Pinecone Builds Vector Search Platform

Search

Table of Contents

Is vector search the future of online searching? Pinecone’s founders and developers certainly think so. They have built their vector database with the objective of enabling a new generation of high-performance vector search applications, which are based on artificial intelligence (AI) in the cloud.

How does the Pinecone vector database work?

“We launched Pinecone to make it easy for developers to build high-performance vector search applications — at any scale and without infrastructure hassles”, explains Edo Liberty, Pinecone’s Founder, and CEO. “That meant creating a completely new kind of infrastructure and indexing algorithm, standing it up as a managed service, and exposing it through a simple API. We needed to call it something, so we came up with “vector database”, he adds.

Pinecone’s three premises are building a fast, fresh, and filtered vector search. It can query enormous databases with low latency, update your searches when you add/delete data, and combine metadata filters with vector search for better and faster results. This way, Pinecone helps business applications produce recommendations, bot detection, image searching, threat detection, question answering, and other tasks. All of this uses the power of Machine Learning through vector embeddings.

What are Vector Embeddings?

“Traditional search” algorithms check a list of rows until finding one that fits the criteria into the database. This method has serious limitations, considering that it can leave out similar or related data, as well as results for the same query in different contexts. 

On the other hand, Machine Learning algorithms analyze numbers. To analyze large sections of text, for example, vector embeddings are created so data can be read as numeric data. Text, audio, images, or even data that is already numerical can be turned into vectors for easier operations and use.

In this matter, the concept of “vector similarity” is key. It compares “the likeness of objects, as captured by machine learning models. Vector similarity search is particularly useful with real-world data because that data is often unstructured and contains similar yet not identical items. It doesn’t require an exact match because the so-called closest value is often good enough. Companies use it for things like semantic search, image search, and recommender systems”, resumes this Venture Beat’s post.

By doing this type of quantification of audio recordings, news articles, images, or maybe social profiles and user’s behavioral patterns, some data can be “similar” to other sets of data, making it easier for algorithms to build recommendations or classifications.

Precisely, what Pinecone does is train models to do the translation of objects into vectors. Vectors usually have lots of dimensions, up to two thousand. Deep neural networks or convolutional neural networks are commonly used to train these types of Machine Learning models. Vector embeddings can be used internally by machine learning models and methods, even though the final product does not use them directly. 

Why choose Pinecone?

Giant tech companies are already using vector search. However, other companies, including large enterprises, can really struggle to implement this technology. When you combine business logic, filters, and vector search algorithms, overall performance can be reduced if not done correctly. Achieving accurate search results is hard enough, but if doing so produces a laggy experience, user experience will be negatively affected.

This is why Pinecone has developed low-latency filtering capabilities that go along with more accurate searches and recommendations. It allows businesses to store metadata together with their items and to filter vector searches by this metadata. This helps to produce more accurate results, at really fast speeds. 

Pinecone has the ability to use hybrid storage. This approach reduces infrastructure costs because vector searches typically run completely in memory, which is more expensive than using a hybrid of disk and RAM. Speed and accuracy are not impacted, helping with cost reduction for customers.

It’s already production-ready, is scalable, and has high availability and minimal latency impact when querying billions of items. Being a cloud-based product, it does not require infrastructure maintenance or service monitoring and is secure (certified) and GDPR compliant. Companies just need to connect via API and can start using the managed vector database.

Since its launch in 2021, the company has introduced multiple updates and new features, such as REST API, SOAC2, and a new architecture, designed to use Kafka and Kubernetes, in order to provide fault tolerance, data persistence, more security, and high availability for customers.

The Future of Vector Search According to Pinecone

Pinecone aims to keep up with a fast-paced growing market and ever-changing landscape of technologies. According to the company, the mission is to continue building search and database technology for the AI age. This means, making it easy for developers, and their teams, to use vector search applications, whether they have already worked with Machine Learning or are just starting to. 

Additionally, Pinecone wants to keep stretching the boundaries of vector search to help provide more accurate results without compromising speed or performance when there are billions or trillions of items. Consequently, Pinecone is investing more in its vector search platform, customer success, and its engineering and developer teams. Furthermore, they continue investing in core research into machine learning, natural language processing, and information retrieval for these fields to continue thriving.

Subscribe
Notify of
guest

0 Comments
Inline Feedbacks
View all comments
Scroll to Top