bill-organizer-extract-categorize-bill

name	bill-organizer:extract-categorize-bill
description	Takes raw OCR text and runs the Cohere classification step to extract structured bill data. Use standalone for debugging AI output, reprocessing stored rawText, or testing prompt variations.

Extract and Categorize Bill Data

Run the Cohere classification step in isolation. Takes raw text (from OCR or manual input) and returns structured JSON with title, amount, and category.

When to Use

• Debugging: the full pipeline saves a bill but category/amount is wrong — re-extract from stored rawText
• Prompt tuning: test different prompts against the same raw text
• Batch reprocessing: fix misclassified bills already in MongoDB
• Dry-run testing: verify Cohere output WITHOUT consuming Cloudinary/Tesseract resources

Prerequisites

• COHERE_API_KEY in server/.env
• cohere-ai installed: npm install cohere-ai

Usage

Option A: Extract from text file or pasted text

Create a script that reads text and classifies it:

// server/src/utils/classifyText.js
import { CohereClientV2 } from 'cohere-ai';
import 'dotenv/config';

const co = new CohereClientV2({ token: process.env.COHERE_API_KEY });

async function classifyBillData(rawText) {
  const response = await co.chat({
    model: 'command-a-plus-05-2026',
    messages: [{
      role: 'user',
      content: `You are a bill data extraction specialist.
Extract the title, total amount (as a number), and category from OCR text.
Categories: Electricity, Water, Internet, Phone, Shopping, Other.
Return ONLY valid JSON.

Now extract from this OCR text:

---
${rawText}
---`,
    }],
    response_format: {
      type: 'json_object',
      schema: {
        type: 'object',
        properties: {
          title:    { type: 'string' },
          amount:   { type: 'number' },
          category: { type: 'string', enum: ['Electricity','Water','Internet','Phone','Shopping','Other'] },
        },
        required: ['title', 'amount', 'category'],
      },
    },
  });

  // Find the text block (Cohere may wrap in thinking blocks)
  const contents = response.message?.content || [];
  const textBlock = contents.find((c) => c.type === 'text');
  let text = textBlock?.text || '{}';
  text = text.replace(/```json\s*/g, '').replace(/```\s*/g, '').trim();
  return JSON.parse(text);
}

// CLI usage
const rawText = process.argv[2] || 'Yangon Electricity Supply Corporation\nMarch 2025 Bill\nTotal Amount: 25,000 MMK';
const result = await classifyBillData(rawText);
console.log(JSON.stringify(result, null, 2));

cd server && node src/utils/classifyText.js "Yangon Electricity, Total: 25000"
# { title: "Yangon Electricity Bill", amount: 25000, category: "Electricity" }

Option B: Reprocess bills from MongoDB

// server/src/utils/reprocessBills.js
import 'dotenv/config';
import mongoose from 'mongoose';
import Bill from '../models/Bill.js';
import { CohereClientV2 } from 'cohere-ai';

const co = new CohereClientV2({ token: process.env.COHERE_API_KEY });

await mongoose.connect(process.env.MONGODB_URI);

// Find bills that need reclassification (e.g., category "Other" that shouldn't be)
const bills = await Bill.find({ category: 'Other' });

for (const bill of bills) {
  if (!bill.rawText) {
    console.log(`Skipping ${bill._id} — no rawText stored`);
    continue;
  }

  const response = await co.chat({
    model: 'command-a-plus-05-2026',
    messages: [{
      role: 'user',
      content: `Reclassify this bill:\n\n`,
    }],
    : {
      : ,
      : {  },
    },
  });

   contents = response.?. || [];
   textBlock = contents.( c. === );
   text = textBlock?. || ;
  text = text.(, ).(, ).();
   newData = .(text);
   .(bill., {
    : newData.,
    : newData.,
    : newData.,
  });
  .();
}

 mongoose.();

Option C: Interactive prompt testing

To test different prompts against the same text:

import { CohereClientV2 } from 'cohere-ai';

const co = new CohereClientV2({ token: process.env.COHERE_API_KEY });

const rawText = `Your OCR text here...`;

const prompts = [
  { name: 'default', instruction: 'Extract bill data. Return JSON.' },
  { name: 'myanmar-focused', instruction: 'Extract bill data. The text contains Myanmar language. Look for "ကျသင့်ငွေ" for amount. Return JSON.' },
  { name: 'verbose', instruction: 'You are a Myanmar utility bill expert. Identify the biller, extract total, categorize precisely. Return JSON.' },
];

for (const prompt of prompts) {
  const response = await co.chat({
    model: 'command-a-plus-05-2026',
    messages: [{ role: 'user', content: `${prompt.instruction}\n\nOCR TEXT:\n${rawText}` }],
    response_format: {
      type: 'json_object',
      schema: { /* ... */ },
    },
  });
  const contents = response.message?.content || [];
   textBlock = contents.( c. === );
   text = textBlock?. || ;
  text = text.(, ).(, ).();
  .(, .(text));
}

Expected Output

{
  "title": "March Electricity Bill",
  "amount": 25000,
  "category": "Electricity"
}

Validation Checklist

• title is a non-empty string
• amount is a positive number (not NaN, not string, not negative)
• category is exactly one of: Electricity, Water, Internet, Phone, Shopping, Other
• Amount makes sense for the category (e.g., electricity 10k–100k MMK, phone top-up 1k–20k MMK)

Common Fixes

Problem	Fix
Amount is 0	OCR text may not have the amount clearly — check rawText quality. The backend will now reject these with 422.
Category always "Other"	User prompt needs Myanmar company/context hints
Title is "Unknown Bill"	OCR text may be garbled — improve image quality or add more Myanmar traineddata
Cohere returns markdown	Strip with .replace(/```json\n?/g, '').replace(/```/g, '').trim()
response.message.content[0].text throws on JSON.parse	Cohere may wrap JSON in markdown fences or thinking blocks — find by .type === 'text'
401 Unauthorized	Wrong or missing COHERE_API_KEY

Add to package.json

"classify": "node src/utils/classifyText.js"