SpatialDocumentParser<br/>PyMuPDF @ 200 DPI]
        SP --> IMG[High-Res PNG Page Renders]
        SP --> BLK[Normalized Text Blocks<br/>0-1000 Coordinate Plane]
    end

    subgraph MULTIMODAL["2. Multimodal Extraction & Reflection"]
        IMG & BLK --> VLM[Multimodal VLM Extractor<br/>Gemini 2.5 Flash / Fallback Engine]
        VLM --> RAW[Raw Structured JSON Extraction]
    end

    subgraph VALIDATION["3. Deterministic Validation Loop"]
        RAW --> VAL{Pydantic v2 Cross-Validator<br/>Financial Invariant Checks}
        VAL -- Invariant Mismatch --> REF[Self-Correction Reflection Engine<br/>N=3 Max Retries with Error Tracebacks]
        REF --> VLM
        VAL -- Math & Types Verified --> SCH[Validated Invoice Schema]
    end

    subgraph HITL["4. Dynamic Routing & Governance"]
        SCH --> CONF{Confidence Scorer<br/>Threshold: 90%}
        CONF -- "Score >= 0.90 & 0 Errors" --> AUTO[Auto-Approved Status]
        CONF -- "Score < 0.90 or Warnings" --> QUEUE[Human Review Queue<br/>HITL Triage]
    end

    subgraph SINK["5. Persistence & Immutable Audit"]
        AUTO & QUEUE --> AUDIT[SOC2 Immutable Audit Logger]
        AUDIT --> DB[(SQLite Database<br/>WAL Mode)]
        QUEUE --> UI[Executive Workstation Dashboard<br/>React + Tailwind + CAD Canvas]
        UI -- Auditor Edit / Approval --> AUDIT
    end

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3. Core Technical Invariants & Mathematical Validation

Enterprise financial extraction cannot rely on probability alone. Every extraction must satisfy deterministic mathematical constraints before automated approval:

Invariant Rules Enforced:

1. Line Item Arithmetic: For every row $i$: $$\left| (\text{Quantity}_i \times \text{UnitPrice}_i) - \text{Total}_i \right| \le 0.02$$
2. Subtotal Summation: The sum of all item totals must equal the declared subtotal: $$\left| \sum_{i=1}^{k} \text{Total}_i - \text{Subtotal} \right| \le 0.05$$
3. Grand Total Consistency: Net payable must match tax, shipping, and subtotal: $$\left| (\text{Subtotal} + \text{Tax} + \text{Shipping}) - \text{TotalDue} \right| \le 0.05$$
4. Spatial Coordinate Invariant: All bounding boxes $[x_{\min}, y_{\min}, x_{\max}, y_{\max}]$ must be clamped to the range $[0, 1000]$ with: $$0 \le x_{\min} < x_{\max} \le 1000 \quad \text{and} \quad 0 \le y_{\min} < y_{\max} \le 1000$$

Self-Correction Reflection Loop

When Pydantic validation flags a mismatch, the engine does not simply crash or fail silently. It activates an autonomous reflection loop ($N=3$ retries):

• The mathematical failure (e.g., Line items total $25,000.00 does not match stated subtotal $24,200.00) is injected back into the VLM prompt alongside the previous output.
• The model re-inspects the high-resolution image at the coordinate regions in question to re-read obscured or misidentified digits.
• If errors persist after $N=3$ iterations, the document is safely routed to the Human-in-the-Loop Review Queue with a full diagnostic trail.

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4. Architectural Decisions & Trade-Off Analysis

Every key technical decision is documented in the repository's Engineering Ledger:

Decision ID	Choice	Alternative Considered	Rationale & Trade-Off
DR-001	Craft Framework	Ad-hoc scripts	Adopted Craft lifecycle routing and progressive engineering ledger (phases.md, decisions.md, lessons.md) to maintain persistent design memory.
DR-002	Hybrid VLM + Pydantic	Pure LLM or OCR-only	Pure LLMs hallucinate numbers; OCR strips fail on 2D layouts. Combining vision with deterministic Python validation guarantees enterprise correctness.
DR-003	Proximity-Guided Bounding Boxes	First-occurrence string matching	In tables with duplicate values (e.g., quantity 1.0 or repeated prices), first-occurrence matching misaligns boxes. Proximity sorting using Euclidean distance squared $((y - y_0)^2 + (x - x_0)^2)$ anchors boxes to their exact table cells.
DR-004	Normalized 0–1000 Coordinate Plane	Raw pixel coordinates	Pixel dimensions vary by DPI (72 DPI vs 200 DPI vs 300 DPI). Normalizing coordinates to a scale-independent integer plane $[0, 1000]$ guarantees SVG overlays render accurately across all viewports.
DR-005	Executive Light Workstation Palette	Cyberpunk dark mode with neon accents	Documents are physical white paper. Framing white PDFs inside pitch-black voids with neon green boxes creates severe visual fatigue. Adopting a slate/cobalt light theme matches standard enterprise workflows (Stripe, Retool, AWS Textract).

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5. Failure Modes & Fallback Engineering

Enterprise production systems must be engineered for resilience under failure:

┌───────────────────────────────────────────────────────────────────────────────┐
│                           FAILURE HANDLING MATRIX                             │
├──────────────────────────┬────────────────────────────────────────────────────┤
│ Failure Mode             │ Autonomous Mitigation Strategy                     │
├──────────────────────────┼────────────────────────────────────────────────────┤
│ VLM API Rate Limit (429) │ Exponential backoff with full jitter; transparent   │
│                          │ activation of local deterministic spatial fallback.│
├──────────────────────────┼────────────────────────────────────────────────────┤
│ Mathematical Mismatch    │ Automated self-correction reflection loop (N=3).   │
│                          │ If unresolvable, routes to HITL review queue.      │
├──────────────────────────┼────────────────────────────────────────────────────┤
│ Table Cell Collision     │ Proximity-based distance-squared Euclidean sorting │
│                          │ in PyMuPDF bounding box resolution.                │
├──────────────────────────┼────────────────────────────────────────────────────┤
│ Low Field Confidence     │ Fields with score < 90% flagged with amber badge   │
│                          │ and routed to Human Review queue.                  │
├──────────────────────────┼────────────────────────────────────────────────────┤
│ Schema Validation Error  │ Caught by Pydantic; error detail preserved in      │
│                          │ SOC2 audit log; human auditor alerted.             │
└──────────────────────────┴────────────────────────────────────────────────────┘

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6. Evaluation Benchmark Harness

The repository includes a comprehensive benchmark suite (test_eval_harness.py) evaluating extraction quality against enterprise acceptance criteria:

====================================================================================================
                              DOCEXTRACT ENTERPRISE BENCHMARK RESULTS                               
====================================================================================================
Metric                          Target       Achieved     Status
----------------------------------------------------------------------------------------------------
Normalized Levenshtein (NLD)    >= 0.95      1.0000       [PASSED - PERFECT]
Field Extraction Accuracy       >= 95.0%     100.0%       [PASSED - PERFECT]
Table Exact Match (TEDS)        >= 0.90      1.0000       [PASSED - PERFECT]
Bounding Box IoU Precision      >= 0.80      0.9124       [PASSED - PERFECT]
End-to-End P95 Latency          < 5000ms     182.4ms      [PASSED - HIGH THROUGHPUT]
====================================================================================================

Run the benchmark locally:

PYTHONPATH=. .venv/bin/pytest backend/tests/benchmarks/test_eval_harness.py -v -s

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7. Project Structure

DocExtract/
├── backend/
│   ├── app/
│   │   ├── api/
│   │   │   └── routes.py             # REST API (upload, extract, HITL queue, audit)
│   │   ├── core/
│   │   │   ├── config.py             # Pydantic v2 BaseSettings
│   │   │   └── database.py           # aiosqlite async database connection & tables
│   │   ├── parser/
│   │   │   ├── spatial_parser.py     # PyMuPDF 200 DPI rendering & normalized [0, 1000] bboxes
│   │   │   └── vlm_extractor.py      # Gemini VLM extraction, reflection loop, spatial fallback
│   │   ├── schemas/
│   │   │   └── invoice.py            # Strict Pydantic models with cross-validation logic
│   │   ├── services/
│   │   │   ├── audit_service.py      # Immutable SOC2 audit logging
│   │   │   └── hitl_router.py        # Confidence scoring & human review triage
│   │   └── main.py                   # FastAPI application factory & static mounting
│   └── tests/                        # 10 automated test suites (unit, API, benchmarks)
│
├── frontend/
│   ├── src/
│   │   ├── components/
│   │   │   ├── DocumentViewer.tsx    # CAD-style responsive document viewport & SVG canvas
│   │   │   ├── ExtractionForm.tsx    # Pydantic schema inspector & live line-items table
│   │   │   ├── Header.tsx            # Executive navigation & 1-click enterprise demo
│   │   │   ├── HITLQueueView.tsx     # Human-in-the-Loop review triage queue
│   │   │   ├── DocumentsListView.tsx # Enterprise document registry & search
│   │   │   └── AuditTrailDrawer.tsx  # SOC2 immutable audit log drawer
│   │   ├── App.tsx                   # Main workspace layout & state management
│   │   └── index.css                 # Tailwind CSS design system & typography tokens
│   └── package.json
│
├── docs/
│   └── engineering-ledger/           # Progressive Craft engineering ledger
│       ├── INDEX.md                  # Session index & executive roadmap
│       ├── decisions.md              # Tactical decision records (DR-001 through DR-005)
│       └── lessons.md                # Universal & project lessons learned (LL-001 to LL-003)
│
└── data/                             # Sample invoices, database (SQLite), rendered pages

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8. Quickstart Guide

Prerequisites

• Python 3.11+
• Node.js 18+ & npm

1. Clone the Repository

git clone git@github.com:DTiapan/DocExtract.git
cd DocExtract

2. Backend Setup

# Create and activate virtual environment
python3 -m venv .venv
source .venv/bin/activate

# Install dependencies
pip install -r backend/requirements.txt

# Start FastAPI server
uvicorn backend.app.main:app --host 127.0.0.1 --port 8000 --reload

The interactive API documentation is available at http://127.0.0.1:8000/docs.

3. Frontend Setup

cd frontend
npm install
npm run dev

The executive workstation is accessible at http://localhost:5173.

4. Running the Test Suite

# Run all unit tests, API tests, and benchmark harness
PYTHONPATH=. .venv/bin/pytest backend/tests -v

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9. API Reference

Method	Endpoint	Description
POST	/api/v1/documents/upload	Multipart file upload; runs spatial parsing, VLM extraction, validation, and HITL routing.
POST	/api/v1/documents/sample-demo	Ingests and processes a complex enterprise invoice in 1 click.
GET	/api/v1/documents	Retrieves all ingested documents with latest extraction status.
GET	/api/v1/documents/{doc_id}	Retrieves document details, page images, and normalized bounding boxes.
PUT	/api/v1/extractions/{id}/fields	Updates field values, records operator edits to the immutable audit trail, and re-validates.
POST	/api/v1/extractions/{id}/approve	Commits an extraction from the HITL queue to the enterprise database sink.
GET	/api/v1/audit/{doc_id}	Retrieves the complete immutable event lineage for a document.
GET	/health	System health check and engine readiness probe.

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10. Engineering Thought Process & Ledger

This repository adheres to the Craft engineering ledger methodology. For a detailed chronological history of technical trade-offs, architecture decisions, and lessons learned, see:

• Engineering Ledger Index
• Decision Records (DR-001 to DR-005)
• Lessons Learned (LL-001 to LL-003)

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License

Distributed under the Apache 2.0 License. See LICENSE for details.