Lesson 3.4 — Metadata as a Compass
Introduction: Beyond the Text
In the previous lesson, we learned how to break down large documents into smaller, more manageable chunks. But what if we could give our AI agent a compass to help it navigate this sea of information? This is where metadata comes in.
Metadata is data about data. It is a set of descriptive tags that you can add to your data chunks to provide additional context and information. This information can be anything from the source of the data to the date it was created, and it can be used to filter and search your data in powerful ways.
This lesson will explore the role of metadata as a compass for your AI agent, and show you how to use it to build a more intelligent and effective knowledge base.
Why is Metadata So Important?
Metadata is important for two main reasons:
Improved Retrieval Accuracy: By adding metadata to your data chunks, you can provide your agent with additional context that it can use to find the most relevant information. For example, you could add a "source" tag to each chunk to indicate where the information came from. This would allow your agent to filter its search results by source, which would be very useful if you were looking for information from a specific source.
More Powerful Filtering and Searching: Metadata can also be used to filter and search your data in more powerful ways. For example, you could add a "date" tag to each chunk to indicate when the information was created. This would allow you to filter your search results by date, which would be very useful if you were looking for information from a specific time period.
Metadata is the key to unlocking the full potential of your Vector Store. By providing your agent with a rich set of metadata, you can enable it to perform more sophisticated searches and retrieve more relevant information [1].
Common Metadata Fields
There are many different types of metadata that you can add to your data chunks. Here are some of the most common fields:
Source
The source of the data, such as a URL, a file name, or a database table.
Date
The date the data was created or last modified.
Author
The author of the data.
Keywords
A list of keywords that describe the content of the data.
Metadata as a Compass Worksheet
---
title: "Framework-HR"
source: "Framework-HR.md"
tags: ["M&A", "Framework", "Framework-HR"]
version: "1.0"
last_updated: "2025-09-20"
---Metadata Field
[Source, Date, Author, Keywords, etc.]
Value
[The value of the metadata field]
Conclusion: The Power of Context
Metadata is a powerful tool that can be used to improve the performance of your AI agent. By providing your agent with a rich set of metadata, you can enable it to perform more sophisticated searches and retrieve more relevant information.
In our next lesson, we will explore the concept of cross-source normalization and learn how to create a consistent and coherent knowledge base from multiple sources.
References
Last updated