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SARIN S · @sarin

Ai Procurement Purchase Order Auditor

From github.com

I built the AI-Procurement-and-Purchase-Order-Auditor as an Edvexa Technical Assessment: a Python full-stack application for invoice validation and purchase-order reconciliation. I started and last updated the GitHub project in 2026-10. It accepts PDF and PNG/JPG invoices, extracts and validates their data, compares them with purchase orders, and routes them through a Submitter-to-Reviewer workflow with an audit trail.

I document several deployment limits: SQLite is adequate for simplicity, but I suggest PostgreSQL for production because of SQLite’s concurrency constraints. The upload endpoint has no hard file-size limit; I expect Nginx to handle that in production. The local JWT secret and database make the project single-machine only, with no multi-instance support. On this Windows system, App Control blocks PyMuPDF’s _mupdf.pyd, so PDF extraction falls back to pure-Python pypdf; PyMuPDF may work on systems without that restriction. Image OCR requires Tesseract to be installed separately, and image uploads fall back to empty extraction without it.

I use regex patterns rather than a trained ML model for invoice extraction, so accuracy depends on how consistent invoice formats are; the project documentation reports no measured extraction accuracy or benchmark results. The code separates PDF/image text extraction in backend/app/services/extractor.py, field validation in backend/app/services/validator.py, PO comparison in backend/app/services/matcher.py, and workflow state-machine behavior in backend/app/services/audit.py.

I keep the project’s AI-use disclosure in AI_USAGE.md. I created this project as a technical assessment for Edvexa, and the source describes all code as original work produced during the assessment period. My GitHub profile also lists the repository: my github profile.

  • Invoice extraction uses regex and pypdf for PDF text, with pytesseract OCR as a fallback for scanned images. PyMuPDF 1.25 was attempted but was DLL-blocked on this system; Tesseract is optional and needed for scanned-image support.

The React 18 and TypeScript frontend uses Tailwind CSS, React Router, Axios, Recharts, and lucide-react. It communicates over HTTP/REST with a FastAPI 0.115 backend on port 8000, using JWT bearer tokens; passwords are BCrypt-hashed. The API prefix is /api/v1, with routers for authentication, documents, purchase orders, reviews, and dashboard, plus /health. The backend uses SQLAlchemy 2.0, Alembic 1.14, Pydantic 2.x, and SQLite in procurement.db. Its seven entities are User, Document, ExtractedField, PurchaseOrder, PurchaseOrderItem, ReviewComment, and WorkflowHistory; user relationships include uploaded_by and reviewed_by.

I documented local setup with Python 3.11+ (tested on 3.12.1), Node.js 18+ (tested on 24.19.0), and Git. The backend setup creates a virtual environment, installs requirements.txt, copies .env.example to .env, runs Alembic migrations, seeds demo data, and starts Uvicorn on port 8000. The frontend installs dependencies and runs the Vite development server at http://localhost:5173. Seeding creates 2 users and 4 sample documents; python generate_samples.py generates the sample files.

# Backend, from the backend directory
python -m venv .venv
# Windows PowerShell: .venv\Scripts\Activate.ps1
# Linux/macOS: source .venv/bin/activate
pip install -r requirements.txt
# Windows: copy .env.example .env
# Linux/macOS: cp .env.example .env
alembic upgrade head
python seed.py
uvicorn app.main:app --port 8000 --reload

# Frontend, from the frontend directory
npm install
# Copy .env.example to .env
npm run dev

# Generate samples and run tests from the backend directory
python generate_samples.py
pytest -v
  • The four sample files are sample_invoice_matched.pdf, sample_invoice_mismatch_qty.pdf, sample_invoice_mismatch_price.pdf, and sample_invoice_scanned.png. They cover an exact PO match, quantity and unit-price mismatches, and a simulated scanned invoice; the quantity example is 12 chairs versus 10 ordered.
  • Demo accounts are Submitter submitter@edvexa.com / Submitter123! and Reviewer reviewer@edvexa.com / Reviewer123!. The demo flow is to upload a PDF and select “Submit for Review” as Submitter, then find the document and approve or reject it as Reviewer.
  • The API includes login, registration, current-user profile, document listing/upload/detail/file/comparison, PO attachment and submission; reviewer-only queue, approval, rejection, correction requests and comments; PO listing and creation; dashboard statistics and recent activity; and a health check.
  • The 16 pytest tests comprise 5 authentication tests, 4 extraction/validation tests, 3 PO-comparison tests, and 4 workflow/RBAC tests. Targeted commands include pytest tests/test_auth.py -v and pytest tests/test_po_comparison.py -v.
  • Validation covers required fields, date format, monetary sanity, line-item consistency, and duplicate detection. PO matching compares line items with a configurable tolerance of +/- 0.05 and flags quantity, unit-price, and total-amount discrepancies. The README does not explain the detailed rules or tolerance semantics.
  • The workflow states are DRAFT → SUBMITTED → APPROVED / REJECTED. Submitters upload and submit; Reviewers approve, reject, or request corrections. Reviewer comments are retained and visible on documents, and the audit trail records chronological state transitions, actors, and timestamps.
  • The dashboard summarizes total documents, pending review, approved and flagged documents, and recent activity.