Otherwise it seems like a prompt building tool, or am I missing something here?

What I've noticed, that on scanned documents, where stamp-text and handwriting is just as important as printed text, Gemini was way better compared to chat gpt.

Of course, my prompts might have been an issue, but gemini with very brief and generic queries made significantly better results.

Alas, i am let down. It is an open-source tool creating the prompt for the OpenAI API and i can't go and send customer data to them.

I'm aware of https://github.com/clovaai/donut so i hoped this would be more like that.

If you're dealing with unstructured data trapped in PDFs, Documind might be the tool you’ve been waiting for. It’s an open-source solution that simplifies the process of turning documents into clean, structured JSON data with the power of AI.

Key Features: 1. Customizable Data Extraction Define your own schema to extract exactly the information you need from PDFs—no unnecessary clutter.

2. Simple Input, Clean Output Just provide a PDF link and your schema definition, and it returns structured JSON data, ready to integrate into your workflows.

3. Developer-Friendly With a simple setup (`npm install documind`), you can get started right away and start automating tedious document processing tasks.

Whether you’re automating invoice processing, handling contracts, or working with any document-heavy workflows, Documind offers a lightweight, accessible solution. And since it’s open-source, you can customize it further to suit your specific needs.

Would love to hear if others in the community have tried it—how does it stack up for your use cases?

enthusiastically setting up a lounge chair

> OPENAI_API_KEY=your_openai_api_key

carrying it back apathetically

  const systemPrompt = `
    Convert the following PDF page to markdown.
    Return only the markdown with no explanation text. Do not include deliminators like '''markdown.
    You must include all information on the page. Do not exclude headers, footers, or subtext.
  `;

However, my main issue is that I need to work with confidential client data that cannot be uploaded to a third party. Setting up the open-source, locally hosted version of Unstructured was quite cumbersome due to the numerous additional packages and installation steps required.

While I’m open to the idea of parsing content with an LLM that has vision capabilities, data safety and confidentiality are critical for many applications. I think your project would go from good to great if it would be possible to connect to Ollama and run locally,

That said, this is an excellent application! I can definitely see myself using it in other projects that don’t demand such stringent data confidentiality.”

1) Install tools like Ghostscript, GraphicsMagick, and LibreOffice with a JS script. 2) Convert document pages to Base64 PNGs and send them to OpenAI for data extraction. 3) Use Supabase for unclear reasons.

Some issues with this approach:

* OpenAI may retain and use your data for training, raising privacy concerns [1].

* Dependencies should be managed with Docker or package managers like Nix or Pixi, which are more robust. Example: a tool like Parsr [2] provides a Dockerized pdf-to-json solution, complete with OCR support and an HTTP api.

* GPT-4 vision seems like a costly, error-prone, and unreliable solution, not really suited for extracting data from sensitive docs like invoices, without review.

* Traditional methods (PDF parsers with OCR support) are cheaper, more reliable, and avoid retention risks for this particular use case. Although these tools do require some plumbing... probably LLMs can really help with that!

While there are plenty of tools for structured data extraction, I think there’s still room for a streamlined, all-in-one solution. This gap likely explains the abundance of closed-source commercial options tackling this very challenge.

---

1: https://platform.openai.com/docs/models#how-we-use-your-data

2: https://github.com/axa-group/Parsr

In my experience your much better of starting with a Azure Doc Intelligence or AWS Textract to first get the structure of the document (PDF). These tools are incredibly robust and do a great job with most of the common cases you can throw at it. From there you can use an LLM to interrogate and structure the data to your hearts delight.

* Run locally or on premise for security/privacy reasons

* Support multiple LLMs and vector DBs - plug and play

* Support customisable schemas

* Method to check/confirm accuracy with source

* Cron jobs for automation

There is Unstract that solves the above requirements.

https://github.com/Zipstack/unstract

https://news.ycombinator.com/item?id=42178413

You may wanna get ahead of this because the evidence is fairly damning. Failing to even give credit to the original project is a pretty gross move.

The MIT license has just 2 conditions. They are pretty easy to read, and the fist one is:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

By replacing the license, you violate this very simple agreement.