🧾 invoice2json

Turns a PDF invoice or photo of a receipt into structured JSON using a local Ollama vision model — no cloud APIs, no data leaving your machine.

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Quick start

1. Prerequisites

• Python 3.10+
• Ollama running locally (ollama serve)
• A vision-capable model pulled, e.g.:

ollama pull llava          # default model used by the API
# or
ollama pull llava:13b      # more accurate, needs ~10 GB VRAM
# or
ollama pull moondream      # very fast, lower accuracy

For PDF support you also need poppler-utils (only needed if you don't install pypdfium2):

# macOS
brew install poppler

# Ubuntu / Debian
sudo apt install poppler-utils

2. Install Python dependencies

pip install -r requirements.txt

# Optional: faster PDF rasterisation
pip install pypdfium2

3. Run the server

uvicorn main:app --reload --port 8000

Interactive docs: http://localhost:8000/docs

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API reference

POST /extract

Upload a file and get back structured JSON.

Parameter	Type	Default	Description
file	form-data file	—	PDF, PNG, JPG, or WEBP
model	query string	llava	Ollama model name

Example — curl:

# Image
curl -s -X POST "http://localhost:8000/extract" \
     -F "[email protected]" | jq .

# PDF
curl -s -X POST "http://localhost:8000/extract" \
     -F "[email protected]" | jq .

# Use a different model
curl -s -X POST "http://localhost:8000/extract?model=llava:13b" \
     -F "[email protected]" | jq .

Example — Python:

import httpx

with open("invoice.pdf", "rb") as f:
    resp = httpx.post(
        "http://localhost:8000/extract",
        files={"file": ("invoice.pdf", f, "application/pdf")},
    )
resp.raise_for_status()
data = resp.json()
print(data["total_amount"])
print(data["line_items"])

Example response:

{
  "document_type": "invoice",
  "vendor": {
    "name": "Acme Corp",
    "address": "123 Main St, Springfield, IL 62701",
    "phone": "555-867-5309",
    "email": "[email protected]",
    "website": null,
    "tax_id": "12-3456789"
  },
  "customer": {
    "name": "Jane Smith",
    "address": "456 Oak Ave, Portland, OR 97201",
    "email": null,
    "account_number": "CUST-00412"
  },
  "document_number": "INV-2024-0087",
  "document_date": "2024-03-15",
  "due_date": "2024-04-14",
  "currency": "USD",
  "line_items": [
    { "description": "Widget Pro × 3", "quantity": 3, "unit_price": 49.99, "total": 149.97 },
    { "description": "Shipping", "quantity": 1, "unit_price": 9.95, "total": 9.95 }
  ],
  "subtotal": 159.92,
  "tax_rate": 8.5,
  "tax_amount": 13.59,
  "discount_amount": null,
  "total_amount": 173.51,
  "amount_paid": 0,
  "amount_due": 173.51,
  "payment_method": null,
  "notes": "Net 30 payment terms",
  "_source_file": "invoice.pdf",
  "_model_used": "llava",
  "_pages_processed": 1
}

GET /models

Lists all models currently available in your local Ollama instance.

curl http://localhost:8000/models | jq .

GET /health

Liveness check — returns {"status": "ok"}.

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Multi-page PDFs

PDFs are rasterised page-by-page (up to 10 pages by default, configurable via MAX_PDF_PAGES in main.py). Line items are merged across all pages; scalar fields (totals, vendor name, etc.) use the first non-null value found.

Tips

• Accuracy: llava:13b or llava:34b are noticeably more accurate than the default 7B for dense invoices. moondream is fast but misses fields more often.
• Scanned / low-res images: pre-process with an upscaler or ensure the source is at least 150 DPI before uploading.
• Batch processing: wrap the /extract endpoint in an async loop with asyncio.gather() or use a task queue (Celery, ARQ) for large volumes.