Training a Language Model Using Your Personal Library

Are you interested in harnessing the power of Artificial Intelligence to interact with the vast knowledge contained within your personal library? If so, you’re in the right place. In this post, we’ll explore how you can transform your collection of 2,000 PDF books into a functional large language model (LLM) that allows you to ask questions and receive informative answers based on the material from those texts. Let’s dive into the steps you’ll need to consider in this exciting project.

Step 1: Convert PDF to HTML

The first step in your journey is to convert your PDF files into a format that can be easily processed. HTML is a great choice because it preserves the text structure. There are various tools available that can assist with this conversion, such as pdftohtml or Adobe Acrobat. Make sure to check that the text remains legible and well-organized after the conversion.

Step 2: Extract Text from HTML

Once your documents are in HTML format, you’ll want to extract the text for further processing. Libraries like Cheerio in Node.js can be useful for parsing HTML and extracting the necessary text content. Keep in mind that cleaning up the text will be essential to maintain the integrity of the information.

Step 3: Preparing Your Data for Modeling

After extracting text, the next step is to preprocess it. This involves tokenization, normalization (converting text to lower case, for instance), and removing any unnecessary formatting or characters. By preparing your data correctly, you ensure that the language model can learn effectively from the content.

Step 4: Utilizing TensorFlow.js with Node.js

With your data ready, you can now turn to TensorFlow.js in a Node.js environment to train your language model. If you’re new to this, consider exploring existing tutorials on training neural networks with TensorFlow.js. There are also numerous libraries and tutorials focused on building language models that can help you get started on the right foot.

Step 5: Asking Questions and Interacting with Your Model

Ultimately, your goal is to enable your LLM to respond to questions based on the content of your books. This will require setting up a method for inputting queries and retrieving responses from your trained model. You might consider creating a simple web interface where you can type your questions, or even explore integrating voice recognition features for a more interactive experience.

Seek Guidance and Collaborate

Embarking on this project