In the age of information overload, the ability to quickly understand the essence of a text can be an invaluable skill.

This is where the art of summarization comes into play, and it has been revolutionized by AI tools like Mistral 7B and LangChain.

But how can one harness this technology in a user-friendly way? Enter Streamlit, a game-changer for building data applications.

In this article, we will guide you through the steps of combining Streamlit's simplicity with the summarization prowess of Mistral 7B powered by LangChain orchestration to create a powerful tool for distilling long articles into their core messages.

Whether you're a busy professional, a student, or just someone with a voracious appetite for knowledge, this tutorial will empower you to stay informed and efficient.

Prepare to unlock the potential of AI-assisted reading without the need for deep technical know-how.

What is Streamlit?

Streamlit was born out of the need to simplify the transition from data scripts to interactive web applications.

It was developed by a group of AI researchers who recognized that the tools for building custom web apps were too complex and time-consuming for data scientists.

Streamlit's development began with the idea that turning data scripts into web apps should not be more difficult than writing the scripts themselves.

Since its inception, Streamlit has rapidly gained popularity in the data science community for its ease of use and speed of development.

Key features of Streamlit include:

  • No Front-end Experience Required: Streamlit allows developers to create applications with only Python knowledge, eliminating the need for HTML, CSS, or JavaScript expertise.
  • Rapid Prototyping: Changes to the code are automatically reflected in the app, enabling real-time app updates without the need for a refresh.
  • Component Library: A rich set of widgets and components, including sliders, buttons, and text inputs, that can be easily integrated with minimal code.
  • Data Caching: Streamlit's caching mechanism speeds up data loading and processing, making apps more efficient.
  • Custom Components: Developers can create custom components or use community-built components to extend functionality.
  • Deployment Ease: Streamlit apps can be quickly deployed on various platforms, allowing for easy sharing and collaboration.

What is Mistral 7B?

The Mistral 7B is a Large Language Model (LLM) with 7 billion parameters, designed for generating text and performing various natural language processing tasks.

It is notable for its superior performance and efficiency, particularly in comparison to other models such as Llama 2 13B and Llama 1 34B, excelling across all evaluated benchmarks, including reasoning, mathematics, and code generation.

The architecture of the Mistral 7B leverages grouped-query attention for faster inference and sliding window attention to effectively manage tasks, making it suitable for real-time applications that require quick responses

This allows Mistral 7B to extract key information from texts and understand the context and nuances of the language used.

Advantages of using Mistral 7B for summarization include:

  • Contextual Understanding: Mistral 7B can grasp the context of the content, ensuring that summaries are coherent and capture the essence of the original text.
  • Brevity and Relevance: The model is trained to identify and condense the most relevant information, producing succinct summaries that save time for the reader.
  • Scalability: Mistral 7B can handle a variety of documents, from short news articles to lengthy research papers, making it versatile.
  • Language Comprehension: The AI has a broad understanding of language and can summarize content in a way that is accessible to non-expert readers.
  • Continuous Learning: Like many AI models, Mistral 7B can improve over time, learning from its interactions to provide better summaries.

Streamlit and Mistral 7B are powerful on their own. Still, when combined, they offer a compelling solution for anyone looking to streamline the process of summarizing and digesting large volumes of text.

What is LangChain?

LangChain is a versatile open-source framework designed to facilitate the development of applications that utilize large language models (LLMs).

It's tailored to simplify the creation of generative AI application interfaces, making it easier for developers to create advanced natural language processing (NLP) applications.

LangChain supports a range of uses, from chatbots and Generative Question-Answering (GQA) to document analysis and summarization.

The core idea is to "chain" together different components to create advanced use cases for LLMs, allowing for a modular approach to building applications.

These chains can consist of multiple components from several modules, creating end-to-end solutions for frequently encountered applications. It's designed to be simple to use, supporting a wide array of LLM-powered applications and encouraging contributions from a large user and contributor community.

In summary, LangChain is a powerful tool for developers looking to harness the capabilities of large language models in creating context-aware, intelligent applications that can analyze, summarize, and interact using natural language.

Building the Application

To build your application, only 2 sets of files will be required, one will contain the summarization logic with LangChain and Mistal 7B the other file will contain the UI interface with Streamlit.

First, we start installing the necessary requirements. Assuming you have created a new Python project and set up a virtual environment, run the command:

pip install streamlit langchain beautifulsoup4 ctransformers transformers newspaper3k

Here's what each package is used for:

  • streamlit: This is an open-source Python library that makes it easy to create and share beautiful, custom web apps for machine learning and data science.
  • langchain: LangChain is a Python framework for developing applications powered by language models, which simplifies the integration of Large Language Models (LLMs) into various applications.
  • beautifulsoup4: BeautifulSoup is a Python library for parsing HTML and XML documents. It's commonly used for web scraping, which is the process of extracting information from websites.
  • ctransformers and transformers: This library by Hugging Face provides general-purpose architectures for Natural Language Understanding (NLU) and Natural Language Generation (NLG) with thousands of pre-trained models in 100+ languages including Mistral 7B.
  • newspaper3k: This package is used for extracting and parsing newspaper articles. It's useful for web scraping purposes, allowing for easy article retrieval and content curation.

For the Mistral 7B, you will use a version of Mistral 7B from TheBloke, which is optimized to run on the CPU, hence the use of ctransforemers and transformers.