Train GPT for analyze large number of pdf

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Hello, I need advice on how to train a GPT to analyze a large number of PDF files, specifically around 800,000 PDF files?

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Not how that works unfortunately. What is the use-case/what are you trying to achieve? If you’re trying to do data analysis there may be other methods, but if you’re trying to get it to know and comprehend all the information in the documents I’m afraid that’s not feasible with the current technology.

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Welcome to the community!
While it’s not possible to analyze 800,000 PDF files all at once, you can break them down into chunks that GPT can process.

However, to analyze PDFs, you need to convert them to text files and analyze those.

If the PDFs contain images you want to analyze, the situation becomes a bit more complex.
You would need to convert the PDFs into image files, and doing this for 800,000 PDFs would be quite a task.

And it’s not the GPT that needs training, but probably the person doing the analysis…

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@trenton.dambrowitz @dignity_for_all
Thank you for your responses! The PDF files are actually textual documents without images, which are court rulings from a specific country. My goal is to create an AI lawyer that will provide answers to specific legal questions based on the analysis of the rulings (800,000 PDF files).

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If you have text data available, one consideration is using the Retrieval-Augmented Generation (RAG) approach.

RAG works by retrieving and ranking relevant pieces of text from a large dataset, and then presenting these top-ranked texts along with a prompt that asks the model to assess relevance to the query.

Based on this relevance, the model is instructed to transform the information into a response that answers the user’s question.
This method helps the model generate accurate answers.

If RAG alone is not sufficient, another consideration is fine-tuning the GPT model to improve its performance with RAG.

However, it’s important not to rely on GPT alone; a broader range of natural language processing techniques will be needed to address this complex challenge.

In any case, extensive trial and error will be required.

It’s also important to ensure that the results are verified by humans to maintain the reliability of the results. (Apologies if this comes off as unwarranted advice.)

I hope this information is helpful, or at least gives you an idea of how to solve the problem you’re trying to solve🙂

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Thank you for the detailed response! I hope I find someone who knows how to work with RAG.

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One of the critical points in your journey will be to come up with a systematic classification system or taxonomy to help structure the cases and enable a targeted retrieval of information. Especially, if you are going to use RAG, then it will be critical that you have a filtering system in place that is anchored in such a classification / taxonomy.

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Thank you all for your responses! Do you know anyone who works on this topic and how much it would cost to implement my idea?

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Have you validated your scenario with a smaller sample, say by manually analyzing 10 PDFs (prompt by prompt)? Is there an existing standard that you have listed and validated for this analysis?

I ask because 800k is a significant amount of data, especially if you haven’t gone through these preliminary steps before.

To streamline and scale this process, you might consider using tools like Python and LangChain. Python, with its rich ecosystem of libraries such as PyPDF2 or PDFMiner, can help automate the PDF extraction and analysis process. LangChain, on the other hand, can facilitate the chaining of prompts and responses in a structured manner, making the manual analysis more efficient and scalable.

Here are some steps you might take:

  1. Initial Validation:

    • Start by manually analyzing a small set of PDFs to understand the nuances of your data.
    • Create a checklist or standard criteria for the analysis process.

  2. Automation with Python:

    • Use libraries like PyPDF2 or PDFMiner to automate the extraction of text from PDFs.
    • Employ data processing libraries such as pandas or numpy for text analysis and manipulation.

  3. Prompt Assistance with LangChain:

    • Utilize LangChain to automate the process of generating and chaining prompts based on the analysis criteria you’ve developed.
    • Implement feedback loops to continuously refine and validate the output.

By leveraging these technologies, you can enhance both the efficiency and accuracy of your analysis process.