Insights from my Software Engineering Radio interview with Kacper Łukawski
Introduction
In a recent episode of the Software Engineering Radio podcast, I interviewed Kacper Łukawski, a Senior Developer Advocate at Qdrant. Our conversation focused on Qdrant, an open-source vector database and similarity search engine implemented in Rust. During the interview, Kacper shares insights into vector embeddings and similarity search, the performance benefits of implementing a search engine in Rust, the principles behind benchmarking vector databases, and how developers can get started building AI-powered applications with Qdrant. I learned a lot from this interview and I hope you do as well!
Insights
I’m thankful for this action-packed and informative discussion with Kacper Łukawski! Before you listen to the full episode, please checkout some of its insights:
What is a vector database and how does similarity search work?
“It’s a service that can efficiently store and handle high dimensional vectors for the proposals of similarity search and similarity of these vectors is defined by the closeness of the vectors in that space. So vector databases are built to make that process efficient.”
What are vector embeddings and why are they important for semantic search?
“Vector embeddings are just numerical representations of the input data and the main idea is that they keep the semantic meaning of the input data that was used to generate them. And if we have two different vectors which are similar in some way, then we assume that the objects that are used to generate them are also similar in their nature.”
Why did the Qdrant team choose Rust as the implementation language?
“Definitely the biggest factor behind choosing Rust is its safety. And we can achieve almost similar performance like C or C++ sometimes even better. While keeping this language safety and this strong type system that Rust provides is very helpful in preventing us from making some mistakes in a highly concurrent system. So reading or writing some value from multiple threats concurrently because that’s ultimately what you can expect from a search engine.”
What metrics does Qdrant use when benchmarking performance?
“Depending on a specific use case you have or maybe some budget constraints, you might prefer to optimize for a particular metric from those four. But we measure all of them and report them in our benchmarks just so you can have understanding of what you can expect in a very specific setup. For example, low latency might be important if your users expect immediate response, and we measure an average latency P95 and P99 so we can see like what the majority of users can expect from the system and how fast it is going to be.”
What is the future of vector databases in the AI landscape?
“The main problem of AI or LLMs … even the latest LLM can suffer from knowledge cutoff because they were trained on some specific data sets and definitely don’t know that most recent news and none of them could have been trained on your own data. So definitely some sort of retrieval is needed and vector databases will definitely serve that functionality.”
Listen
If you’re interested in learning more about Qdrant and vector similarity search, I highly recommend listening to my interview with Kacper Łukawski on Software Engineering Radio! You can find it on your favorite podcast player, Apple Podcasts, Spotify, YouTube, or you can listen to it with this handy podcast player.