After parsing the document, we can obtain structured or semi-structured data. The main task now is to break them down into smaller chunks to extract detailed features, and then embed these features to represent their semantics. Its position in RAG is shown in Figure 1.

In this tutorial we will create a personal local LLM assistant, that you can talk to. You will be able to record your voice using your microphone and send to the LLM. The LLM will return the answer with text AND speech.

20240215 Additional content: Unveiling PDF Parsing: How to extract formulas from scientific pdf papers

The advent of large language models (LLMs) , trained on vast amounts of text data, has been one of the most significant breakthroughs in natural language processing. The ability of these models to generate remarkably fluent and coherent text with just a short prompt has opened up new possibilities for conversational AI, creative writing, and a wide array of other applications.

We are entering the era of small & highly efficient models!

Prompt engineering, or prompt design, is crafting instructions for LLMs to get desired responses. It’s essential for ensuring accurate, high-quality responses from a large language model.

Implementing RAG presents a challenge, especially when it comes to effectively parsing and understanding tables in unstructured documents. This is particularly difficult with scanned documents or documents in image format. There are at least three aspects of these challenges: