Imagine a world where search engines understand not just the words you type, but the context and meaning behind them. This is where vector search comes into play, revolutionizing the way we find information and improving the search experience for users.
In this article, we'll delve into the concept of vector search, explore its benefits, and see how it can dramatically enhance your search results. Join me on this journey as we discover the potential of vector search and its impact on the search field.
We are curious about the future and eager to find information quickly and accurately. In the past we have relied on traditional search engines, whose results are based on keyword matching. Yet, we often find ourselves sifting through countless irrelevant links and struggling to find what we really need. We started looking for a better solution, and that's when the era of vector search came. It brings a whole new experience to our search!
Understanding Vector Search
Vector search represents a leap forward in search technology. It uses the power of machine learning and artificial intelligence to understand the semantic relationship between words and documents. Rather than relying solely on keyword matching, vector search creates a mathematical representation of documents and queries, making it possible to understand the context, relevance, and similarity of different information.
To grasp the concept of vector search, let's imagine vectors as arrows pointing in different directions in a multidimensional space. Each vector represents a document or query, and the direction and magnitude of the vector represent the context and importance of words in it. By computing the cosine similarity between vectors, a vector search engine can identify the most relevant documents based on the angle between the query vector and the document vector.
So what is the difference between traditional keyword-based search and vector similarity search? For many years, relational databases and full-text search engines have been the foundation of information retrieval in modern IT systems. For example, you can add tags or category keywords such as "movie", "music" or "actor" to each piece of content (image or text) or each entity (product, user, IoT device, or any other entity). You can then add these records to your database so that you can perform searches using these tags or keywords.
In contrast, vector search uses vectors (where each vector is a list of numbers) to represent and search for content. Combinations of numbers define similarity to a particular theme. For example, if an image (or any content) contains 10% "movie", 2% "music" and 30% "actor" related content, then you can define a vector [0.1, 0.02, 0.3] to represent it. (Note: this is an oversimplified explanation of the concept; real vectors have more complex vector spaces). You can find similar content by comparing the distance and similarity between vectors. This is how Google services find valuable content for all kinds of users around the world in milliseconds.
With keyword search, you can only specify binary choices as attributes for each piece of content; it's either movie-related, movie-free, music-free, etc. Also, you can't express the actual "meaning" of what you're searching for. For example, if you specify the keyword "films," you won't see anything related to "movie" unless there is a thesaurus in your database or search engine that explicitly links the two terms.
Vector search offers a more refined way of finding content, with subtle nuances and meaning. A vector can represent a subset of content that contains "most content about actor, some content about movie, and some content about music". A vector can represent the meaning of the contents of "movie", "films", and "cinema" grouped together. In addition, vectors provide flexibility to represent previously unknown or undefined categories of service providers. For example, emerging content categories that primarily appeal to children are indeed difficult for adults or marketing professionals to predict in advance, and manually updating content with these new labels through a huge database is almost impossible, but the vector Never-before-seen categories can be captured and represented instantly.
Vector Search Using Python
Now, let's dive into the practical aspects of vector search by exploring a Python code snippet that demonstrates its implementation.
# Importing necessary libraries
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
# Creating document vectors
document1 = np.array([0.2, 0.3, 0.8])
document2 = np.array([0.5, 0.1, 0.9])
document3 = np.array([0.7, 0.5, 0.2])
# Creating a query vector
query = np.array([0.3, 0.6, 0.2])
# Calculating cosine similarity
similarity1 = cosine_similarity([document1], [query])[0][0]
similarity2 = cosine_similarity([document2], [query])[0][0]
similarity3 = cosine_similarity([document3], [query])[0][0]
# Printing the results
print("Similarity between Document 1 and Query:", similarity1)
print("Similarity between Document 2 and Query:", similarity2)
print("Similarity between Document 3 and Query:", similarity3)In this code snippet, we use NumPy arrays to create document vectors and query vectors. Then, by using the cosine_similarity function from the scikit-learn library, we compute the cosine similarity between each document vector and the query vector. The higher the similarity score, the more relevant the document is to the query.
Advantages of Vector Search
Vector search has several key advantages that help improve the effectiveness of search results:
- Enhanced Relevance : By considering the semantic relationship between words and documents, vector search provides more accurate and relevant search results, significantly reducing irrelevant matches.
- Flexibility and adaptability : Unlike traditional keyword-based searches, vector searches can be adapted to different languages, domains, and can even handle misspelled words or synonyms efficiently.
- Personalization : Vector Search can understand user preferences and customize search results accordingly, providing a personalized search experience.
Vector Search Changing Business
Vector search isn't just for images and text content. It can also be used for information retrieval on anything in your business when you can define a vector to represent each thing. Here are some examples:
- Find similar users : If you define a vector to represent every user in your business by combining the user's activity, past purchase history, and other user attributes, then you can find all users that are similar to a given user. For example, you can see users who are buying similar products, users who may be bots, or users who are potential quality customers and should be targeted by digital marketing.
- Find similar products or items : With a vector generated from product characteristics such as description, price, where it was sold, you can find similar products to answer any number of questions; e.g., "What other products do we have that are similar to this and might apply for the same use case?" or "Which products were sold in this region in the past 24 hours?" (based on time and distance)
- Find defective IoT devices : Capturing the characteristics of defective devices from signals via vectors, vector search enables you to instantly find potentially defective devices for proactive maintenance.
- Find Ads : Well-defined vectors allow you to find the most relevant or appropriate ad for the viewer with high throughput in milliseconds.
- Find Security Threats : You can identify security threats by vectoring signatures of computer virus binaries or malicious attacks against web services or network devices.
- ...and more : There are likely to be thousands of different vector search applications across all industries in the next few years, making the technology as important as relational databases.
Well, vector searches sound pretty cool. But what is the biggest challenge in applying this technology to real business use cases? Actually there are two:
- Create vectors that make sense for your business use case
- Build a fast and scalable vector search service
Now that we understand the potential of vector search, it's time to take this technology and use it to improve search results. Whether you're a developer, content creator, or user looking for accurate information, adopting vector search can have a transformative impact on your search experience.
in conclusion
Vector Search has been a game changer in the search engine world. By going beyond keyword matching and harnessing the power of machine learning, vector search provides a smarter, more efficient way to find information. Its ability to understand context, relevance, and similarity has the potential to revolutionize the search experience for users worldwide. So let's embrace the power of vector search and usher in a new era of enhanced search results.
Remember, the journey doesn't end there.
By exploring and implementing vector search, we have embarked on a path of continuous improvement that has allowed us to advance our search capabilities and reach greater heights in the field of information retrieval. Let's embrace this technology to revolutionize our search experience and shape a future where finding information is a seamless and enriching process.
What are we waiting for? If you want to use the fun of vector search in your search business as soon as possible, you can use Elasticsearch for vector search. For details, see the " NLP - Natural Language Processing and Vector Search " chapter in " Elastic: A Developer's Guide " .