Pick Your Packer: Building Intelligent Search with Modus and Dgraph
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@Gajanan Chinchwadkar, CTO of Dgraph Labs | @Kevin Van Gundy, CEO of Hypermode.
Today we announce Dgraph Labs is becoming part of Hypermode! This ensures the continued development & support of Dgraph, the open-source graph database, as well as Badger and Ristretto.
Gajanan Chinchwadkar, CTO of Dgraph Labs, sat down with Kevin Van Gundy, CEO of Hypermode, to answer anticipated questions.
GC & KVG will also host a livestream on Wednesday, November 8 at 10am Pacific. To join, please register below or subscribe on YouTube.
Gajanan Chinchwadkar (GC): With the tailwinds for graphs in AI, trends like RAG, interpretability, and vector search, Dgraph is seeing a surge of interest. The outreach from community members has shown us there’s a unique opportunity to make the world of AI more accessible. Dgraph (the database) is a great foundation; graph structures are used widely in AI/ML. Further, adding graphs to LLMs makes things like model trust and debugging far easier than with traditional structures. I wrote about this in my recent Graph + Vector blog post.
On Hypermode and its CEO – Kevin isn’t new to the Dgraph community. We met when I was working at NASA and he was at Neo4j, nearly ten years ago. We gave a seminar to the NASA team about graph structures. When I joined Dgraph Labs, we brought him on as an advisor. He’s been helping our team since 2022. So I’m excited for this move and to work with Kevin! The vision for Hypermode and how Dgraph’s team and technology amplify that vision is compelling, so Kevin and I have worked together for the past several months to get to this announcement.
Kevin Van Gundy (KVG): I’ve been a fan of Gajanan, graphs, and specifically Dgraph for a long time.
Every developer’s theme this year is firmly “experiment with AI”, and Hypermode sees a massive opportunity for simplifying how developers build AI into their sites and applications. Major projects involving AI shouldn’t automatically add severe and insurmountable complexity, let alone early POCs and experiments.
The toolchain we’re building breaks down into three main parts: an SDK, an inference engine, and a data plane.
The SDK creates shorthand methods for requesting input from your AI models, the inference engine allows you to chain together services in the programming language(s) you’re most comfortable with. This is all enabled by a data plane comprised of a fully managed vector-graph database with model management, embedding automation, and analytics.
Our goal is to make it easy for developers to augment their applications with artificial intelligence in a few lines of code. We can solve the fundamental problems of performance, trust, and complexity.
GC: Dgraph creates a foundation for us to marry vectors, structured data, and models. For example, when thinking about adding guardrails to an LLM, you can use graphs to trace the shortest paths between similar objects found via vector search. If no connections exist between those nodes in graph space, you might want to throw that result out as a possible hallucination.
By combining our backend architecture with a frontend SDK, we can optimize how we compute and serve responses to the frontend to provide more reliable infrastructure with better performance. We’re also making the entire platform extensible through integrations and key partnerships across the AI toolchain to benefit companies with engineering investments in specific technologies. Hypermode.com is a live demo application showing off our beta.
KVG: Dgraph, Badger, and Ristretto will not only remain open source, but we will ensure these projects continue to grow. We’re going to ensure that our open source users have all the tools and features they need to be successful with Dgraph in production, regardless of where they choose to host it.
While known largely for Next.js, one thing I enjoyed while at Vercel was investing in a variety of OSS, oftentimes because it felt right regardless of the “ROI”. This is clearly a sustainable practice as shown by Meta with React and Google’s Aurora team.
Highly permissive open source is the best way for any developer-first product to be built and adopted. It’s critical to both our credibility as a developer-first company as well as the best way to share our view of what good looks like for the future of AI.
GC: I joined Dgraph Labs because I’m passionate about building great open-source data products. I’m excited to continue investing in Dgraph’s open-source projects as a part of Hypermode. Dgraph will be a core part of how Hypermode’s platform operates. Dgraph now has a well-funded commercial entity whose success is predicated on Dgraph being a great database.
GC: I will be joining Kevin as the CTO of Hypermode along with the Dgraph Labs team. We’re believers in Hypermode’s vision of easy, safe, and performant AI for the web. We will continue our focus on making Dgraph the best database for applications.
KVG: With Dgraph being a critical part of Hypermode’s infrastructure— Gajanan and I both wanted to retain as much of the team as possible. There are some truly exceptional engineers who will be joining Hypermode!
KVG - Dgraph and Dgraph Cloud are mission critical to our users. We take that responsibility very seriously. We’re going to ensure that all users have a paved pathway that ensures their workloads continue operating without downtime. As is the responsibility of all critical infrastructure providers, changes we eventually make will be made very slowly with an abundance of communication.
We hope to see even more users from our community come over to Dgraph Cloud to explore all the new functionality we’re adding.
GC - There are hundreds of users signing up for Dgraph Cloud every month, we will continue to serve those teams. We’ve been investing behind the scenes into more scalable and reliable infrastructure over the last several months.
Learn about Hypermode and request early access. Kevin and Gajanan will also host a livestream on Wednesday, November 8 at 10am Pacific. To join, please register below or subscribe on YouTube.
