Using Instructor to Return Typed Data from Ollama

The video demonstrates how to generate structured and typed output from Alpaca using Python libraries Instructor and Pontic, addressing challenges with inconsistent response structures. By leveraging these libraries, users can automate the parsing and structuring of data, enabling more efficient data handling in various scenarios such as function calling and web scraping.

In the video, the presenter demonstrates how to generate structured and typed output from Alpaca using Python libraries Instructor and Pontic. They start by making a call to the Alpaca 2 model to list five cities and their countries with short descriptions. The initial response is in plain text, making it challenging to use the data in other applications. To resolve this, the presenter converts the response to JSON format for better structuring and usability. However, they highlight the inconsistency in the structure of responses, which can complicate data manipulation and processing.

The video introduces JSON format for Alpaca, also known as function calling, which provides a structured way to call functions. By passing the format argument as JSON, the responses become more organized and predictable. Despite the improvements, the video shows that the JSON responses can still vary in structure, leading to potential challenges in data handling and processing. The presenter emphasizes the importance of having a consistent and reliable response structure when working with data.

To address the issues with data consistency and structure, the presenter introduces the Instructor library by Jason Lou. By patching the OpenAI client library with Instructor, it adds behavior that allows for typed output. This enhancement enables the creation of typed objects directly from the Alpaca responses, eliminating the need for manual parsing and structuring of data. The video showcases how using Instructor with the Pontic model can return typed city objects, demonstrating the power of structured and typed data handling.

The video further explores the application of structured data handling in web scraping scenarios. By utilizing additional libraries like Markdown, the presenter demonstrates how to scrape content from websites and transform it into structured data. Using the defined post model, the video shows how to extract and display a list of posts from a website’s content. The structured approach simplifies the web scraping process, enabling easy extraction of specific data without the need for complex parsing techniques.

Lastly, the video highlights the challenges faced when using the structured data approach for web scraping on sites with vast amounts of content, such as Books to Scrape. Due to the complexity and volume of data on such sites, the Alpaca model may struggle to provide accurate and structured responses. Despite some limitations in handling extensive data sets, the video emphasizes the overall benefits of utilizing structured and typed data handling techniques with libraries like Instructor and Pontic for various data processing tasks, including web scraping.