Documind – Extract Structured Data from Any Document

TL;DR

TL;DR: An open-source document processing tool that uses AI to extract structured data from any file type and export to JSON or Markdown.

Source and Accuracy Notes

⚠️ This section is MANDATORY. All links must be verified from actual source, not guessed.

• Project page: documind.xyz ← MUST visit and verify
• Source repository: github.com/DocumindHQ/documind ← MUST read README
• License: AGPLv3 (core/MIT) — verified via LICENSE file in repository
• HN launch thread: news.ycombinator.com/item?id=42171311

What Is Documind?

Documind is an open-source document processing platform that uses AI to extract structured data from unstructured documents. It converts PDFs, DOCX files, images, and other document types into structured JSON output, making it useful for developers building data pipelines, automating document workflows, or processing large volumes of paperwork.

The tool is Node.js-based and works with both cloud LLMs (OpenAI) and self-hosted models (Llama3.2-vision, Llava). A hosted beta version is available at documind.xyz for users who want to skip local setup.

Setup Workflow

Step 1: Install System Dependencies

Documind requires Ghostscript and GraphicsMagick for PDF and image processing:

# On macOS
brew install ghostscript graphicsmagick

# On Debian/Ubuntu
sudo apt-get update
sudo apt-get install -y ghostscript graphicsmagick

Step 2: Install Documind

npm install documind

Requires Node.js v18 or higher.

Step 3: Configure Environment

Create a .env file in your project directory:

OPENAI_API_KEY=your_openai_api_key_here

For local LLM setups, configure the OLLAMA_BASE_URL and select your model in the config.

Step 4: Define a Schema

Create a schema to tell Documind what data to extract:

import { Documind } from 'documind';

const dm = new Documind();

const schema = {
  fields: [
    { name: 'invoice_number', type: 'string', description: 'The invoice number' },
    { name: 'date', type: 'string', description: 'Invoice date in YYYY-MM-DD format' },
    { name: 'total_amount', type: 'number', description: 'Total amount due' },
    { name: 'vendor_name', type: 'string', description: 'Name of the vendor' }
  ]
};

Step 5: Extract Data

const result = await dm.extract({
  file: './invoice.pdf',
  schema
});

console.log(result.data);
// {
//   invoice_number: 'INV-2024-001',
//   date: '2024-11-15',
//   total_amount: 1250.00,
//   vendor_name: 'Acme Corp'
// }

Deeper Analysis

Supported File Formats

• PDF (primary)
• DOCX
• PNG, JPG (images for OCR + vision models)
• TXT
• HTML

Schema Flexibility

Documind supports both pre-defined templates and custom schemas. Templates are available for common document types like invoices, receipts, and contracts. Custom schemas allow precise control over what fields to extract and how to interpret them.

Local LLM Option

Unlike fully cloud-dependent tools, Documind supports running extraction locally using Ollama with Llama3.2-vision or Llava. This keeps document data private and avoids per-call API costs.

const dm = new Documind({
  provider: 'ollama',
  model: 'llama3.2-vision',
  baseUrl: 'http://localhost:11434'
});

Auto-Schema Generation

Documind can analyze a document and auto-generate an appropriate schema, useful for exploring unknown document structures or rapid prototyping.

Practical Evaluation Checklist

• Ghostscript and GraphicsMagick installed before running Documind
• Node.js v18+ confirmed
• .env file with valid OPENAI_API_KEY or Ollama endpoint configured
• Basic schema extraction tested on a PDF
• JSON output parsed and validated
• Multi-file batch processing works if needed

Security Notes

• API keys are stored in a local .env file — never commit this to version control
• Local LLM mode keeps all document data on-premises
• The AGPLv3 license requires that modifications to the core modules be opensourced if distributed

FAQ

Q: Can Documind process passwords-protected PDFs? A: No — Ghostscript handles PDF processing, and password-protected files will fail silently. Remove password protection before passing files to Documind.

Q: How accurate is the extraction? A: Accuracy depends heavily on the LLM used. OpenAI GPT-4o produces highly reliable results for standard document layouts. Local models like Llama3.2-vision work well for simpler documents but may struggle with complex tables or handwriting.

Q: Is there a hosted API available? A: Yes — the team offers a hosted beta at documind.xyz with managed APIs. The open-source version remains fully functional for self-hosting.

Q: What happens with multi-page documents? A: Documind processes all pages and aggregates results into a single JSON object. The exact aggregation behavior depends on the schema definition.

Conclusion

Documind fills a practical gap between raw OCR (which gives you text, not structure) and manual data entry. By pairing it with a capable vision model, developers can automate document extraction pipelines that previously required human review. The local LLM option makes it viable for privacy-sensitive workflows without sacrificing the extraction quality that GPT-4o provides.

For developers already working with Node.js document pipelines, Documind is worth a look — the setup takes under ten minutes and the npm package handles most of the complexity around schema definition and output formatting.