The Rise of Vector Data
Embedding models convert raw data such as text, images, audio, logs, and videos into vector embeddings (“vectors”) to be used for predictions, comparisons, and other cognitive-like functions.
By Edo Liberty, Founder and CEO, Pinecone
When you see a friend, your eyes convert light into signals that travel to the visual cortex, where they activate millions of neural cells through layers, which reach the temporal lobe for interpretation: “That’s my friend Julie!”
Deep learning applications see the world similarly. Embedding models convert raw data such as text, images, audio, logs, and videos into vector embeddings (“vectors”) to be used for predictions, comparisons, and other cognitive-like functions. As embedding models grow in numbers, capability, and adoption, more vector data gets generated.
Vectors often get discarded immediately, but what if you saved them? That turns out to be valuable. So valuable that Google, Microsoft, Amazon, Facebook, and other AI trailblazers already do this.
Opportunity
A fundamental ability unlocked by storing vectors is similarity search (or “vector search”). Given some vector, find other known vectors that are similar. Using vector representations for search is akin to our brains performing pattern matching, association, and recollection when examining new information.
Similarity search improves many applications: search engines, recommendation systems, chatbots, security systems, analysis tools, and anything with user-facing or internal search functions.
However, there’s a reason why only a few large companies do similarity searches at scale today.
Challenge: Algorithms
Vectors require much more complex search methods involving the geometric relationships between stored items.
Fortunately, there are dozens of open-source libraries for doing this. Unfortunately, choosing the library, algorithm, and parameters is the first hurdle. Each algorithm comes with complex sets of trade-offs, limitations, and behaviors that aren’t obvious. Eg, the fastest algorithm might be inaccurate; a performant index could be slow to update; memory consumption can grow super linearly; and more surprises.
Challenge: Scale
Depending on the data volume and throughput, latency, accuracy, and availability requirements, you may need to build infrastructure with sharding, replication, live index updates, namespacing, filtering, persistence, and consistency. Plus monitoring, alerting, auto-recovery, auto-scaling, etc, to ensure high availability.
This takes significant effort that major tech companies can afford, but everyone else?
Solution
The rise of vector data necessitates tools to work with vector data. We hope to lead the way with our managed vector search solution, specifically designed for use in production with a few lines of code — no need to worry about algorithm tuning or distributed infrastructure. It’s the first step in helping companies harness the power of vector data.
This is an excerpt from Pinecone.io. Read the full post.