23. LLM

Date: 2025-01-03

Status

Accepted

Context

We want to integrate a LLM solution to generate a synthesis of MADR. LLM stands for Large Language Model. It’s a type of artificial intelligence model designed to process and generate human language at a large scale. These models are built using deep learning techniques, specifically neural networks, and are trained on vast amounts of text data. They are capable of understanding and generating natural language, making them useful for tasks like:

1. Text Generation: Creating human-like text for various purposes (e.g., content creation, chatbots).
2. Text Summarization: Condensing long documents into shorter summaries.
3. Translation: Translating text between languages.
4. Question Answering: Providing answers to specific questions from a given context or dataset.
5. Sentiment Analysis: Determining the sentiment behind a piece of text (e.g., positive, negative, neutral).
6. Text Classification: Categorizing text into predefined labels (e.g., spam vs. not spam).

Decision

We will use Gemini Model to generate a synthesis based on MADR file.

Consequences

We will call Gemini API from Python service during file process.