Semantic search of wines

In this repository, you'll find the practical essence of the article Hackernoon article: "Semantic Search Queries Return More Informed Results" distilled into code (albeit updated). The author points out a common hurdle: the struggle with searching through our own unstructured data. Weaviate, an open-source vector search engine, is introduced as a sturdy bridge over this hurdle. Following the narrative, this codebase sets up Weaviate, harnesses the open transformer model sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 for vectorization through the vectorization module, and dives into a dataset of wine reviews. This repository demonstrates how to set up Weaviate with your data and get straight to firing up search queries.

(TODO: Add demo video)

Prerequisites

Before you can run the project, you need to have Docker, Docker Compose, and Python installed on your machine. Follow the instructions below to install the prerequisites:

1. Install Docker:

• For Windows and Mac:
  • Download and install Docker Desktop from Docker's official website.
• For Linux:
  • Run the following commands in your terminal:

    sudo apt-get update
    sudo apt-get install docker-ce docker-ce-cli containerd.io

2. Install Docker Compose:

• For Windows and Mac:
  • Docker Compose is included with Docker Desktop.
• For Linux:
  • Run the following command in your terminal:

    sudo apt install docker-compose

3. Install Python:

• Download and install the latest version of Python from Python's official website.
• Verify the installation by running the following command in your terminal:

  python --version

Setup instructions

1. Install virtualenv (if not already installed):

   pip install virtualenv

2. Create a Virtual Environment: Navigate to the directory where you want to create your virtual environment, then run:

   virtualenv <name_of_virtualenv>

3. Activate the Virtual Environment: On Windows, run:

   .\<name_of_virtualenv>\Scripts\activate

   On macOS and Linux, run:

   source <name_of_virtualenv>/bin/activate

4. Install Python requirements:

   pip install -r requirements.txt

Usage instructions

1. Start up Weaviate: docker-compose up -d. Once completed, Weaviate is running on http://localhost:8080.
2. Run python import.py to import 2500 wines to Weaviate.
3. The data is now stored in the Weaviate instance. You can experiment with it using a python notebook or a python file.

Dataset license

This folder contains Wine review data, retrieved from Kaggle (from WineEnthusiast).

Notes:

This project's origin is here and the Hackernoon article: "Semantic Search Queries Return More Informed Results".