This is a Multimodal Search demo built with Weaviate, Vertex and Nuxt.js

🐥 Getting Started

First, clone the project with the command below

git clone https://github.com/weaviate-tutorials/nuxt-multimodal-search

The repository lets us do three things

1. Run the Nuxt.js Web App.
2. Import images, and text into your Weaviate database.
3. Search like it's 2034!

🏗️ Create a Weaviate Instance

Create a Weaviate instance on Weaviate Cloud Services as described in this guide

🦿 Create a .env file and add the following keys

• your Google Vertex API key as NUXT_GOOGLE_API_KEY (you can get this in your Vertex AI settings)
• your Weaviate API key as NUXT_WEAVIATE_ADMIN_KEY (you can get this in your Weaviate dashboard under sandbox details)
• your Weaviate host URL as NUXT_WEAVIATE_HOST_URL (you can get this in your Weaviate dashboard under sandbox details)

📩 Importing Data

Before you can import data, add any files to their respective media type in the public/ folder. You will have to download a dataset of random images and place them in /public/image. Feel free to use your images too.

With your data in the right folder, run yarn install to install all project dependencies and to import your data into Weaviate and initialize a collection, run:

yarn run import

this may take a minute or two.

🚀 Running your Nuxt.js Application.

Make sure you have your Weaviate instance running with data imported before starting your Nuxt.js Web App.

To run the Web App

yarn dev

... and you can search away!!

📚 Resources

Learn more about multimodal applications

• Multimodal Retrieval Augmented Generation(RAG)
• Multimodal Embedding Models

🤷🏾‍♂️ Troubleshooting

• Check out the Weaviate Docs
• Open an Issue