Unlocking AI's Potential with Pinecone's Vector Database

Table of Contents

1. Introduction
2. What are Vector Embeddings?
3. How do Vector Embeddings Help in AI?
4. What is a Vector Database?
5. Use Cases of Vector Databases
6. Pinecone: The Vector Database Company
7. Pinecone's Go-to-Market Strategy
8. Competition in the Vector Database Space
9. Quality Monitoring on Embeddings
10. The Future of Prompt Engineering

Pinecone: The Vector Database Company

Pinecone is a company that has developed a vector database, which makes it easy to connect company data with Generative AI models. The company was started in 2019 and has raised $138 million in VC money, including a $100 million series B that was just announced. Pinecone's founder and CEO, Edo Liberty, has a background in theoretical machine learning and big data algorithms. He spent time at Yahoo as a scientist and became an adjunct professor at Tel Aviv University. In 2016, he joined AWS and helped build SageMaker and other AI services.

What are Vector Embeddings?

When You look at deep learning models, they are mathematical objects that are number crunching machines. The information that propagates between layer to layer or within the network is always a set of numbers. The same thing is happening in your brain, where one process communicates with the other as a set of activations of neurons. That set of numbers is called a vector or a vector embedding. The embedding is a mathematical term that refers to placing one object in another space, such as taking an image, which is in pixel space, and putting it in another space, which is the high-dimensional number array space in a way that somehow keeps some of the representation of it.

How do Vector Embeddings Help in AI?

Vectors help you understand whether there's similarity or not between concepts. In the same way that you would see somebody that you know, your visual cortex would process the face, and then with your temporal lobes, you would access the kind of your face data stored in your brain and figure out, "Oh, that's my cousin." That's happening in the vector embedding space, and that's happening with the similarity search that's happening with a mechanism that looks a lot more like memory than it looks like processing like a neural net.

What is a Vector Database?

A vector database is the infrastructure that supports that long-term memory. When you have millions or billions of documents or images, you really have to have a very specialized system to be able to do this cost-effectively, with low latency, high persistence, and so on. You really have to start thinking about things in a fundamental way and not just about the data. Pinecone's vector database is a distributed system that is designed to store and retrieve vector embeddings efficiently.

Use Cases of Vector Databases

Pinecone's vector database is used for semantic search, where people are literally just reinventing their own search stack. Pinecone is also used to Create Context for long-form chat, such as customer service. Pinecone is used for anomaly detection, face detection for cows, and a wide variety of other use cases.

Pinecone's Go-to-Market Strategy

Pinecone is a managed service that is focused on the self-serve Journey. Customers can start with Pinecone now and be in production in a week without ever talking to Pinecone. Pinecone is building a sales team that is happy and willing to engage and help, but it's more about assisting in the journey if that's wanted or standing not being in the way when Pinecone is not wanted.

Competition in the Vector Database Space

Pinecone's founder and CEO, Edo Liberty, believes that Pinecone is far and away better in terms of efficiency of running this service at Scale and its stability. Pinecone is just getting started, and there's so much to come. The hyperscalers are looking at vector databases, and they will have something, but it will take time to catch up to where Pinecone is today.

Quality Monitoring on Embeddings

There are many flavors of recommender systems, and it really depends on what you're doing. Pinecone recommends looking at Snorkel, a company that focuses on quality monitoring on embeddings.

The Future of Prompt Engineering

Prompt engineering is a nascent technology, and it's a basic limitation of the technology that people have to work around its sharp edges. Pinecone's founder and CEO, Edo Liberty, believes that prompt engineering may not stick around and that it's a deficiency of the technology because it's very nascent.