The article further demonstrates the extraction process using a real-world example of a web page containing home addresses in Seattle. The extracted data, which adheres to a provided JSON schema, includes the street address, city, state, postal code, and country. The article concludes by highlighting the effectiveness of Language Learning Models (LLMs) like OpenAI GPT-4 in extracting structured data from unstructured sources.
Key takeaways:
- AI-enabled applications like OpenAI GPT-4 are effective in extracting structured data from unstructured data such as web pages, PDFs, or audio transcripts.
- Graphlit offers a new GraphQL mutation 'extractContents' for easy data extraction, using OpenAI GPT-4 Turbo 128K model for high-quality results.
- The extraction process involves creating a specification, defining the tools to be executed by the LLM, and then using the specification with the defined tool to extract the data.
- The extracted data, such as postal addresses, can be synchronized with other software applications like Google Maps, demonstrating the power of using LLMs for data extraction.