# How I Built a Scalable GenAI Invoice Extractor That Handles Hundreds of PDFs in Parallel

> Discover how a GenAI-powered system can extract structured data from multi-page invoices, flag errors, and save AI token costs while scaling effortlessly.

Published: 2025-10-19
Canonical page: https://www.rohanprajapati.dev/blog/scalable-genai-invoice-extractor


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# Scalable GenAI Invoice PDF Data Extractor

![InvoiceAI](https://pub-8d81bf1e6c73494ba22ff9837ccaf5b1.r2.dev/Scalable-Genai-Invoice-PDF-Data-Extractor-LLD.png)

Ever wondered how to process hundreds of invoices without manually extracting line items, totals, or vendor info? Let's dive into a story where GenAI meets **scalability**.

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This isn’t your typical script — it’s a **backend system** designed to handle thousands of invoices **concurrently**, flag errors, and store results efficiently.
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## Features

- **Batch & Async Processing:** Queue multiple invoices in RabbitMQ and let workers do the heavy lifting.
- **High Concurrency:** Each worker can handle ~100 files at once. Multiple workers scale horizontally.
- **GenAI-Powered Extraction:** Extract vendor details, invoice metadata, line items, totals, and payment info.
- **Error Detection:** Automatically flags mismatches in subtotal, tax, shipping, and total.
- **Multi-format Support:** PDFs, images, scanned documents, multi-page invoices.
- **Token-Efficient YAML Output:** Reduces AI token usage by 20–30% before converting to JSON for storage.

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## High-Level Architecture

<img width="1213" height="553" alt="Scalable-Genai-Invoice-PDF-Data-Extractor-LLD" src="https://github.com/user-attachments/assets/43c51579-487e-4bae-be17-8c7c62c1be28" />

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Workflow Summary:
1. Upload invoices via `/api/file/upload`.
2. Files queued in **RabbitMQ**.
3. Workers fetch jobs and run GenAI extraction in parallel.
4. YAML output → converted to JSON → stored in MongoDB.
5. Validation checks applied; errors flagged.
6. Access processed results via API.
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## Dead Letter Queue (DLQ)

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- Failed invoices go to **DLQ**.
- Review failed jobs, retry manually, or trigger alerts.
- Keeps the main queue unblocked.

**Common causes:**
- Corrupted PDFs/images
- Unsupported invoice format
- Persistent AI extraction failure

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## Retry Strategy with Exponential Backoff

- Failed jobs retry automatically.
- Wait time grows exponentially (60s → 120s → 180s…).
- Jobs exceeding max retries go to **DLQ**.

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> Smooth handling of temporary failures ensures uninterrupted processing.
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## Tech Stack

| Layer                | Technology |
|----------------------|------------|
| Backend Framework    | Express.js |
| Database             | MongoDB |
| Queue / Async Jobs   | RabbitMQ |
| Rate Limiting        | Redis |
| Containerization     | Docker & Docker Compose |
| AI / Data Extraction | Generative AI (Gemini) |

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## Installation & Setup

### 🐳 Run with Docker

```bash
docker compose --build -d
````

Access: [http://localhost:3000](http://localhost:3000)

### 💻 Run Directly

```bash
docker compose up -d
npm run dev          # dev server
npm run worker       # start background worker
npm run start        # production via PM2
```

---

## Scripts

```json
{
  "dev": "node --env-file=.env --watch index.js",
  "worker": "node --env-file=.env worker/file.worker.js",
  "queue:flush": "node --env-file=.env scripts/amqp.flush.js",
  "start": "pm2 start ecosystem.config.cjs"
}
```

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## Environment Variables

```env
GEMINI_API_KEY=<your-api-key>
AMQP_URL=amqp://user:password@localhost:5672
MONGODB_URI=mongodb://localhost:27017/invoices
REDIS_HOST=localhost
REDIS_PORT=6379
MAX_FILE_SIZE=200
PORT=3000
WORKER_CONCURRENCY=100
```

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## API Endpoints

### Upload Files

* **Endpoint:** `/api/file/upload`
* **Method:** POST
* **Form Key:** files
* **Response:**

```json
{
 "message": "Files uploaded and queued",
 "file_ids": ["abc123", "def456"]
}
```

### Fetch Files

* **Endpoint:** `/api/file`
* **Method:** GET
* **Query params:** `status=pending|processed|error&flag=true|false`

### Fetch File by ID

* **Endpoint:** `/api/file/{invoice_id}`
* **Method:** GET

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## Sample Output

```yaml
items:
- name: "Activator CREAM 5-gal 5 gallons/pailt"
  quantity: 2
  rate: 269.00
payment_info:
  subtotal: 1187
  sales_tax_percentage: 8
  shipping_handling_cost: 50
  total: 1330.75
errors:
- "Subtotal mismatch: calculated 2550.00 vs declared 1187.00"
- "Sales Tax amount mismatch: calculated 204.00 vs declared 94.75"
- "Total mismatch: calculated 2804.00 vs declared 1330.75"
```

Stored in MongoDB as JSON.

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## Scaling Workers & Concurrency

* Worker concurrency: 100 files
* Horizontal scaling: run multiple workers
* File size limit: 200MB (configurable)
* Rate limiting: Redis ensures stable API usage

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## Token Efficiency

* YAML formatting reduces AI token usage by ~20–30%
* Less characters → faster extraction → lower costs
* Read more: [How I Saved Millions in GenAI Token Costs](https://www.rohanprajapati.dev/blog/how-i-saved-millions-in-genai-token-efficiency)

---

## Postman Collection

* Import from `postman_collection.json`
* Base URL: `http://localhost:3000`
* Test endpoints: upload, fetch all, fetch by ID

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This **will render cleanly as a proper blog**:

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- The blog is interactive, and SEO-friendly.