We’re excited that members of our community are already building applications with the WebAssembly (Wasm) version of Kuzu,
which was only released a few weeks ago!
Early adopters to integrate Kuzu-Wasm include Alibaba Graphscope, see: 1
and 2, and Kineviz, whose project will be launched soon.

In this post, we’ll showcase the potential of Kuzu-Wasm by building a fully in-browser chatbot
that answers questions over LinkedIn data using an advanced retrieval technique: Graph
Retrieval-Augmented Generation (Graph RAG). This is achieved using Kuzu-Wasm
alongside WebLLM, a popular in-browser LLM inference engine that can
run LLMs inside the browser.

A quick introduction to WebAssembly

WebAssembly (Wasm) has transformed browsers into general-purpose computing platforms.
Many fundamental software components, such as full-fledged databases, machine learning
libraries, data visualization tools, and encryption/decryption libraries, now have Wasm versions.
This enables developers to build advanced applications that run entirely in users’
browsers—without requiring backend servers. There are several benefits for building fully
in-browser applications:

• Privacy: Users’ data never leaves their devices, ensuring complete privacy and confidentiality.
• Ease of Deployment: An in-browser application that uses Wasm-based components
  can run in any browser in a completely serverless manner.
• Speed: Eliminating frontend-server communication can lead to a significantly faster and
  more interactive user experience.

With this in mind, let’s now demonstrate how to develop a relatively complex AI application completely in
the browser! We’ll build a fully in-browser chatbot that uses graph retrieval- augmented
generation (Graph RAG) to answer natural language questions. We demonstrate this using
Kuzu-Wasm and WebLLM.

Architecture

The high-level architecture of the application looks as follows:

The term “Graph RAG” is used to refer to several techniques but in its simplest form the term
refers to a 3-step retrieval approach. The goal is to retrieve useful context from a graph DBMS (GDBMS)
to help an LLM answer natural language questions.
In our application, the additional data is information about
a user’s LinkedIn data consisting of their contacts, messages, companies the user or their contacts worked for. Yes, you can download
your own LinkedIn data (and you should, if
for nothing else, to see how much of your data they have!).
The schema of the graph database we use to model this data will be shown below momentarily. First, let’s go over the 3 steps of
Graph RAG:

1. Q$_{NL}$NL​ $rightarrow$→ Q$_{Cypher}$Cypher​: A user asks a natural language question Q$_{NL}$NL​, such as “Which of my contacts work at Google?“.
   Then, using an LLM, this question is converted to a Cypher query, e.g., MATCH (a:Company)<-[:WorksAt]-(b:Contact) WHERE a.name = "Google" RETURN b,