You're Already Using Vector Search! Here's How to Master It

Introduction

Have you ever found yourself searching for a product online, but you can't quite remember its name? Or perhaps a catchy song lyric is stuck in your head, and you want to find out who the artist is? If so, you’ve likely engaged with vector search without even realizing it. Vector search is a cutting-edge technology that assists in finding semantically similar or related items, effectively helping you find what you need among billions of data points.

What Is Vector Search?

Vector search goes beyond traditional keyword searches by understanding the meaning behind your queries. For example, if you type in a description of a product, the system can decode the context and retrieve the most relevant results, such as the "Swift Cleaner 3000," the vacuum of all vacuums. Similarly, if you're trying to find a song based on a few memorable lyrics, vector search can help track down the artist behind that earworm.

The Value of Vector Search for Businesses

Vector search is not just beneficial for personal use; it also holds immense value for businesses. By implementing vector search technology, teams can efficiently navigate and locate relevant documentation, identify subject matter experts, and discover use cases across various teams. Moreover, customers can benefit from this technology by quickly finding similar products through targeted conversations and personalized recommendations.

Implementing Vector Search with Vertex AI

So, how can you harness the power of vector search? Thankfully, Google provides an accessible platform called Vertex AI where developers can set up vector search projects. Here's how you can get started:

No Code Option

If coding isn't your forte, don’t worry! Vertex AI offers an intuitive user interface (UI) for those who prefer a no-code solution. Here's a step-by-step guide on setting up vector search using the UI:

1. Navigate to the "Deploy and Use" tab in Vertex AI (the location may change as updates occur).
2. Click on the "Index" option to create a new vector search index.
3. Fill in the necessary fields: Name, Description, and Region.
4. Choose a search algorithm. The default, "Tree AH," is efficient and scalable, utilizing Approximate Nearest Neighbor (ANN) methodologies. Alternatively, you can use “Brute Force” for simpler, smaller projects.
5. Configure dimensions of your embeddings; dimensions encapsulate the attributes that describe your data.
6. Decide if you’d like to retrieve results in batches or streams.
7. Set the shard size, which breaks your index into equally sized parts.
8. Click "Create" and wait for the index to be established; this typically takes under 10 minutes.
9. After index creation, deploy your index to an endpoint.

Code-Based Approach

If you're comfortable with coding, Vertex AI notebooks can be utilized to implement vector search programmatically. The process includes fetching embeddings from BigQuery using the ml.embed text function, exporting these embeddings to a JSON file, and storing them in Cloud Storage.

1. Define the number of dimensions while calling the embedding API (e.g., 768 dimensions).
2. Create the index by calling vector search APIs, specifying storage paths and parameters.
3. Deploy your index to an endpoint.
4. Run queries against the endpoint to retrieve similar items, leveraging the technology's semantic understanding.

This sophisticated methodology allows for incredibly quick retrieval of relevant information with just a few milliseconds of processing time. Remember, this is a semantic search, so it focuses on meaning rather than mere keywords.

Conclusion

With vector search technology at your fingertips, you have the power to make intelligent searches that are fast and efficient. Whether for personal use or boosting business capability, mastering vector search opens up new possibilities for finding what you need!

Interested in a deeper dive into vector search and embeddings? Check out our full course linked in the description!

Keywords

• Vector Search
• Semantic Search
• Vertex AI
• Approximate Nearest Neighbor (ANN)
• Embeddings
• Shard Size
• Endpoint

FAQ

Q1: What is vector search?
A1: Vector search is a technology that identifies semantically similar or related items by understanding the meaning behind search queries rather than relying on exact keyword matches.

Q2: How does vector search differ from traditional search methods?
A2: Unlike traditional methods that focus solely on keywords, vector search uses semantic understanding to fetch results based on the context and meaning of the queries.

Q3: Can I use vector search without coding skills?
A3: Yes! Google’s Vertex AI offers a user-friendly interface that allows users to set up vector search projects without any coding experience.

Q4: What is the role of embeddings in vector search?
A4: Embeddings are numerical representations of data that encapsulate its features. They serve as input for vector search algorithms to find semantically similar items based on their characteristics.

Q5: Is vector search useful for businesses?
A5: Absolutely! Vector search can significantly improve internal document searches, identify expertise, and enhance customer product recommendations.