Information Extractor

Information Extractor, make you data structural.

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Information Extractor, structurize your data. Finding the output from LLM too messy to use? Want to get the output you want from the file? Try Information Extractor, make your data structural, empower your end-to-end AI APP.

Input Parameters




Please describe your intention for extracting information from the input content. You can specify their types and names explicitly. For example: 'Help me to extract the abstract, named as `abs`, and summarize the chapter 3 Method, named as `approach`. Count how many sections are in this paper, named as `num_sec` of type int.'



The raw text content. It can be from crawler, from reader or copy-paste.



LLM backend


Output Parameters

NameTypeDescriptionFile Type



The output was designed based on your instruction

Output Example

See below

Detailed Guidelines

  • Example

    • input

      • instruction

        Tell me How to build an AI app from start to finish. The step extracted should be named as 'step_{i}', where i is the number from 1.
      • raw_content

        We need to acknowledge that Artificial intelligence has become one of the most promising technologies of the 21st century, and it's rapidly transforming industries across the globe, especially those actively working to stay ahead of the competition.
        Look at industries like healthcare or transportation, proving how much potential AI has to revolutionize how we live, work, and interact with the world around us.
        Since more and more businesses and entrepreneurs are now looking to build AI applications to improve their operational efficiency or enhance customer experience, we want to discuss the steps involved in creating an AI-powered app.
        So, whether you are a startup or an established business looking to integrate AI or chatbot functionality into your operations, read on to discover valuable insights and practical tips that will help you get started.
        (... Omitted for this example.)
    • Output

      # This is json
         "step_1": "Define the problem you’re solving by understanding the problem domain and determining if AI is the best solution. This involves choosing the appropriate AI technique based on the problem's nature, available data, and desired performance metrics.",
         "step_2": "Collect and preprocess data relevant to your AI model. This includes acquiring data from various sources, handling multiple data formats, and employing techniques like data augmentation, normalization, and feature engineering to prepare a high-quality dataset for training and validating the AI model.",
         "step_3": "Choose an algorithm that aligns with your problem and can handle the nuances of your domain. Train your AI model using the prepared dataset, adjusting the algorithm's parameters to minimize error. Use techniques like dropout, early stopping, or adversarial training to ensure the model is accurate and unbiased.",
         "step_4": "Choose a development platform and tech stack suitable for your app's complexity and scalability needs. This involves selecting programming languages, libraries, and tools for deep learning, NLP, cloud computing, data management, and IDEs. Integrate the trained AI model into the app's architecture, focusing on user input and interaction, and ensuring the model is optimized for the target platform.",
         "step_5": "Test the application thoroughly using a combination of synthetic and real-world data, and employing various testing techniques such as unit, integration, and acceptance testing. Ensure the app is reliable, secure, and ready for deployment by setting up infrastructure and resources. Once ready, deploy the app, continuously monitor its performance, and provide timely updates and enhancements based on user feedback and changing requirements."

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