From the course: Fundamentals of AI Engineering: Principles and Practical Applications
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Metadata enrichment and indexing
From the course: Fundamentals of AI Engineering: Principles and Practical Applications
Metadata enrichment and indexing
- [Instructor] Welcome back. So far, we've learned how to extract texts from documents and how to reorganize its structure. In this video, we'll take the next step, enriching our documents with useful metadata. Let's open up the folder that corresponds to Chapter 3 and open up the file 03_04.ipynb. Today, we're going to talk all about metadata, one of the most important topics in our series. Think of metadata as the additional labels or tags that help describe or categorize all documents. Just as the library uses library catalog cards to help you find books, metadata helps AI systems quickly find the right documents. Let's dive in and see how LlamaIndex can help us with these important tasks. Now, before we jump into code, let's understand why metadata is so important. First, it provides context about our documents. Sometimes the author, the date, the source, or the topic. It enables filtering and sorting, meaning it allows us to find all the documents from a specific date range…
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