California Restaurant Finder

This web app demonstrates vector search for restaurants in California stored in Couchbase Capella. It extracts restaurant data from the Landmark collection (via a SQL++ query in ca-eateries.sql), generates embeddings using a sentence-transformer model, and performs semantic search using dot product similarity.

Overview

Data Source

• Extracts restaurant data from the preloaded travel-sample dataset.
• Uses the Landmark collection.

Embedding Generation

• Combines restaurant names and descriptions into a single text string.
• Converts text into a 384-dimensional vector using the all-MiniLM-L6-v2 model.
• Embeddings are generated using gen-embeddings.py and stored in ca-eateries-embedding.json.

Vector Search

• Performs semantic search using dot product similarity between query and restaurant embeddings.
• A large positive dot product indicates a high degree of similarity.
• A value close to zero indicates orthogonal (dissimilar) embeddings.
• To output similarity scores to the console with Couchbase's Python SDK, run vector-search.py.

Google Maps

• Reverse Geocoding: Converts latitude/longitude into a human-readable address.
• Map Embed: Dynamically embeds maps within the search results to visually locate restaurants.

Architecture

Data Preparation

1. Run ca-eateries.sql against the Landmark collection in Couchbase Capella to generate ca-eateries.json.
2. Import ca-eateries.json into the ca-eateries collection.
3. Combine the restaurant's name and description for generating embeddings.

Embedding Generation

• Use gen-embeddings.py along with the all-MiniLM-L6-v2 model.
• Output is stored in ca-eateries-embedding.json.

Search Indexing

• Create a fulltext search index in Couchbase Capella.
• Can import index in Capella restaurants.california.ca-eateries-index.json.

Web App

• Provides a client-side search interface.
• Displays restaurant details and embedded maps.
• Dynamically fetches and displays the total number of restaurants from an API endpoint.
• Links to restaurant websites to easily book a restaurant you discover.

Setup

Environment Variables

Create a .env file and set values in .env.example

Will need to setup an Open AI and Google Maps API key separately.

Running the App

In restaurant-web-app directory:

python3 -m app.main

Index and Collection Setup

• Import the JSON data files into the designated Couchbase collections.
• Follow Couchbase documentation to create the required search indexes.

Usage

Search Interface

• Open the web app in your browser.
• Use the search input to describe your dream restaurant.

Result Display

• Shows details such as name, description, address, phone, price, and website.
• If available, shows an embedded map of the restaurant's location.
• Returns the three most relevant results using dot product similarity.