Discussion on LLMSummarizer component

[julian-risch]

This is the discussion board for LLMSummarizer component.

We introduced the LLMSummarizer component, which summarizes long texts with the help of an LLM. The component can even do so recursively for very long texts. It's inspired by code from an OpenAI blog post.

Usage example

from haystack_experimental.components.summarizers.summarizer import Summarizer
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack import Document
text = ("Machine learning is a subset of artificial intelligence that provides systems "
        "the ability to automatically learn and improve from experience without being "
        "explicitly programmed. The process of learning begins with observations or data. "
        "Supervised learning algorithms build a mathematical model of sample data, known as "
        "training data, in order to make predictions or decisions. Unsupervised learning "
        "algorithms take a set of data that contains only inputs and find structure in the data. "
        "Reinforcement learning is an area of machine learning where an agent learns to behave "
        "in an environment by performing actions and seeing the results. Deep learning uses "
        "artificial neural networks to model complex patterns in data. Neural networks consist "
        "of layers of connected nodes, each performing a simple computation.")
doc = Document(content=text)
chat_generator = OpenAIChatGenerator(model="gpt-4")
summarizer = Summarizer(chat_generator=chat_generator)
summarizer.run(documents=[doc])

Try it out and let us know what you think in the comments 👇

Note: Experimental features live in this repository for a fixed period of time. We don't guarantee that we will continue maintaining experimental features. But if they are successful, stable and if you like it, we will move the feature to the core Haystack package.