Convert a given URL into LLM-friendly markdown. This allows large language models (LLMs) to easily process and understand the content of the URL. This is useful for tasks such as:

Summarizing web pages
Extracting key information from web pages
Generating questions and answers from web pages
Translating web pages into different languages
Creating chatbots that can interact with web pages

Convert a given URL into LLM-friendly markdown.

SemaReader API 📖

In today's rapidly evolving digital landscape, Large Language Models (LLMs) are revolutionising how we interact with information. But even the most sophisticated LLMs are limited by their ability to access and process data from the web. That's where the SemaReader API comes in.

Why? 🤔

Providing context to LLMs are crucial for reducing hallucinations and improving the quality of generated text. But providing too much or noisy context can equally degrade the quality. The SemaReader API is designed to provide a concise and clean version of any webpage that can be easily consumed by LLMs.

How it works? 🛠️

Given a URL, the SemaReader API extracts the main content from the page and returns it in a format that is easy for LLMs to understand. This allows you to quickly and easily access the information you need without having to worry about the noise that often comes with web content. It:

Fetches the webpage
Extracts the main content using a consistency score
Filters out the noise using content scores
Returns the content in a clean and concise format
Provides metadata such as title, description, and image

Use Cases 📚

The SemaReader API works well in a variety of use cases, including:

Automated Content Summarisation: Feed articles, blog posts, or news stories into your LLM for instant summaries.
Knowledge Extraction: Extract key facts, figures, and insights from websites for research, analysis, or decision-making.
Chatbot Enhancement: Equip your chatbots with real-time information from the web, enabling them to answer user queries more effectively.
Content Creation: Generate new content based on existing web resources, such as blog posts, articles, or social media updates.
Data Enrichment: Enrich your datasets with information extracted from web pages, improving the accuracy and effectiveness of your LLM models.

It is most suitable for content heavy websites such as blogs, news websites, and articles.

Limitations 🪧

No JavaScript Rendering: The API does not render JavaScript, so it is not suitable for webpages that require JavaScript to load content. This is not an issue for many content websites but could be a problem for web applications that initially return an HTML scaffold and then use JavaScript to pull in data.