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Invoice Processing Implementation Summary

Overview

This document summarizes the implementation work for processing invoice images, including OCR improvements, dashboard enhancements, and database cleanup utilities.

Key Implementations

1. OCR Language Support Enhancement

File: ingestion/image_processor.py

  • Issue: OCR was configured for English only (lang="en"), causing failures on Chinese invoices
  • Fix: Changed default language to Chinese (lang="ch") which supports both Chinese and English
  • Impact: Enables proper text extraction from Chinese invoices (e.g., 机动车销售统一发票)

2. Extraction Prompt Enhancement

File: brain/extractor.py

  • Issue: LLM extraction prompts didn't handle Chinese invoice field names
  • Fix: Added Chinese field name mappings:
    • 销售方 (seller) → vendor_name
    • 发票号码 → invoice_number
    • 价税合计 → total_amount
    • 税额 → tax_amount
    • 货物或应税劳务名称 → line items
  • Impact: Improved extraction accuracy for Chinese invoices

3. Database Model Fixes

File: core/models.py

  • Issue: Type mismatches between model definitions and database schema
  • Fixes:
    • Changed numeric fields from String to Numeric type (subtotal, tax_amount, tax_rate, total_amount, etc.)
    • Changed date fields from DateTime to Date type (invoice_date, due_date)
    • Added proper imports for Numeric and Date types
  • Impact: Resolves database insertion errors and ensures type consistency

4. Dashboard Enhancements

Files: interface/dashboard/app.py, interface/dashboard/queries.py

Invoice List Improvements:

  • Added new columns: Hash, Size, Vendor, Amount, Duration, Processed timestamp
  • Visual indicators: Status emojis (✅ ❌ ⏳), duplicate detection (🔄 icon)
  • Enhanced metrics: Total Invoices, Unique Files count
  • Better formatting: File sizes in KB/MB, amounts with currency, processing duration
  • Metadata expander: Shows duplicate file detection summary

Invoice Detail Improvements:

  • Dropdown selector: Easy invoice selection from list
  • Manual UUID input: Alternative method with validation
  • Enhanced information display:
    • File information section (name, type, size, hash, version)
    • Processing status with duration calculation
    • Extracted data with financial details
    • Validation results with summary metrics
  • Helper section: Instructions on how to get Invoice ID

5. Duplicate Processing Logic Fix

File: ingestion/orchestrator.py

  • Issue: Version increment didn't always get the latest version when multiple invoices share same hash
  • Fix: Added ordering by version DESC to ensure latest version is used for increment
  • Impact: Correct version numbering for reprocessed files

6. Utility Scripts

scripts/process_all_invoices.py

  • Processes all invoice images in data/ directory
  • Shows progress for each file
  • Displays summary of successful/failed processing
  • Supports custom API URL and data directory

scripts/cleanup_vectors.py

  • Cleans up pgvector/embedding data
  • Finds tables with vector columns
  • Finds LlamaIndex-related tables
  • Currently: No vector data found (LlamaIndex uses in-memory storage)

scripts/cleanup_invoices.py

  • Cleans up invoice records and related data
  • Deletes: invoices, extracted_data, validation_results, processing_jobs
  • Features:
    • Dry-run mode (--dry-run)
    • File path filtering (--file-path-filter)
    • Safety confirmation (requires typing "DELETE ALL")
  • Respects foreign key constraints

7. Documentation

docs/duplicate-processing-logic.md

  • Detailed explanation of duplicate file handling
  • File hashing and version management
  • Example scenarios
  • Use cases and best practices

Technical Details

OCR Configuration

  • Engine: PaddleOCR
  • Default Language: Chinese (ch) - supports both Chinese and English
  • Features: Text direction detection enabled

Database Schema

  • Numeric Fields: Use Numeric(precision, scale) type
  • Date Fields: Use Date type (not DateTime)
  • File Hash: SHA-256 for duplicate detection
  • Version Tracking: Increments for reprocessed files

Processing Flow

  1. File ingestion → Calculate SHA-256 hash
  2. Check for existing invoice with same hash
  3. Determine version (increment if exists)
  4. OCR/Text extraction (PaddleOCR for images)
  5. AI extraction (LlamaIndex RAG)
  6. Validation (business rules)
  7. Self-correction (if validation fails)
  8. Storage (PostgreSQL)

Validation Rules

  • math_check_subtotal_tax: Validates subtotal + tax = total
  • line_item_math: Validates line items sum = subtotal
  • vendor_sanity: Validates vendor name exists
  • date_consistency: Validates due_date >= invoice_date

Files Modified

  • ingestion/image_processor.py - OCR language fix
  • brain/extractor.py - Chinese invoice support
  • core/models.py - Database type fixes
  • ingestion/orchestrator.py - Version increment fix
  • interface/dashboard/app.py - Enhanced UI
  • interface/dashboard/queries.py - Improved queries

Files Created

  • docs/duplicate-processing-logic.md - Technical documentation
  • scripts/process_all_invoices.py - Batch processing
  • scripts/cleanup_vectors.py - Vector cleanup utility
  • scripts/cleanup_invoices.py - Invoice cleanup utility

Testing & Verification

  • Verified OCR works with Chinese invoices
  • Fixed database type mismatches
  • Enhanced dashboard displays all invoice data
  • Created cleanup utilities for maintenance